Roy Fraley | NREL

Science.gov Websites

Roy Fraley Roy Fraley Professional II-Engineer Roy.Fraley@nrel.gov | 303-384-6468 Roy Fraley is the high-performance computing (HPC) data center engineer with the Computational Science Center's HPC

RedThreads: An Interface for Application-Level Fault Detection/Correction Through Adaptive Redundant Multithreading

DOE PAGES

Hukerikar, Saurabh; Teranishi, Keita; Diniz, Pedro C.; ...

2017-02-11

In the presence of accelerated fault rates, which are projected to be the norm on future exascale systems, it will become increasingly difficult for high-performance computing (HPC) applications to accomplish useful computation. Due to the fault-oblivious nature of current HPC programming paradigms and execution environments, HPC applications are insufficiently equipped to deal with errors. We believe that HPC applications should be enabled with capabilities to actively search for and correct errors in their computations. The redundant multithreading (RMT) approach offers lightweight replicated execution streams of program instructions within the context of a single application process. Furthermore, the use of completemore » redundancy incurs significant overhead to the application performance.« less

RedThreads: An Interface for Application-Level Fault Detection/Correction Through Adaptive Redundant Multithreading

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hukerikar, Saurabh; Teranishi, Keita; Diniz, Pedro C.

In the presence of accelerated fault rates, which are projected to be the norm on future exascale systems, it will become increasingly difficult for high-performance computing (HPC) applications to accomplish useful computation. Due to the fault-oblivious nature of current HPC programming paradigms and execution environments, HPC applications are insufficiently equipped to deal with errors. We believe that HPC applications should be enabled with capabilities to actively search for and correct errors in their computations. The redundant multithreading (RMT) approach offers lightweight replicated execution streams of program instructions within the context of a single application process. Furthermore, the use of completemore » redundancy incurs significant overhead to the application performance.« less

RAPPORT: running scientific high-performance computing applications on the cloud.

PubMed

Cohen, Jeremy; Filippis, Ioannis; Woodbridge, Mark; Bauer, Daniela; Hong, Neil Chue; Jackson, Mike; Butcher, Sarah; Colling, David; Darlington, John; Fuchs, Brian; Harvey, Matt

2013-01-28

Cloud computing infrastructure is now widely used in many domains, but one area where there has been more limited adoption is research computing, in particular for running scientific high-performance computing (HPC) software. The Robust Application Porting for HPC in the Cloud (RAPPORT) project took advantage of existing links between computing researchers and application scientists in the fields of bioinformatics, high-energy physics (HEP) and digital humanities, to investigate running a set of scientific HPC applications from these domains on cloud infrastructure. In this paper, we focus on the bioinformatics and HEP domains, describing the applications and target cloud platforms. We conclude that, while there are many factors that need consideration, there is no fundamental impediment to the use of cloud infrastructure for running many types of HPC applications and, in some cases, there is potential for researchers to benefit significantly from the flexibility offered by cloud platforms.

Mixing HTC and HPC Workloads with HTCondor and Slurm

NASA Astrophysics Data System (ADS)

Hollowell, C.; Barnett, J.; Caramarcu, C.; Strecker-Kellogg, W.; Wong, A.; Zaytsev, A.

2017-10-01

Traditionally, the RHIC/ATLAS Computing Facility (RACF) at Brookhaven National Laboratory (BNL) has only maintained High Throughput Computing (HTC) resources for our HEP/NP user community. We’ve been using HTCondor as our batch system for many years, as this software is particularly well suited for managing HTC processor farm resources. Recently, the RACF has also begun to design/administrate some High Performance Computing (HPC) systems for a multidisciplinary user community at BNL. In this paper, we’ll discuss our experiences using HTCondor and Slurm in an HPC context, and our facility’s attempts to allow our HTC and HPC processing farms/clusters to make opportunistic use of each other’s computing resources.

Faster than Real-Time Dynamic Simulation for Large-Size Power System with Detailed Dynamic Models using High-Performance Computing Platform

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, Renke; Jin, Shuangshuang; Chen, Yousu

This paper presents a faster-than-real-time dynamic simulation software package that is designed for large-size power system dynamic simulation. It was developed on the GridPACKTM high-performance computing (HPC) framework. The key features of the developed software package include (1) faster-than-real-time dynamic simulation for a WECC system (17,000 buses) with different types of detailed generator, controller, and relay dynamic models, (2) a decoupled parallel dynamic simulation algorithm with optimized computation architecture to better leverage HPC resources and technologies, (3) options for HPC-based linear and iterative solvers, (4) hidden HPC details, such as data communication and distribution, to enable development centered on mathematicalmore » models and algorithms rather than on computational details for power system researchers, and (5) easy integration of new dynamic models and related algorithms into the software package.« less

On the Impact of Execution Models: A Case Study in Computational Chemistry

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chavarría-Miranda, Daniel; Halappanavar, Mahantesh; Krishnamoorthy, Sriram

2015-05-25

Efficient utilization of high-performance computing (HPC) platforms is an important and complex problem. Execution models, abstract descriptions of the dynamic runtime behavior of the execution stack, have significant impact on the utilization of HPC systems. Using a computational chemistry kernel as a case study and a wide variety of execution models combined with load balancing techniques, we explore the impact of execution models on the utilization of an HPC system. We demonstrate a 50 percent improvement in performance by using work stealing relative to a more traditional static scheduling approach. We also use a novel semi-matching technique for load balancingmore » that has comparable performance to a traditional hypergraph-based partitioning implementation, which is computationally expensive. Using this study, we found that execution model design choices and assumptions can limit critical optimizations such as global, dynamic load balancing and finding the correct balance between available work units and different system and runtime overheads. With the emergence of multi- and many-core architectures and the consequent growth in the complexity of HPC platforms, we believe that these lessons will be beneficial to researchers tuning diverse applications on modern HPC platforms, especially on emerging dynamic platforms with energy-induced performance variability.« less

An Application-Based Performance Evaluation of NASAs Nebula Cloud Computing Platform

NASA Technical Reports Server (NTRS)

Saini, Subhash; Heistand, Steve; Jin, Haoqiang; Chang, Johnny; Hood, Robert T.; Mehrotra, Piyush; Biswas, Rupak

2012-01-01

The high performance computing (HPC) community has shown tremendous interest in exploring cloud computing as it promises high potential. In this paper, we examine the feasibility, performance, and scalability of production quality scientific and engineering applications of interest to NASA on NASA's cloud computing platform, called Nebula, hosted at Ames Research Center. This work represents the comprehensive evaluation of Nebula using NUTTCP, HPCC, NPB, I/O, and MPI function benchmarks as well as four applications representative of the NASA HPC workload. Specifically, we compare Nebula performance on some of these benchmarks and applications to that of NASA s Pleiades supercomputer, a traditional HPC system. We also investigate the impact of virtIO and jumbo frames on interconnect performance. Overall results indicate that on Nebula (i) virtIO and jumbo frames improve network bandwidth by a factor of 5x, (ii) there is a significant virtualization layer overhead of about 10% to 25%, (iii) write performance is lower by a factor of 25x, (iv) latency for short MPI messages is very high, and (v) overall performance is 15% to 48% lower than that on Pleiades for NASA HPC applications. We also comment on the usability of the cloud platform.

SCEAPI: A unified Restful Web API for High-Performance Computing

NASA Astrophysics Data System (ADS)

Rongqiang, Cao; Haili, Xiao; Shasha, Lu; Yining, Zhao; Xiaoning, Wang; Xuebin, Chi

2017-10-01

The development of scientific computing is increasingly moving to collaborative web and mobile applications. All these applications need high-quality programming interface for accessing heterogeneous computing resources consisting of clusters, grid computing or cloud computing. In this paper, we introduce our high-performance computing environment that integrates computing resources from 16 HPC centers across China. Then we present a bundle of web services called SCEAPI and describe how it can be used to access HPC resources with HTTP or HTTPs protocols. We discuss SCEAPI from several aspects including architecture, implementation and security, and address specific challenges in designing compatible interfaces and protecting sensitive data. We describe the functions of SCEAPI including authentication, file transfer and job management for creating, submitting and monitoring, and how to use SCEAPI in an easy-to-use way. Finally, we discuss how to exploit more HPC resources quickly for the ATLAS experiment by implementing the custom ARC compute element based on SCEAPI, and our work shows that SCEAPI is an easy-to-use and effective solution to extend opportunistic HPC resources.

Development of a SaaS application probe to the physical properties of the Earth's interior: An attempt at moving HPC to the cloud

NASA Astrophysics Data System (ADS)

Huang, Qian

2014-09-01

Scientific computing often requires the availability of a massive number of computers for performing large-scale simulations, and computing in mineral physics is no exception. In order to investigate physical properties of minerals at extreme conditions in computational mineral physics, parallel computing technology is used to speed up the performance by utilizing multiple computer resources to process a computational task simultaneously thereby greatly reducing computation time. Traditionally, parallel computing has been addressed by using High Performance Computing (HPC) solutions and installed facilities such as clusters and super computers. Today, it has been seen that there is a tremendous growth in cloud computing. Infrastructure as a Service (IaaS), the on-demand and pay-as-you-go model, creates a flexible and cost-effective mean to access computing resources. In this paper, a feasibility report of HPC on a cloud infrastructure is presented. It is found that current cloud services in IaaS layer still need to improve performance to be useful to research projects. On the other hand, Software as a Service (SaaS), another type of cloud computing, is introduced into an HPC system for computing in mineral physics, and an application of which is developed. In this paper, an overall description of this SaaS application is presented. This contribution can promote cloud application development in computational mineral physics, and cross-disciplinary studies.

User Account Passwords | High-Performance Computing | NREL

Science.gov Websites

Account Passwords User Account Passwords For NREL's high-performance computing (HPC) systems, learn about user account password requirements and how to set up, log in, and change passwords. Password Logging In the First Time After you request an HPC user account, you'll receive a temporary password. Set

Data Security Policy | High-Performance Computing | NREL

Science.gov Websites

to use its high-performance computing (HPC) systems. NREL HPC systems are operated as research systems and may only contain data related to scientific research. These systems are categorized as low per sensitive or non-sensitive. One example of sensitive data would be personally identifiable information (PII

Shared Storage Usage Policy | High-Performance Computing | NREL

Science.gov Websites

Shared Storage Usage Policy Shared Storage Usage Policy To use NREL's high-performance computing (HPC) systems, you must abide by the Shared Storage Usage Policy. /projects NREL HPC allocations include storage space in the /projects filesystem. However, /projects is a shared resource and project

Business Models of High Performance Computing Centres in Higher Education in Europe

ERIC Educational Resources Information Center

Eurich, Markus; Calleja, Paul; Boutellier, Roman

2013-01-01

High performance computing (HPC) service centres are a vital part of the academic infrastructure of higher education organisations. However, despite their importance for research and the necessary high capital expenditures, business research on HPC service centres is mostly missing. From a business perspective, it is important to find an answer to…

High Performance Computing (HPC)-Enabled Computational Study on the Feasibility of using Shape Memory Alloys for Gas Turbine Blade Actuation

DTIC Science & Technology

2016-11-01

Feasibility of using Shape Memory Alloys for Gas Turbine Blade Actuation by Kathryn Esham, Luis Bravo, Anindya Ghoshal, Muthuvel Murugan, and Michael...Computational Study on the Feasibility of using Shape Memory Alloys for Gas Turbine Blade Actuation by Luis Bravo, Anindya Ghoshal, Muthuvel...High Performance Computing (HPC)-Enabled Computational Study on the Feasibility of using Shape Memory Alloys for Gas Turbine Blade Actuation 5a

What Physicists Should Know About High Performance Computing - Circa 2002

NASA Astrophysics Data System (ADS)

Frederick, Donald

2002-08-01

High Performance Computing (HPC) is a dynamic, cross-disciplinary field that traditionally has involved applied mathematicians, computer scientists, and others primarily from the various disciplines that have been major users of HPC resources - physics, chemistry, engineering, with increasing use by those in the life sciences. There is a technological dynamic that is powered by economic as well as by technical innovations and developments. This talk will discuss practical ideas to be considered when developing numerical applications for research purposes. Even with the rapid pace of development in the field, the author believes that these concepts will not become obsolete for a while, and will be of use to scientists who either are considering, or who have already started down the HPC path. These principles will be applied in particular to current parallel HPC systems, but there will also be references of value to desktop users. The talk will cover such topics as: computing hardware basics, single-cpu optimization, compilers, timing, numerical libraries, debugging and profiling tools and the emergence of Computational Grids.

High Performance Computing Innovation Service Portal Study (HPC-ISP)

DTIC Science & Technology

2009-04-01

threatened by global competition. It is essential that these suppliers remain competitive and maintain their technological advantage . In this increasingly...place themselves, as well as customers who rely on them, in competitive jeopardy. Despite the potential competitive advantage associated with adopting...computing users into the HPC fold and to enable more entry-level users to exploit HPC more fully for competitive advantage . About half of the surveyed

Performance measurement and modeling of component applications in a high performance computing environment : a case study.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Armstrong, Robert C.; Ray, Jaideep; Malony, A.

2003-11-01

We present a case study of performance measurement and modeling of a CCA (Common Component Architecture) component-based application in a high performance computing environment. We explore issues peculiar to component-based HPC applications and propose a performance measurement infrastructure for HPC based loosely on recent work done for Grid environments. A prototypical implementation of the infrastructure is used to collect data for a three components in a scientific application and construct performance models for two of them. Both computational and message-passing performance are addressed.

Comparative Implementation of High Performance Computing for Power System Dynamic Simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jin, Shuangshuang; Huang, Zhenyu; Diao, Ruisheng

Dynamic simulation for transient stability assessment is one of the most important, but intensive, computations for power system planning and operation. Present commercial software is mainly designed for sequential computation to run a single simulation, which is very time consuming with a single processer. The application of High Performance Computing (HPC) to dynamic simulations is very promising in accelerating the computing process by parallelizing its kernel algorithms while maintaining the same level of computation accuracy. This paper describes the comparative implementation of four parallel dynamic simulation schemes in two state-of-the-art HPC environments: Message Passing Interface (MPI) and Open Multi-Processing (OpenMP).more » These implementations serve to match the application with dedicated multi-processor computing hardware and maximize the utilization and benefits of HPC during the development process.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Klitsner, Tom

The recent Executive Order creating the National Strategic Computing Initiative (NSCI) recognizes the value of high performance computing for economic competitiveness and scientific discovery and commits to accelerate delivery of exascale computing. The HPC programs at Sandia –the NNSA ASC program and Sandia’s Institutional HPC Program– are focused on ensuring that Sandia has the resources necessary to deliver computation in the national interest.

Running ANSYS Fluent on the WinHPC System | High-Performance Computing |

Science.gov Websites

. If you don't have one, see WinHPC system user basics. Check License Use Status Start > All Jason Lustbader. Run Using Fluent Launcher Start Fluent launcher by opening: Start > All Programs > . Available node groups can be found from HPC Job Manager. Start > All Programs > Microsoft HPC Pack

Cognitive Model Exploration and Optimization: A New Challenge for Computational Science

DTIC Science & Technology

2010-03-01

the generation and analysis of computational cognitive models to explain various aspects of cognition. Typically the behavior of these models...computational scale of a workstation, so we have turned to high performance computing (HPC) clusters and volunteer computing for large-scale...computational resources. The majority of applications on the Department of Defense HPC clusters focus on solving partial differential equations (Post

Kevin Regimbal | NREL

Science.gov Websites

-275-4303 Kevin Regimbal oversees NREL's High Performance Computing (HPC) Systems & Operations , engineering, and operations. Kevin is interested in data center design and computing as well as data center integration and optimization. Professional Experience HPC oversight: program manager, project manager, center

HPC in a HEP lab: lessons learned from setting up cost-effective HPC clusters

NASA Astrophysics Data System (ADS)

Husejko, Michal; Agtzidis, Ioannis; Baehler, Pierre; Dul, Tadeusz; Evans, John; Himyr, Nils; Meinhard, Helge

2015-12-01

In this paper we present our findings gathered during the evaluation and testing of Windows Server High-Performance Computing (Windows HPC) in view of potentially using it as a production HPC system for engineering applications. The Windows HPC package, an extension of Microsofts Windows Server product, provides all essential interfaces, utilities and management functionality for creating, operating and monitoring a Windows-based HPC cluster infrastructure. The evaluation and test phase was focused on verifying the functionalities of Windows HPC, its performance, support of commercial tools and the integration with the users work environment. We describe constraints imposed by the way the CERN Data Centre is operated, licensing for engineering tools and scalability and behaviour of the HPC engineering applications used at CERN. We will present an initial set of requirements, which were created based on the above constraints and requests from the CERN engineering user community. We will explain how we have configured Windows HPC clusters to provide job scheduling functionalities required to support the CERN engineering user community, quality of service, user- and project-based priorities, and fair access to limited resources. Finally, we will present several performance tests we carried out to verify Windows HPC performance and scalability.

Smart Sampling and HPC-based Probabilistic Look-ahead Contingency Analysis Implementation and its Evaluation with Real-world Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Yousu; Etingov, Pavel V.; Ren, Huiying

This paper describes a probabilistic look-ahead contingency analysis application that incorporates smart sampling and high-performance computing (HPC) techniques. Smart sampling techniques are implemented to effectively represent the structure and statistical characteristics of uncertainty introduced by different sources in the power system. They can significantly reduce the data set size required for multiple look-ahead contingency analyses, and therefore reduce the time required to compute them. High-performance-computing (HPC) techniques are used to further reduce computational time. These two techniques enable a predictive capability that forecasts the impact of various uncertainties on potential transmission limit violations. The developed package has been tested withmore » real world data from the Bonneville Power Administration. Case study results are presented to demonstrate the performance of the applications developed.« less

Prediction and characterization of application power use in a high-performance computing environment

DOE PAGES

Bugbee, Bruce; Phillips, Caleb; Egan, Hilary; ...

2017-02-27

Power use in data centers and high-performance computing (HPC) facilities has grown in tandem with increases in the size and number of these facilities. Substantial innovation is needed to enable meaningful reduction in energy footprints in leadership-class HPC systems. In this paper, we focus on characterizing and investigating application-level power usage. We demonstrate potential methods for predicting power usage based on a priori and in situ characteristics. Lastly, we highlight a potential use case of this method through a simulated power-aware scheduler using historical jobs from a real scientific HPC system.

High Performance Computing (HPC) Innovation Service Portal Pilots Cloud Computing (HPC-ISP Pilot Cloud Computing)

DTIC Science & Technology

2011-08-01

5 Figure 4 Architetural diagram of running Blender on Amazon EC2 through Nimbis...classification of streaming data. Example input images (top left). All digit prototypes (cluster centers) found, with size proportional to frequency (top...Figure 4 Architetural diagram of running Blender on Amazon EC2 through Nimbis 1 http

WinHPC System Software | High-Performance Computing | NREL

Science.gov Websites

Software WinHPC System Software Learn about the software applications, tools, toolchains, and for industrial applications. Intel Compilers Development Tool, Toolchain Suite featuring an industry

Economic Model For a Return on Investment Analysis of United States Government High Performance Computing (HPC) Research and Development (R & D) Investment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Joseph, Earl C.; Conway, Steve; Dekate, Chirag

This study investigated how high-performance computing (HPC) investments can improve economic success and increase scientific innovation. This research focused on the common good and provided uses for DOE, other government agencies, industry, and academia. The study created two unique economic models and an innovation index: 1 A macroeconomic model that depicts the way HPC investments result in economic advancements in the form of ROI in revenue (GDP), profits (and cost savings), and jobs. 2 A macroeconomic model that depicts the way HPC investments result in basic and applied innovations, looking at variations by sector, industry, country, and organization size. Amore » new innovation index that provides a means of measuring and comparing innovation levels. Key findings of the pilot study include: IDC collected the required data across a broad set of organizations, with enough detail to create these models and the innovation index. The research also developed an expansive list of HPC success stories.« less

The Case for Modular Redundancy in Large-Scale High Performance Computing Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Engelmann, Christian; Ong, Hong Hoe; Scott, Stephen L

2009-01-01

Recent investigations into resilience of large-scale high-performance computing (HPC) systems showed a continuous trend of decreasing reliability and availability. Newly installed systems have a lower mean-time to failure (MTTF) and a higher mean-time to recover (MTTR) than their predecessors. Modular redundancy is being used in many mission critical systems today to provide for resilience, such as for aerospace and command \\& control systems. The primary argument against modular redundancy for resilience in HPC has always been that the capability of a HPC system, and respective return on investment, would be significantly reduced. We argue that modular redundancy can significantly increasemore » compute node availability as it removes the impact of scale from single compute node MTTR. We further argue that single compute nodes can be much less reliable, and therefore less expensive, and still be highly available, if their MTTR/MTTF ratio is maintained.« less

Automating NEURON Simulation Deployment in Cloud Resources.

PubMed

Stockton, David B; Santamaria, Fidel

2017-01-01

Simulations in neuroscience are performed on local servers or High Performance Computing (HPC) facilities. Recently, cloud computing has emerged as a potential computational platform for neuroscience simulation. In this paper we compare and contrast HPC and cloud resources for scientific computation, then report how we deployed NEURON, a widely used simulator of neuronal activity, in three clouds: Chameleon Cloud, a hybrid private academic cloud for cloud technology research based on the OpenStack software; Rackspace, a public commercial cloud, also based on OpenStack; and Amazon Elastic Cloud Computing, based on Amazon's proprietary software. We describe the manual procedures and how to automate cloud operations. We describe extending our simulation automation software called NeuroManager (Stockton and Santamaria, Frontiers in Neuroinformatics, 2015), so that the user is capable of recruiting private cloud, public cloud, HPC, and local servers simultaneously with a simple common interface. We conclude by performing several studies in which we examine speedup, efficiency, total session time, and cost for sets of simulations of a published NEURON model.

Automating NEURON Simulation Deployment in Cloud Resources

PubMed Central

Santamaria, Fidel

2016-01-01

Simulations in neuroscience are performed on local servers or High Performance Computing (HPC) facilities. Recently, cloud computing has emerged as a potential computational platform for neuroscience simulation. In this paper we compare and contrast HPC and cloud resources for scientific computation, then report how we deployed NEURON, a widely used simulator of neuronal activity, in three clouds: Chameleon Cloud, a hybrid private academic cloud for cloud technology research based on the Open-Stack software; Rackspace, a public commercial cloud, also based on OpenStack; and Amazon Elastic Cloud Computing, based on Amazon’s proprietary software. We describe the manual procedures and how to automate cloud operations. We describe extending our simulation automation software called NeuroManager (Stockton and Santamaria, Frontiers in Neuroinformatics, 2015), so that the user is capable of recruiting private cloud, public cloud, HPC, and local servers simultaneously with a simple common interface. We conclude by performing several studies in which we examine speedup, efficiency, total session time, and cost for sets of simulations of a published NEURON model. PMID:27655341

Expanding HPC and Research Computing--The Sustainable Way

ERIC Educational Resources Information Center

Grush, Mary

2009-01-01

Increased demands for research and high-performance computing (HPC)--along with growing expectations for cost and environmental savings--are putting new strains on the campus data center. More and more, CIOs like the University of Notre Dame's (Indiana) Gordon Wishon are seeking creative ways to build more sustainable models for data center and…

Low latency network and distributed storage for next generation HPC systems: the ExaNeSt project

NASA Astrophysics Data System (ADS)

Ammendola, R.; Biagioni, A.; Cretaro, P.; Frezza, O.; Lo Cicero, F.; Lonardo, A.; Martinelli, M.; Paolucci, P. S.; Pastorelli, E.; Pisani, F.; Simula, F.; Vicini, P.; Navaridas, J.; Chaix, F.; Chrysos, N.; Katevenis, M.; Papaeustathiou, V.

2017-10-01

With processor architecture evolution, the HPC market has undergone a paradigm shift. The adoption of low-cost, Linux-based clusters extended the reach of HPC from its roots in modelling and simulation of complex physical systems to a broader range of industries, from biotechnology, cloud computing, computer analytics and big data challenges to manufacturing sectors. In this perspective, the near future HPC systems can be envisioned as composed of millions of low-power computing cores, densely packed — meaning cooling by appropriate technology — with a tightly interconnected, low latency and high performance network and equipped with a distributed storage architecture. Each of these features — dense packing, distributed storage and high performance interconnect — represents a challenge, made all the harder by the need to solve them at the same time. These challenges lie as stumbling blocks along the road towards Exascale-class systems; the ExaNeSt project acknowledges them and tasks itself with investigating ways around them.

System-Level Virtualization Research at Oak Ridge National Laboratory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Scott, Stephen L; Vallee, Geoffroy R; Naughton, III, Thomas J

2010-01-01

System-level virtualization is today enjoying a rebirth as a technique to effectively share what were then considered large computing resources to subsequently fade from the spotlight as individual workstations gained in popularity with a one machine - one user approach. One reason for this resurgence is that the simple workstation has grown in capability to rival that of anything available in the past. Thus, computing centers are again looking at the price/performance benefit of sharing that single computing box via server consolidation. However, industry is only concentrating on the benefits of using virtualization for server consolidation (enterprise computing) whereas ourmore » interest is in leveraging virtualization to advance high-performance computing (HPC). While these two interests may appear to be orthogonal, one consolidating multiple applications and users on a single machine while the other requires all the power from many machines to be dedicated solely to its purpose, we propose that virtualization does provide attractive capabilities that may be exploited to the benefit of HPC interests. This does raise the two fundamental questions of: is the concept of virtualization (a machine sharing technology) really suitable for HPC and if so, how does one go about leveraging these virtualization capabilities for the benefit of HPC. To address these questions, this document presents ongoing studies on the usage of system-level virtualization in a HPC context. These studies include an analysis of the benefits of system-level virtualization for HPC, a presentation of research efforts based on virtualization for system availability, and a presentation of research efforts for the management of virtual systems. The basis for this document was material presented by Stephen L. Scott at the Collaborative and Grid Computing Technologies meeting held in Cancun, Mexico on April 12-14, 2007.« less

An Innovative Approach to Bridge a Skill Gap and Grow a Workforce Pipeline: The Computer System, Cluster, and Networking Summer Institute

DOE Office of Scientific and Technical Information (OSTI.GOV)

Connor, Carolyn Marie; Jacobson, Andree Lars; Bonnie, Amanda Marie

Sustainable and effective computing infrastructure depends critically on the skills and expertise of domain scientists and of committed and well-trained advanced computing professionals. But, in its ongoing High Performance Computing (HPC) work, Los Alamos National Laboratory noted a persistent shortage of well-prepared applicants, particularly for entry-level cluster administration, file systems administration, and high speed networking positions. Further, based upon recruiting efforts and interactions with universities graduating students in related majors of interest (e.g., computer science (CS)), there has been a long standing skillset gap, as focused training in HPC topics is typically lacking or absent in undergraduate and in evenmore » many graduate programs. Given that the effective operation and use of HPC systems requires specialized and often advanced training, that there is a recognized HPC skillset gap, and that there is intense global competition for computing and computational science talent, there is a long-standing and critical need for innovative approaches to help bridge the gap and create a well-prepared, next generation HPC workforce. Our paper places this need in the context of the HPC work and workforce requirements at Los Alamos National Laboratory (LANL) and presents one such innovative program conceived to address the need, bridge the gap, and grow an HPC workforce pipeline at LANL. The Computer System, Cluster, and Networking Summer Institute (CSCNSI) completed its 10th year in 2016. The story of the CSCNSI and its evolution is detailed below with a description of the design of its Boot Camp, and a summary of its success and some key factors that have enabled that success.« less

An Innovative Approach to Bridge a Skill Gap and Grow a Workforce Pipeline: The Computer System, Cluster, and Networking Summer Institute

DOE PAGES

Connor, Carolyn Marie; Jacobson, Andree Lars; Bonnie, Amanda Marie; ...

2016-11-01

Sustainable and effective computing infrastructure depends critically on the skills and expertise of domain scientists and of committed and well-trained advanced computing professionals. But, in its ongoing High Performance Computing (HPC) work, Los Alamos National Laboratory noted a persistent shortage of well-prepared applicants, particularly for entry-level cluster administration, file systems administration, and high speed networking positions. Further, based upon recruiting efforts and interactions with universities graduating students in related majors of interest (e.g., computer science (CS)), there has been a long standing skillset gap, as focused training in HPC topics is typically lacking or absent in undergraduate and in evenmore » many graduate programs. Given that the effective operation and use of HPC systems requires specialized and often advanced training, that there is a recognized HPC skillset gap, and that there is intense global competition for computing and computational science talent, there is a long-standing and critical need for innovative approaches to help bridge the gap and create a well-prepared, next generation HPC workforce. Our paper places this need in the context of the HPC work and workforce requirements at Los Alamos National Laboratory (LANL) and presents one such innovative program conceived to address the need, bridge the gap, and grow an HPC workforce pipeline at LANL. The Computer System, Cluster, and Networking Summer Institute (CSCNSI) completed its 10th year in 2016. The story of the CSCNSI and its evolution is detailed below with a description of the design of its Boot Camp, and a summary of its success and some key factors that have enabled that success.« less

High Productivity Computing Systems and Competitiveness Initiative

DTIC Science & Technology

2007-07-01

planning committee for the annual, international Supercomputing Conference in 2004 and 2005. This is the leading HPC industry conference in the world. It...sector partnerships. Partnerships will form a key part of discussions at the 2nd High Performance Computing Users Conference, planned for July 13, 2005...other things an interagency roadmap for high-end computing core technologies and an accessibility improvement plan . Improving HPC Education and

DOE High Performance Computing Operational Review (HPCOR): Enabling Data-Driven Scientific Discovery at HPC Facilities

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gerber, Richard; Allcock, William; Beggio, Chris

2014-10-17

U.S. Department of Energy (DOE) High Performance Computing (HPC) facilities are on the verge of a paradigm shift in the way they deliver systems and services to science and engineering teams. Research projects are producing a wide variety of data at unprecedented scale and level of complexity, with community-specific services that are part of the data collection and analysis workflow. On June 18-19, 2014 representatives from six DOE HPC centers met in Oakland, CA at the DOE High Performance Operational Review (HPCOR) to discuss how they can best provide facilities and services to enable large-scale data-driven scientific discovery at themore » DOE national laboratories. The report contains findings from that review.« less

Chip-scale integrated optical interconnects: a key enabler for future high-performance computing

NASA Astrophysics Data System (ADS)

Haney, Michael; Nair, Rohit; Gu, Tian

2012-01-01

High Performance Computing (HPC) systems are putting ever-increasing demands on the throughput efficiency of their interconnection fabrics. In this paper, the limits of conventional metal trace-based inter-chip interconnect fabrics are examined in the context of state-of-the-art HPC systems, which currently operate near the 1 GFLOPS/W level. The analysis suggests that conventional metal trace interconnects will limit performance to approximately 6 GFLOPS/W in larger HPC systems that require many computer chips to be interconnected in parallel processing architectures. As the HPC communications bottlenecks push closer to the processing chips, integrated Optical Interconnect (OI) technology may provide the ultra-high bandwidths needed at the inter- and intra-chip levels. With inter-chip photonic link energies projected to be less than 1 pJ/bit, integrated OI is projected to enable HPC architecture scaling to the 50 GFLOPS/W level and beyond - providing a path to Peta-FLOPS-level HPC within a single rack, and potentially even Exa-FLOPSlevel HPC for large systems. A new hybrid integrated chip-scale OI approach is described and evaluated. The concept integrates a high-density polymer waveguide fabric directly on top of a multiple quantum well (MQW) modulator array that is area-bonded to the Silicon computing chip. Grayscale lithography is used to fabricate 5 μm x 5 μm polymer waveguides and associated novel small-footprint total internal reflection-based vertical input/output couplers directly onto a layer containing an array of GaAs MQW devices configured to be either absorption modulators or photodetectors. An external continuous wave optical "power supply" is coupled into the waveguide links. Contrast ratios were measured using a test rider chip in place of a Silicon processing chip. The results suggest that sub-pJ/b chip-scale communication is achievable with this concept. When integrated into high-density integrated optical interconnect fabrics, it could provide a seamless interconnect fabric spanning the intra-

Towards New Metrics for High-Performance Computing Resilience

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hukerikar, Saurabh; Ashraf, Rizwan A; Engelmann, Christian

Ensuring the reliability of applications is becoming an increasingly important challenge as high-performance computing (HPC) systems experience an ever-growing number of faults, errors and failures. While the HPC community has made substantial progress in developing various resilience solutions, it continues to rely on platform-based metrics to quantify application resiliency improvements. The resilience of an HPC application is concerned with the reliability of the application outcome as well as the fault handling efficiency. To understand the scope of impact, effective coverage and performance efficiency of existing and emerging resilience solutions, there is a need for new metrics. In this paper, wemore » develop new ways to quantify resilience that consider both the reliability and the performance characteristics of the solutions from the perspective of HPC applications. As HPC systems continue to evolve in terms of scale and complexity, it is expected that applications will experience various types of faults, errors and failures, which will require applications to apply multiple resilience solutions across the system stack. The proposed metrics are intended to be useful for understanding the combined impact of these solutions on an application's ability to produce correct results and to evaluate their overall impact on an application's performance in the presence of various modes of faults.« less

User Accounts | High-Performance Computing | NREL

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see information on user account policies. ACCOUNT PASSWORDS Logging in for the first time? Forgot your Accounts User Accounts Learn how to request an NREL HPC user account. Request an HPC Account To request an HPC account, please complete our request form. This form is provided using DocuSign. REQUEST

The impact of the U.S. supercomputing initiative will be global

DOE Office of Scientific and Technical Information (OSTI.GOV)

Crawford, Dona

2016-01-15

Last July, President Obama issued an executive order that created a coordinated federal strategy for HPC research, development, and deployment called the U.S. National Strategic Computing Initiative (NSCI). However, this bold, necessary step toward building the next generation of supercomputers has inaugurated a new era for U.S. high performance computing (HPC).

High-Performance Computing and Visualization | Energy Systems Integration

Science.gov Websites

Facility | NREL High-Performance Computing and Visualization High-Performance Computing and Visualization High-performance computing (HPC) and visualization at NREL propel technology innovation as a . Capabilities High-Performance Computing NREL is home to Peregrine-the largest high-performance computing system

Performance Analysis Tool for HPC and Big Data Applications on Scientific Clusters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yoo, Wucherl; Koo, Michelle; Cao, Yu

Big data is prevalent in HPC computing. Many HPC projects rely on complex workflows to analyze terabytes or petabytes of data. These workflows often require running over thousands of CPU cores and performing simultaneous data accesses, data movements, and computation. It is challenging to analyze the performance involving terabytes or petabytes of workflow data or measurement data of the executions, from complex workflows over a large number of nodes and multiple parallel task executions. To help identify performance bottlenecks or debug the performance issues in large-scale scientific applications and scientific clusters, we have developed a performance analysis framework, using state-ofthe-more » art open-source big data processing tools. Our tool can ingest system logs and application performance measurements to extract key performance features, and apply the most sophisticated statistical tools and data mining methods on the performance data. It utilizes an efficient data processing engine to allow users to interactively analyze a large amount of different types of logs and measurements. To illustrate the functionality of the big data analysis framework, we conduct case studies on the workflows from an astronomy project known as the Palomar Transient Factory (PTF) and the job logs from the genome analysis scientific cluster. Our study processed many terabytes of system logs and application performance measurements collected on the HPC systems at NERSC. The implementation of our tool is generic enough to be used for analyzing the performance of other HPC systems and Big Data workows.« less

Connecting to HPC Systems | High-Performance Computing | NREL

Science.gov Websites

one of the following methods, which use multi-factor authentication. First, you will need to set up If you just need access to a command line on an HPC system, use one of the following methods

Direct SSH Gateway Access to Peregrine | High Performance Computing |

Science.gov Websites

can access peregrine-ssh.nrel.gov, you must have: An active NREL HPC user account (see User Accounts ) An OTP Token (see One Time Password Tokens) Logging into peregrine-ssh.nrel.gov With your HPC account

Parallel computing in genomic research: advances and applications

PubMed Central

Ocaña, Kary; de Oliveira, Daniel

2015-01-01

Today’s genomic experiments have to process the so-called “biological big data” that is now reaching the size of Terabytes and Petabytes. To process this huge amount of data, scientists may require weeks or months if they use their own workstations. Parallelism techniques and high-performance computing (HPC) environments can be applied for reducing the total processing time and to ease the management, treatment, and analyses of this data. However, running bioinformatics experiments in HPC environments such as clouds, grids, clusters, and graphics processing unit requires the expertise from scientists to integrate computational, biological, and mathematical techniques and technologies. Several solutions have already been proposed to allow scientists for processing their genomic experiments using HPC capabilities and parallelism techniques. This article brings a systematic review of literature that surveys the most recently published research involving genomics and parallel computing. Our objective is to gather the main characteristics, benefits, and challenges that can be considered by scientists when running their genomic experiments to benefit from parallelism techniques and HPC capabilities. PMID:26604801

Parallel computing in genomic research: advances and applications.

PubMed

Ocaña, Kary; de Oliveira, Daniel

2015-01-01

Today's genomic experiments have to process the so-called "biological big data" that is now reaching the size of Terabytes and Petabytes. To process this huge amount of data, scientists may require weeks or months if they use their own workstations. Parallelism techniques and high-performance computing (HPC) environments can be applied for reducing the total processing time and to ease the management, treatment, and analyses of this data. However, running bioinformatics experiments in HPC environments such as clouds, grids, clusters, and graphics processing unit requires the expertise from scientists to integrate computational, biological, and mathematical techniques and technologies. Several solutions have already been proposed to allow scientists for processing their genomic experiments using HPC capabilities and parallelism techniques. This article brings a systematic review of literature that surveys the most recently published research involving genomics and parallel computing. Our objective is to gather the main characteristics, benefits, and challenges that can be considered by scientists when running their genomic experiments to benefit from parallelism techniques and HPC capabilities.

Active Flash: Out-of-core Data Analytics on Flash Storage

DOE Office of Scientific and Technical Information (OSTI.GOV)

Boboila, Simona; Kim, Youngjae; Vazhkudai, Sudharshan S

2012-01-01

Next generation science will increasingly come to rely on the ability to perform efficient, on-the-fly analytics of data generated by high-performance computing (HPC) simulations, modeling complex physical phenomena. Scientific computing workflows are stymied by the traditional chaining of simulation and data analysis, creating multiple rounds of redundant reads and writes to the storage system, which grows in cost with the ever-increasing gap between compute and storage speeds in HPC clusters. Recent HPC acquisitions have introduced compute node-local flash storage as a means to alleviate this I/O bottleneck. We propose a novel approach, Active Flash, to expedite data analysis pipelines bymore » migrating to the location of the data, the flash device itself. We argue that Active Flash has the potential to enable true out-of-core data analytics by freeing up both the compute core and the associated main memory. By performing analysis locally, dependence on limited bandwidth to a central storage system is reduced, while allowing this analysis to proceed in parallel with the main application. In addition, offloading work from the host to the more power-efficient controller reduces peak system power usage, which is already in the megawatt range and poses a major barrier to HPC system scalability. We propose an architecture for Active Flash, explore energy and performance trade-offs in moving computation from host to storage, demonstrate the ability of appropriate embedded controllers to perform data analysis and reduction tasks at speeds sufficient for this application, and present a simulation study of Active Flash scheduling policies. These results show the viability of the Active Flash model, and its capability to potentially have a transformative impact on scientific data analysis.« less

Enabling Diverse Software Stacks on Supercomputers using High Performance Virtual Clusters.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Younge, Andrew J.; Pedretti, Kevin; Grant, Ryan

While large-scale simulations have been the hallmark of the High Performance Computing (HPC) community for decades, Large Scale Data Analytics (LSDA) workloads are gaining attention within the scientific community not only as a processing component to large HPC simulations, but also as standalone scientific tools for knowledge discovery. With the path towards Exascale, new HPC runtime systems are also emerging in a way that differs from classical distributed com- puting models. However, system software for such capabilities on the latest extreme-scale DOE supercomputing needs to be enhanced to more appropriately support these types of emerging soft- ware ecosystems. In thismore » paper, we propose the use of Virtual Clusters on advanced supercomputing resources to enable systems to support not only HPC workloads, but also emerging big data stacks. Specifi- cally, we have deployed the KVM hypervisor within Cray's Compute Node Linux on a XC-series supercomputer testbed. We also use libvirt and QEMU to manage and provision VMs directly on compute nodes, leveraging Ethernet-over-Aries network emulation. To our knowledge, this is the first known use of KVM on a true MPP supercomputer. We investigate the overhead our solution using HPC benchmarks, both evaluating single-node performance as well as weak scaling of a 32-node virtual cluster. Overall, we find single node performance of our solution using KVM on a Cray is very efficient with near-native performance. However overhead increases by up to 20% as virtual cluster size increases, due to limitations of the Ethernet-over-Aries bridged network. Furthermore, we deploy Apache Spark with large data analysis workloads in a Virtual Cluster, ef- fectively demonstrating how diverse software ecosystems can be supported by High Performance Virtual Clusters.« less

Trends in data locality abstractions for HPC systems

DOE PAGES

Unat, Didem; Dubey, Anshu; Hoefler, Torsten; ...

2017-05-10

The cost of data movement has always been an important concern in high performance computing (HPC) systems. It has now become the dominant factor in terms of both energy consumption and performance. Support for expression of data locality has been explored in the past, but those efforts have had only modest success in being adopted in HPC applications for various reasons. However, with the increasing complexity of the memory hierarchy and higher parallelism in emerging HPC systems, locality management has acquired a new urgency. Developers can no longer limit themselves to low-level solutions and ignore the potential for productivity andmore » performance portability obtained by using locality abstractions. Fortunately, the trend emerging in recent literature on the topic alleviates many of the concerns that got in the way of their adoption by application developers. Data locality abstractions are available in the forms of libraries, data structures, languages and runtime systems; a common theme is increasing productivity without sacrificing performance. Furthermore, this paper examines these trends and identifies commonalities that can combine various locality concepts to develop a comprehensive approach to expressing and managing data locality on future large-scale high-performance computing systems.« less

Strengthening LLNL Missions through Laboratory Directed Research and Development in High Performance Computing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Willis, D. K.

2016-12-01

High performance computing (HPC) has been a defining strength of Lawrence Livermore National Laboratory (LLNL) since its founding. Livermore scientists have designed and used some of the world’s most powerful computers to drive breakthroughs in nearly every mission area. Today, the Laboratory is recognized as a world leader in the application of HPC to complex science, technology, and engineering challenges. Most importantly, HPC has been integral to the National Nuclear Security Administration’s (NNSA’s) Stockpile Stewardship Program—designed to ensure the safety, security, and reliability of our nuclear deterrent without nuclear testing. A critical factor behind Lawrence Livermore’s preeminence in HPC ismore » the ongoing investments made by the Laboratory Directed Research and Development (LDRD) Program in cutting-edge concepts to enable efficient utilization of these powerful machines. Congress established the LDRD Program in 1991 to maintain the technical vitality of the Department of Energy (DOE) national laboratories. Since then, LDRD has been, and continues to be, an essential tool for exploring anticipated needs that lie beyond the planning horizon of our programs and for attracting the next generation of talented visionaries. Through LDRD, Livermore researchers can examine future challenges, propose and explore innovative solutions, and deliver creative approaches to support our missions. The present scientific and technical strengths of the Laboratory are, in large part, a product of past LDRD investments in HPC. Here, we provide seven examples of LDRD projects from the past decade that have played a critical role in building LLNL’s HPC, computer science, mathematics, and data science research capabilities, and describe how they have impacted LLNL’s mission.« less

Computational Science News | Computational Science | NREL

Science.gov Websites

-Cooled High-Performance Computing Technology at the ESIF February 28, 2018 NREL Launches New Website for High-Performance Computing System Users The National Renewable Energy Laboratory (NREL) Computational Science Center has launched a revamped website for users of the lab's high-performance computing (HPC

Performance Analysis, Modeling and Scaling of HPC Applications and Tools

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bhatele, Abhinav

2016-01-13

E cient use of supercomputers at DOE centers is vital for maximizing system throughput, mini- mizing energy costs and enabling science breakthroughs faster. This requires complementary e orts along several directions to optimize the performance of scienti c simulation codes and the under- lying runtimes and software stacks. This in turn requires providing scalable performance analysis tools and modeling techniques that can provide feedback to physicists and computer scientists developing the simulation codes and runtimes respectively. The PAMS project is using time allocations on supercomputers at ALCF, NERSC and OLCF to further the goals described above by performing research alongmore » the following fronts: 1. Scaling Study of HPC applications; 2. Evaluation of Programming Models; 3. Hardening of Performance Tools; 4. Performance Modeling of Irregular Codes; and 5. Statistical Analysis of Historical Performance Data. We are a team of computer and computational scientists funded by both DOE/NNSA and DOE/ ASCR programs such as ECRP, XStack (Traleika Glacier, PIPER), ExaOSR (ARGO), SDMAV II (MONA) and PSAAP II (XPACC). This allocation will enable us to study big data issues when analyzing performance on leadership computing class systems and to assist the HPC community in making the most e ective use of these resources.« less

Getting ready for petaflop capacities and beyond: a utility perspective

NASA Astrophysics Data System (ADS)

Hamelin, J. F.; Berthou, J. Y.

2008-07-01

Why should EDF, the leading producer and marketer of electricity in Europe, start adding teraflops to its terawatt-hours and become involved in high-performance computing (HPC)? In this paper we answer this question through examples of major opportunities that HPC brings to our business today and, we hope well into the future of petaflop and exaflop computing. Five cases are presented dealing with nondestructive testing, nuclear fuel management, mechanical behavior of nuclear fuel assemblies, water management, and energy management. For each case we show the benefits brought by HPC, describe the current level of numerical simulation performance, and discuss the perspectives for future steps. We also present the general background that explains why EDF is moving to this technology and briefly comment on the development of user-oriented simulation platforms.

Computational Science and Innovation

NASA Astrophysics Data System (ADS)

Dean, D. J.

2011-09-01

Simulations - utilizing computers to solve complicated science and engineering problems - are a key ingredient of modern science. The U.S. Department of Energy (DOE) is a world leader in the development of high-performance computing (HPC), the development of applied math and algorithms that utilize the full potential of HPC platforms, and the application of computing to science and engineering problems. An interesting general question is whether the DOE can strategically utilize its capability in simulations to advance innovation more broadly. In this article, I will argue that this is certainly possible.

ATLAS computing on CSCS HPC

NASA Astrophysics Data System (ADS)

Filipcic, A.; Haug, S.; Hostettler, M.; Walker, R.; Weber, M.

2015-12-01

The Piz Daint Cray XC30 HPC system at CSCS, the Swiss National Supercomputing centre, was the highest ranked European system on TOP500 in 2014, also featuring GPU accelerators. Event generation and detector simulation for the ATLAS experiment have been enabled for this machine. We report on the technical solutions, performance, HPC policy challenges and possible future opportunities for HEP on extreme HPC systems. In particular a custom made integration to the ATLAS job submission system has been developed via the Advanced Resource Connector (ARC) middleware. Furthermore, a partial GPU acceleration of the Geant4 detector simulations has been implemented.

2013 R&D 100 Award: ‘Miniapps’ Bolster High Performance Computing

ScienceCinema

Belak, Jim; Richards, David

2018-06-12

Two Livermore computer scientists served on a Sandia National Laboratories-led team that developed Mantevo Suite 1.0, the first integrated suite of small software programs, also called "miniapps," to be made available to the high performance computing (HPC) community. These miniapps facilitate the development of new HPC systems and the applications that run on them. Miniapps (miniature applications) serve as stripped down surrogates for complex, full-scale applications that can require a great deal of time and effort to port to a new HPC system because they often consist of hundreds of thousands of lines of code. The miniapps are a prototype that contains some or all of the essentials of the real application but with many fewer lines of code, making the miniapp more versatile for experimentation. This allows researchers to more rapidly explore options and optimize system design, greatly improving the chances the full-scale application will perform successfully. These miniapps have become essential tools for exploring complex design spaces because they can reliably predict the performance of full applications.

Quantifying Scheduling Challenges for Exascale System Software

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mondragon, Oscar; Bridges, Patrick G.; Jones, Terry R

2015-01-01

The move towards high-performance computing (HPC) ap- plications comprised of coupled codes and the need to dra- matically reduce data movement is leading to a reexami- nation of time-sharing vs. space-sharing in HPC systems. In this paper, we discuss and begin to quantify the perfor- mance impact of a move away from strict space-sharing of nodes for HPC applications. Specifically, we examine the po- tential performance cost of time-sharing nodes between ap- plication components, we determine whether a simple coor- dinated scheduling mechanism can address these problems, and we research how suitable simple constraint-based opti- mization techniques are for solvingmore » scheduling challenges in this regime. Our results demonstrate that current general- purpose HPC system software scheduling and resource al- location systems are subject to significant performance de- ciencies which we quantify for six representative applica- tions. Based on these results, we discuss areas in which ad- ditional research is needed to meet the scheduling challenges of next-generation HPC systems.« less

Havery Mudd 2014-2015 Computer Science Conduit Clinic Final Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aspesi, G; Bai, J; Deese, R

2015-05-12

Conduit, a new open-source library developed at Lawrence Livermore National Laboratories, provides a C++ application programming interface (API) to describe and access scientific data. Conduit’s primary use is for inmemory data exchange in high performance computing (HPC) applications. Our team tested and improved Conduit to make it more appealing to potential adopters in the HPC community. We extended Conduit’s capabilities by prototyping four libraries: one for parallel communication using MPI, one for I/O functionality, one for aggregating performance data, and one for data visualization.

Towards anatomic scale agent-based modeling with a massively parallel spatially explicit general-purpose model of enteric tissue (SEGMEnT_HPC).

PubMed

Cockrell, Robert Chase; Christley, Scott; Chang, Eugene; An, Gary

2015-01-01

Perhaps the greatest challenge currently facing the biomedical research community is the ability to integrate highly detailed cellular and molecular mechanisms to represent clinical disease states as a pathway to engineer effective therapeutics. This is particularly evident in the representation of organ-level pathophysiology in terms of abnormal tissue structure, which, through histology, remains a mainstay in disease diagnosis and staging. As such, being able to generate anatomic scale simulations is a highly desirable goal. While computational limitations have previously constrained the size and scope of multi-scale computational models, advances in the capacity and availability of high-performance computing (HPC) resources have greatly expanded the ability of computational models of biological systems to achieve anatomic, clinically relevant scale. Diseases of the intestinal tract are exemplary examples of pathophysiological processes that manifest at multiple scales of spatial resolution, with structural abnormalities present at the microscopic, macroscopic and organ-levels. In this paper, we describe a novel, massively parallel computational model of the gut, the Spatially Explicitly General-purpose Model of Enteric Tissue_HPC (SEGMEnT_HPC), which extends an existing model of the gut epithelium, SEGMEnT, in order to create cell-for-cell anatomic scale simulations. We present an example implementation of SEGMEnT_HPC that simulates the pathogenesis of ileal pouchitis, and important clinical entity that affects patients following remedial surgery for ulcerative colitis.

Contact Us | High-Performance Computing | NREL

Science.gov Websites

Select Peregrine Merlin WinHPC Allocation project handle (if requesting HPC account) Description of "SEND REQUEST" and nothing happens, it most likely means you forgot to provide information in a required field. You may need to scroll up to see what required information is missing

WinHPC System Programming | High-Performance Computing | NREL

Science.gov Websites

Programming WinHPC System Programming Learn how to build and run an MPI (message passing interface (mpi.h) and library (msmpi.lib) are. To build from the command line, run... Start > Intel Software Development Tools > Intel C++ Compiler Professional... > C++ Build Environment for applications running

High-Performance Computing Data Center Warm-Water Liquid Cooling |

Science.gov Websites

Computational Science | NREL Warm-Water Liquid Cooling High-Performance Computing Data Center Warm-Water Liquid Cooling NREL's High-Performance Computing Data Center (HPC Data Center) is liquid water Liquid cooling technologies offer a more energy-efficient solution that also allows for effective

Fingerprinting Communication and Computation on HPC Machines

DOE Office of Scientific and Technical Information (OSTI.GOV)

Peisert, Sean

2010-06-02

How do we identify what is actually running on high-performance computing systems? Names of binaries, dynamic libraries loaded, or other elements in a submission to a batch queue can give clues, but binary names can be changed, and libraries provide limited insight and resolution on the code being run. In this paper, we present a method for"fingerprinting" code running on HPC machines using elements of communication and computation. We then discuss how that fingerprint can be used to determine if the code is consistent with certain other types of codes, what a user usually runs, or what the user requestedmore » an allocation to do. In some cases, our techniques enable us to fingerprint HPC codes using runtime MPI data with a high degree of accuracy.« less

HPC enabled real-time remote processing of laparoscopic surgery

NASA Astrophysics Data System (ADS)

Ronaghi, Zahra; Sapra, Karan; Izard, Ryan; Duffy, Edward; Smith, Melissa C.; Wang, Kuang-Ching; Kwartowitz, David M.

2016-03-01

Laparoscopic surgery is a minimally invasive surgical technique. The benefit of small incisions has a disadvantage of limited visualization of subsurface tissues. Image-guided surgery (IGS) uses pre-operative and intra-operative images to map subsurface structures. One particular laparoscopic system is the daVinci-si robotic surgical system. The video streams generate approximately 360 megabytes of data per second. Real-time processing this large stream of data on a bedside PC, single or dual node setup, has become challenging and a high-performance computing (HPC) environment may not always be available at the point of care. To process this data on remote HPC clusters at the typical 30 frames per second rate, it is required that each 11.9 MB video frame be processed by a server and returned within 1/30th of a second. We have implement and compared performance of compression, segmentation and registration algorithms on Clemson's Palmetto supercomputer using dual NVIDIA K40 GPUs per node. Our computing framework will also enable reliability using replication of computation. We will securely transfer the files to remote HPC clusters utilizing an OpenFlow-based network service, Steroid OpenFlow Service (SOS) that can increase performance of large data transfers over long-distance and high bandwidth networks. As a result, utilizing high-speed OpenFlow- based network to access computing clusters with GPUs will improve surgical procedures by providing real-time medical image processing and laparoscopic data.

Additive Manufacturing and High-Performance Computing: a Disruptive Latent Technology

NASA Astrophysics Data System (ADS)

Goodwin, Bruce

2015-03-01

This presentation will discuss the relationship between recent advances in Additive Manufacturing (AM) technology, High-Performance Computing (HPC) simulation and design capabilities, and related advances in Uncertainty Quantification (UQ), and then examines their impacts upon national and international security. The presentation surveys how AM accelerates the fabrication process, while HPC combined with UQ provides a fast track for the engineering design cycle. The combination of AM and HPC/UQ almost eliminates the engineering design and prototype iterative cycle, thereby dramatically reducing cost of production and time-to-market. These methods thereby present significant benefits for US national interests, both civilian and military, in an age of austerity. Finally, considering cyber security issues and the advent of the ``cloud,'' these disruptive, currently latent technologies may well enable proliferation and so challenge both nuclear and non-nuclear aspects of international security.

Performance Analysis of Ivshmem for High-Performance Computing in Virtual Machines

NASA Astrophysics Data System (ADS)

Ivanovic, Pavle; Richter, Harald

2018-01-01

High-Performance computing (HPC) is rarely accomplished via virtual machines (VMs). In this paper, we present a remake of ivshmem which can change this. Ivshmem was a shared memory (SHM) between virtual machines on the same server, with SHM-access synchronization included, until about 5 years ago when newer versions of Linux and its virtualization library libvirt evolved. We restored that SHM-access synchronization feature because it is indispensable for HPC and made ivshmem runnable with contemporary versions of Linux, libvirt, KVM, QEMU and especially MPICH, which is an implementation of MPI - the standard HPC communication library. Additionally, MPICH was transparently modified by us to get ivshmem included, resulting in a three to ten times performance improvement compared to TCP/IP. Furthermore, we have transparently replaced MPI_PUT, a single-side MPICH communication mechanism, by an own MPI_PUT wrapper. As a result, our ivshmem even surpasses non-virtualized SHM data transfers for block lengths greater than 512 KBytes, showing the benefits of virtualization. All improvements were possible without using SR-IOV.

GraphMeta: Managing HPC Rich Metadata in Graphs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dai, Dong; Chen, Yong; Carns, Philip

High-performance computing (HPC) systems face increasingly critical metadata management challenges, especially in the approaching exascale era. These challenges arise not only from exploding metadata volumes, but also from increasingly diverse metadata, which contains data provenance and arbitrary user-defined attributes in addition to traditional POSIX metadata. This ‘rich’ metadata is becoming critical to supporting advanced data management functionality such as data auditing and validation. In our prior work, we identified a graph-based model as a promising solution to uniformly manage HPC rich metadata due to its flexibility and generality. However, at the same time, graph-based HPC rich metadata anagement also introducesmore » significant challenges to the underlying infrastructure. In this study, we first identify the challenges on the underlying infrastructure to support scalable, high-performance rich metadata management. Based on that, we introduce GraphMeta, a graphbased engine designed for this use case. It achieves performance scalability by introducing a new graph partitioning algorithm and a write-optimal storage engine. We evaluate GraphMeta under both synthetic and real HPC metadata workloads, compare it with other approaches, and demonstrate its advantages in terms of efficiency and usability for rich metadata management in HPC systems.« less

A configurable distributed high-performance computing framework for satellite's TDI-CCD imaging simulation

NASA Astrophysics Data System (ADS)

Xue, Bo; Mao, Bingjing; Chen, Xiaomei; Ni, Guoqiang

2010-11-01

This paper renders a configurable distributed high performance computing(HPC) framework for TDI-CCD imaging simulation. It uses strategy pattern to adapt multi-algorithms. Thus, this framework help to decrease the simulation time with low expense. Imaging simulation for TDI-CCD mounted on satellite contains four processes: 1) atmosphere leads degradation, 2) optical system leads degradation, 3) electronic system of TDI-CCD leads degradation and re-sampling process, 4) data integration. Process 1) to 3) utilize diversity data-intensity algorithms such as FFT, convolution and LaGrange Interpol etc., which requires powerful CPU. Even uses Intel Xeon X5550 processor, regular series process method takes more than 30 hours for a simulation whose result image size is 1500 * 1462. With literature study, there isn't any mature distributing HPC framework in this field. Here we developed a distribute computing framework for TDI-CCD imaging simulation, which is based on WCF[1], uses Client/Server (C/S) layer and invokes the free CPU resources in LAN. The server pushes the process 1) to 3) tasks to those free computing capacity. Ultimately we rendered the HPC in low cost. In the computing experiment with 4 symmetric nodes and 1 server , this framework reduced about 74% simulation time. Adding more asymmetric nodes to the computing network, the time decreased namely. In conclusion, this framework could provide unlimited computation capacity in condition that the network and task management server are affordable. And this is the brand new HPC solution for TDI-CCD imaging simulation and similar applications.

Data Transfer Study HPSS Archiving

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wynne, James; Parete-Koon, Suzanne T; Mitchell, Quinn

2015-01-01

The movement of the large amounts of data produced by codes run in a High Performance Computing (HPC) environment can be a bottleneck for project workflows. To balance filesystem capacity and performance requirements, HPC centers enforce data management policies to purge old files to make room for new computation and analysis results. Users at Oak Ridge Leadership Computing Facility (OLCF) and many other HPC user facilities must archive data to avoid data loss during purges, therefore the time associated with data movement for archiving is something that all users must consider. This study observed the difference in transfer speed frommore » the originating location on the Lustre filesystem to the more permanent High Performance Storage System (HPSS). The tests were done with a number of different transfer methods for files that spanned a variety of sizes and compositions that reflect OLCF user data. This data will be used to help users of Titan and other Cray supercomputers plan their workflow and data transfers so that they are most efficient for their project. We will also discuss best practice for maintaining data at shared user facilities.« less

NREL Evaluates Aquarius Liquid-Cooled High-Performance Computing Technology

Science.gov Websites

HPC and influence the modern data center designer towards adoption of liquid cooling. Our shared technology. Aquila and Sandia chose NREL's HPC Data Center for the initial installation and evaluation because the data center is configured for liquid cooling, along with the required instrumentation to

HPC Aspects of Variable-Resolution Global Climate Modeling using a Multi-scale Convection Parameterization

EPA Science Inventory

High performance computing (HPC) requirements for the new generation variable grid resolution (VGR) global climate models differ from that of traditional global models. A VGR global model with 15 km grids over the CONUS stretching to 60 km grids elsewhere will have about ~2.5 tim...

Analysis of scalability of high-performance 3D image processing platform for virtual colonoscopy

NASA Astrophysics Data System (ADS)

Yoshida, Hiroyuki; Wu, Yin; Cai, Wenli

2014-03-01

One of the key challenges in three-dimensional (3D) medical imaging is to enable the fast turn-around time, which is often required for interactive or real-time response. This inevitably requires not only high computational power but also high memory bandwidth due to the massive amount of data that need to be processed. For this purpose, we previously developed a software platform for high-performance 3D medical image processing, called HPC 3D-MIP platform, which employs increasingly available and affordable commodity computing systems such as the multicore, cluster, and cloud computing systems. To achieve scalable high-performance computing, the platform employed size-adaptive, distributable block volumes as a core data structure for efficient parallelization of a wide range of 3D-MIP algorithms, supported task scheduling for efficient load distribution and balancing, and consisted of a layered parallel software libraries that allow image processing applications to share the common functionalities. We evaluated the performance of the HPC 3D-MIP platform by applying it to computationally intensive processes in virtual colonoscopy. Experimental results showed a 12-fold performance improvement on a workstation with 12-core CPUs over the original sequential implementation of the processes, indicating the efficiency of the platform. Analysis of performance scalability based on the Amdahl's law for symmetric multicore chips showed the potential of a high performance scalability of the HPC 3DMIP platform when a larger number of cores is available.

Running Jobs on the Peregrine System | High-Performance Computing | NREL

Science.gov Websites

on the Peregrine high-performance computing (HPC) system. Running Different Types of Jobs Batch jobs scheduling policies - queue names, limits, etc. Requesting different node types Sample batch scripts

Algorithm for fast event parameters estimation on GEM acquired data

NASA Astrophysics Data System (ADS)

Linczuk, Paweł; Krawczyk, Rafał D.; Poźniak, Krzysztof T.; Kasprowicz, Grzegorz; Wojeński, Andrzej; Chernyshova, Maryna; Czarski, Tomasz

2016-09-01

We present study of a software-hardware environment for developing fast computation with high throughput and low latency methods, which can be used as back-end in High Energy Physics (HEP) and other High Performance Computing (HPC) systems, based on high amount of input from electronic sensor based front-end. There is a parallelization possibilities discussion and testing on Intel HPC solutions with consideration of applications with Gas Electron Multiplier (GEM) measurement systems presented in this paper.

Implementing an Affordable High-Performance Computing for Teaching-Oriented Computer Science Curriculum

ERIC Educational Resources Information Center

Abuzaghleh, Omar; Goldschmidt, Kathleen; Elleithy, Yasser; Lee, Jeongkyu

2013-01-01

With the advances in computing power, high-performance computing (HPC) platforms have had an impact on not only scientific research in advanced organizations but also computer science curriculum in the educational community. For example, multicore programming and parallel systems are highly desired courses in the computer science major. However,…

On efficiency of fire simulation realization: parallelization with greater number of computational meshes

NASA Astrophysics Data System (ADS)

Valasek, Lukas; Glasa, Jan

2017-12-01

Current fire simulation systems are capable to utilize advantages of high-performance computer (HPC) platforms available and to model fires efficiently in parallel. In this paper, efficiency of a corridor fire simulation on a HPC computer cluster is discussed. The parallel MPI version of Fire Dynamics Simulator is used for testing efficiency of selected strategies of allocation of computational resources of the cluster using a greater number of computational cores. Simulation results indicate that if the number of cores used is not equal to a multiple of the total number of cluster node cores there are allocation strategies which provide more efficient calculations.

High Performance Proactive Digital Forensics

NASA Astrophysics Data System (ADS)

Alharbi, Soltan; Moa, Belaid; Weber-Jahnke, Jens; Traore, Issa

2012-10-01

With the increase in the number of digital crimes and in their sophistication, High Performance Computing (HPC) is becoming a must in Digital Forensics (DF). According to the FBI annual report, the size of data processed during the 2010 fiscal year reached 3,086 TB (compared to 2,334 TB in 2009) and the number of agencies that requested Regional Computer Forensics Laboratory assistance increasing from 689 in 2009 to 722 in 2010. Since most investigation tools are both I/O and CPU bound, the next-generation DF tools are required to be distributed and offer HPC capabilities. The need for HPC is even more evident in investigating crimes on clouds or when proactive DF analysis and on-site investigation, requiring semi-real time processing, are performed. Although overcoming the performance challenge is a major goal in DF, as far as we know, there is almost no research on HPC-DF except for few papers. As such, in this work, we extend our work on the need of a proactive system and present a high performance automated proactive digital forensic system. The most expensive phase of the system, namely proactive analysis and detection, uses a parallel extension of the iterative z algorithm. It also implements new parallel information-based outlier detection algorithms to proactively and forensically handle suspicious activities. To analyse a large number of targets and events and continuously do so (to capture the dynamics of the system), we rely on a multi-resolution approach to explore the digital forensic space. Data set from the Honeynet Forensic Challenge in 2001 is used to evaluate the system from DF and HPC perspectives.

HPC Annual Report 2017

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dennig, Yasmin

Sandia National Laboratories has a long history of significant contributions to the high performance community and industry. Our innovative computer architectures allowed the United States to become the first to break the teraFLOP barrier—propelling us to the international spotlight. Our advanced simulation and modeling capabilities have been integral in high consequence US operations such as Operation Burnt Frost. Strong partnerships with industry leaders, such as Cray, Inc. and Goodyear, have enabled them to leverage our high performance computing (HPC) capabilities to gain a tremendous competitive edge in the marketplace. As part of our continuing commitment to providing modern computing infrastructuremore » and systems in support of Sandia missions, we made a major investment in expanding Building 725 to serve as the new home of HPC systems at Sandia. Work is expected to be completed in 2018 and will result in a modern facility of approximately 15,000 square feet of computer center space. The facility will be ready to house the newest National Nuclear Security Administration/Advanced Simulation and Computing (NNSA/ASC) Prototype platform being acquired by Sandia, with delivery in late 2019 or early 2020. This new system will enable continuing advances by Sandia science and engineering staff in the areas of operating system R&D, operation cost effectiveness (power and innovative cooling technologies), user environment and application code performance.« less

Large-scale parallel genome assembler over cloud computing environment.

PubMed

Das, Arghya Kusum; Koppa, Praveen Kumar; Goswami, Sayan; Platania, Richard; Park, Seung-Jong

2017-06-01

The size of high throughput DNA sequencing data has already reached the terabyte scale. To manage this huge volume of data, many downstream sequencing applications started using locality-based computing over different cloud infrastructures to take advantage of elastic (pay as you go) resources at a lower cost. However, the locality-based programming model (e.g. MapReduce) is relatively new. Consequently, developing scalable data-intensive bioinformatics applications using this model and understanding the hardware environment that these applications require for good performance, both require further research. In this paper, we present a de Bruijn graph oriented Parallel Giraph-based Genome Assembler (GiGA), as well as the hardware platform required for its optimal performance. GiGA uses the power of Hadoop (MapReduce) and Giraph (large-scale graph analysis) to achieve high scalability over hundreds of compute nodes by collocating the computation and data. GiGA achieves significantly higher scalability with competitive assembly quality compared to contemporary parallel assemblers (e.g. ABySS and Contrail) over traditional HPC cluster. Moreover, we show that the performance of GiGA is significantly improved by using an SSD-based private cloud infrastructure over traditional HPC cluster. We observe that the performance of GiGA on 256 cores of this SSD-based cloud infrastructure closely matches that of 512 cores of traditional HPC cluster.

Towards Anatomic Scale Agent-Based Modeling with a Massively Parallel Spatially Explicit General-Purpose Model of Enteric Tissue (SEGMEnT_HPC)

PubMed Central

Cockrell, Robert Chase; Christley, Scott; Chang, Eugene; An, Gary

2015-01-01

Perhaps the greatest challenge currently facing the biomedical research community is the ability to integrate highly detailed cellular and molecular mechanisms to represent clinical disease states as a pathway to engineer effective therapeutics. This is particularly evident in the representation of organ-level pathophysiology in terms of abnormal tissue structure, which, through histology, remains a mainstay in disease diagnosis and staging. As such, being able to generate anatomic scale simulations is a highly desirable goal. While computational limitations have previously constrained the size and scope of multi-scale computational models, advances in the capacity and availability of high-performance computing (HPC) resources have greatly expanded the ability of computational models of biological systems to achieve anatomic, clinically relevant scale. Diseases of the intestinal tract are exemplary examples of pathophysiological processes that manifest at multiple scales of spatial resolution, with structural abnormalities present at the microscopic, macroscopic and organ-levels. In this paper, we describe a novel, massively parallel computational model of the gut, the Spatially Explicitly General-purpose Model of Enteric Tissue_HPC (SEGMEnT_HPC), which extends an existing model of the gut epithelium, SEGMEnT, in order to create cell-for-cell anatomic scale simulations. We present an example implementation of SEGMEnT_HPC that simulates the pathogenesis of ileal pouchitis, and important clinical entity that affects patients following remedial surgery for ulcerative colitis. PMID:25806784

Webinar: Delivering Transformational HPC Solutions to Industry

ScienceCinema

Streitz, Frederick

2018-01-16

Dr. Frederick Streitz, director of the High Performance Computing Innovation Center, discusses Lawrence Livermore National Laboratory computational capabilities and expertise available to industry in this webinar.

Shifter: Containers for HPC

NASA Astrophysics Data System (ADS)

Gerhardt, Lisa; Bhimji, Wahid; Canon, Shane; Fasel, Markus; Jacobsen, Doug; Mustafa, Mustafa; Porter, Jeff; Tsulaia, Vakho

2017-10-01

Bringing HEP computing to HPC can be difficult. Software stacks are often very complicated with numerous dependencies that are difficult to get installed on an HPC system. To address this issue, NERSC has created Shifter, a framework that delivers Docker-like functionality to HPC. It works by extracting images from native formats and converting them to a common format that is optimally tuned for the HPC environment. We have used Shifter to deliver the CVMFS software stack for ALICE, ATLAS, and STAR on the supercomputers at NERSC. As well as enabling the distribution multi-TB sized CVMFS stacks to HPC, this approach also offers performance advantages. Software startup times are significantly reduced and load times scale with minimal variation to 1000s of nodes. We profile several successful examples of scientists using Shifter to make scientific analysis easily customizable and scalable. We will describe the Shifter framework and several efforts in HEP and NP to use Shifter to deliver their software on the Cori HPC system.

P43-S Computational Biology Applications Suite for High-Performance Computing (BioHPC.net)

PubMed Central

Pillardy, J.

2007-01-01

One of the challenges of high-performance computing (HPC) is user accessibility. At the Cornell University Computational Biology Service Unit, which is also a Microsoft HPC institute, we have developed a computational biology application suite that allows researchers from biological laboratories to submit their jobs to the parallel cluster through an easy-to-use Web interface. Through this system, we are providing users with popular bioinformatics tools including BLAST, HMMER, InterproScan, and MrBayes. The system is flexible and can be easily customized to include other software. It is also scalable; the installation on our servers currently processes approximately 8500 job submissions per year, many of them requiring massively parallel computations. It also has a built-in user management system, which can limit software and/or database access to specified users. TAIR, the major database of the plant model organism Arabidopsis, and SGN, the international tomato genome database, are both using our system for storage and data analysis. The system consists of a Web server running the interface (ASP.NET C#), Microsoft SQL server (ADO.NET), compute cluster running Microsoft Windows, ftp server, and file server. Users can interact with their jobs and data via a Web browser, ftp, or e-mail. The interface is accessible at http://cbsuapps.tc.cornell.edu/.

Data Storage and Transfer | High-Performance Computing | NREL

Science.gov Websites

High-Performance Computing (HPC) systems. Photo of computer server wiring and lights, blurred to show data. WinSCP for Windows File Transfers Use to transfer files from a local computer to a remote computer. Robinhood for File Management Use this tool to manage your data files on Peregrine. Best

DOE Office of Scientific and Technical Information (OSTI.GOV)

Unat, Didem; Dubey, Anshu; Hoefler, Torsten

The cost of data movement has always been an important concern in high performance computing (HPC) systems. It has now become the dominant factor in terms of both energy consumption and performance. Support for expression of data locality has been explored in the past, but those efforts have had only modest success in being adopted in HPC applications for various reasons. However, with the increasing complexity of the memory hierarchy and higher parallelism in emerging HPC systems, locality management has acquired a new urgency. Developers can no longer limit themselves to low-level solutions and ignore the potential for productivity andmore » performance portability obtained by using locality abstractions. Fortunately, the trend emerging in recent literature on the topic alleviates many of the concerns that got in the way of their adoption by application developers. Data locality abstractions are available in the forms of libraries, data structures, languages and runtime systems; a common theme is increasing productivity without sacrificing performance. Furthermore, this paper examines these trends and identifies commonalities that can combine various locality concepts to develop a comprehensive approach to expressing and managing data locality on future large-scale high-performance computing systems.« less

System Resource Allocation Requests | High-Performance Computing | NREL

Science.gov Websites

Account to utilize the online allocation request system. If you need a HPC User Account, please request one online: Visit User Accounts. Click the green "Request Account" Button - this will direct . Follow the online instructions provided in the DocuSign form. Write "Need HPC User Account to use

Exploiting HPC Platforms for Metagenomics: Challenges and Opportunities (MICW - Metagenomics Informatics Challenges Workshop: 10K Genomes at a Time)

ScienceCinema

Canon, Shane

2018-01-24

DOE JGI's Zhong Wang, chair of the High-performance Computing session, gives a brief introduction before Berkeley Lab's Shane Canon talks about "Exploiting HPC Platforms for Metagenomics: Challenges and Opportunities" at the Metagenomics Informatics Challenges Workshop held at the DOE JGI on October 12-13, 2011.

Divide and Conquer (DC) BLAST: fast and easy BLAST execution within HPC environments

DOE PAGES

Yim, Won Cheol; Cushman, John C.

2017-07-22

Bioinformatics is currently faced with very large-scale data sets that lead to computational jobs, especially sequence similarity searches, that can take absurdly long times to run. For example, the National Center for Biotechnology Information (NCBI) Basic Local Alignment Search Tool (BLAST and BLAST+) suite, which is by far the most widely used tool for rapid similarity searching among nucleic acid or amino acid sequences, is highly central processing unit (CPU) intensive. While the BLAST suite of programs perform searches very rapidly, they have the potential to be accelerated. In recent years, distributed computing environments have become more widely accessible andmore » used due to the increasing availability of high-performance computing (HPC) systems. Therefore, simple solutions for data parallelization are needed to expedite BLAST and other sequence analysis tools. However, existing software for parallel sequence similarity searches often requires extensive computational experience and skill on the part of the user. In order to accelerate BLAST and other sequence analysis tools, Divide and Conquer BLAST (DCBLAST) was developed to perform NCBI BLAST searches within a cluster, grid, or HPC environment by using a query sequence distribution approach. Scaling from one (1) to 256 CPU cores resulted in significant improvements in processing speed. Thus, DCBLAST dramatically accelerates the execution of BLAST searches using a simple, accessible, robust, and parallel approach. DCBLAST works across multiple nodes automatically and it overcomes the speed limitation of single-node BLAST programs. DCBLAST can be used on any HPC system, can take advantage of hundreds of nodes, and has no output limitations. Thus, this freely available tool simplifies distributed computation pipelines to facilitate the rapid discovery of sequence similarities between very large data sets.« less

Divide and Conquer (DC) BLAST: fast and easy BLAST execution within HPC environments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yim, Won Cheol; Cushman, John C.

Bioinformatics is currently faced with very large-scale data sets that lead to computational jobs, especially sequence similarity searches, that can take absurdly long times to run. For example, the National Center for Biotechnology Information (NCBI) Basic Local Alignment Search Tool (BLAST and BLAST+) suite, which is by far the most widely used tool for rapid similarity searching among nucleic acid or amino acid sequences, is highly central processing unit (CPU) intensive. While the BLAST suite of programs perform searches very rapidly, they have the potential to be accelerated. In recent years, distributed computing environments have become more widely accessible andmore » used due to the increasing availability of high-performance computing (HPC) systems. Therefore, simple solutions for data parallelization are needed to expedite BLAST and other sequence analysis tools. However, existing software for parallel sequence similarity searches often requires extensive computational experience and skill on the part of the user. In order to accelerate BLAST and other sequence analysis tools, Divide and Conquer BLAST (DCBLAST) was developed to perform NCBI BLAST searches within a cluster, grid, or HPC environment by using a query sequence distribution approach. Scaling from one (1) to 256 CPU cores resulted in significant improvements in processing speed. Thus, DCBLAST dramatically accelerates the execution of BLAST searches using a simple, accessible, robust, and parallel approach. DCBLAST works across multiple nodes automatically and it overcomes the speed limitation of single-node BLAST programs. DCBLAST can be used on any HPC system, can take advantage of hundreds of nodes, and has no output limitations. Thus, this freely available tool simplifies distributed computation pipelines to facilitate the rapid discovery of sequence similarities between very large data sets.« less

A framework for graph-based synthesis, analysis, and visualization of HPC cluster job data.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mayo, Jackson R.; Kegelmeyer, W. Philip, Jr.; Wong, Matthew H.

The monitoring and system analysis of high performance computing (HPC) clusters is of increasing importance to the HPC community. Analysis of HPC job data can be used to characterize system usage and diagnose and examine failure modes and their effects. This analysis is not straightforward, however, due to the complex relationships that exist between jobs. These relationships are based on a number of factors, including shared compute nodes between jobs, proximity of jobs in time, etc. Graph-based techniques represent an approach that is particularly well suited to this problem, and provide an effective technique for discovering important relationships in jobmore » queuing and execution data. The efficacy of these techniques is rooted in the use of a semantic graph as a knowledge representation tool. In a semantic graph job data, represented in a combination of numerical and textual forms, can be flexibly processed into edges, with corresponding weights, expressing relationships between jobs, nodes, users, and other relevant entities. This graph-based representation permits formal manipulation by a number of analysis algorithms. This report presents a methodology and software implementation that leverages semantic graph-based techniques for the system-level monitoring and analysis of HPC clusters based on job queuing and execution data. Ontology development and graph synthesis is discussed with respect to the domain of HPC job data. The framework developed automates the synthesis of graphs from a database of job information. It also provides a front end, enabling visualization of the synthesized graphs. Additionally, an analysis engine is incorporated that provides performance analysis, graph-based clustering, and failure prediction capabilities for HPC systems.« less

Research | Computational Science | NREL

Science.gov Websites

Research Research NREL's computational science experts use advanced high-performance computing (HPC technologies, thereby accelerating the transformation of our nation's energy system. Enabling High-Impact Research NREL's computational science capabilities enable high-impact research. Some recent examples

User-level framework for performance monitoring of HPC applications

NASA Astrophysics Data System (ADS)

Hristova, R.; Goranov, G.

2013-10-01

HP-SEE is an infrastructure that links the existing HPC facilities in South East Europe in a common infrastructure. The analysis of the performance monitoring of the High-Performance Computing (HPC) applications in the infrastructure can be useful for the end user as diagnostic for the overall performance of his applications. The existing monitoring tools for HP-SEE provide to the end user only aggregated information for all applications. Usually, the user does not have permissions to select only the relevant information for him and for his applications. In this article we present a framework for performance monitoring of the HPC applications in the HP-SEE infrastructure. The framework provides standardized performance metrics, which every user can use in order to monitor his applications. Furthermore as a part of the framework a program interface is developed. The interface allows the user to publish metrics data from his application and to read and analyze gathered information. Publishing and reading through the framework is possible only with grid certificate valid for the infrastructure. Therefore the user is authorized to access only the data for his applications.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lingerfelt, Eric J; Endeve, Eirik; Hui, Yawei

Improvements in scientific instrumentation allow imaging at mesoscopic to atomic length scales, many spectroscopic modes, and now--with the rise of multimodal acquisition systems and the associated processing capability--the era of multidimensional, informationally dense data sets has arrived. Technical issues in these combinatorial scientific fields are exacerbated by computational challenges best summarized as a necessity for drastic improvement in the capability to transfer, store, and analyze large volumes of data. The Bellerophon Environment for Analysis of Materials (BEAM) platform provides material scientists the capability to directly leverage the integrated computational and analytical power of High Performance Computing (HPC) to perform scalablemore » data analysis and simulation and manage uploaded data files via an intuitive, cross-platform client user interface. This framework delivers authenticated, "push-button" execution of complex user workflows that deploy data analysis algorithms and computational simulations utilizing compute-and-data cloud infrastructures and HPC environments like Titan at the Oak Ridge Leadershp Computing Facility (OLCF).« less

Desktop supercomputer: what can it do?

NASA Astrophysics Data System (ADS)

Bogdanov, A.; Degtyarev, A.; Korkhov, V.

2017-12-01

The paper addresses the issues of solving complex problems that require using supercomputers or multiprocessor clusters available for most researchers nowadays. Efficient distribution of high performance computing resources according to actual application needs has been a major research topic since high-performance computing (HPC) technologies became widely introduced. At the same time, comfortable and transparent access to these resources was a key user requirement. In this paper we discuss approaches to build a virtual private supercomputer available at user's desktop: a virtual computing environment tailored specifically for a target user with a particular target application. We describe and evaluate possibilities to create the virtual supercomputer based on light-weight virtualization technologies, and analyze the efficiency of our approach compared to traditional methods of HPC resource management.

Construction of the energy matrix for complex atoms. Part VIII: Hyperfine structure HPC calculations for terbium atom

NASA Astrophysics Data System (ADS)

Elantkowska, Magdalena; Ruczkowski, Jarosław; Sikorski, Andrzej; Dembczyński, Jerzy

2017-11-01

A parametric analysis of the hyperfine structure (hfs) for the even parity configurations of atomic terbium (Tb I) is presented in this work. We introduce the complete set of 4fN-core states in our high-performance computing (HPC) calculations. For calculations of the huge hyperfine structure matrix, requiring approximately 5000 hours when run on a single CPU, we propose the methods utilizing a personal computer cluster or, alternatively a cluster of Microsoft Azure virtual machines (VM). These methods give a factor 12 performance boost, enabling the calculations to complete in an acceptable time.

Linux VPN Set Up | High-Performance Computing | NREL

Science.gov Websites

methods to connect to NREL's HPC systems via the HPC VPN: one using a simple command line, and a second UserID in place of the one in the example image. Connection name: hpcvpn Gateway: hpcvpn.nrel.gov User hpcvpn option as seen in the following screen shot. Screenshot image NetworkManager will present you with

WinHPC System Policies | High-Performance Computing | NREL

Science.gov Websites

requiring high CPU utilization or large amounts of memory should be run on the worker nodes. WinHPC02 is not associated data are removed when NREL worker status is discontinued. Users should make arrangements to save other users. Licenses are returned to the license pool when other users close the application or after

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hussain, Hameed; Malik, Saif Ur Rehman; Hameed, Abdul

An efficient resource allocation is a fundamental requirement in high performance computing (HPC) systems. Many projects are dedicated to large-scale distributed computing systems that have designed and developed resource allocation mechanisms with a variety of architectures and services. In our study, through analysis, a comprehensive survey for describing resource allocation in various HPCs is reported. The aim of the work is to aggregate under a joint framework, the existing solutions for HPC to provide a thorough analysis and characteristics of the resource management and allocation strategies. Resource allocation mechanisms and strategies play a vital role towards the performance improvement ofmore » all the HPCs classifications. Therefore, a comprehensive discussion of widely used resource allocation strategies deployed in HPC environment is required, which is one of the motivations of this survey. Moreover, we have classified the HPC systems into three broad categories, namely: (a) cluster, (b) grid, and (c) cloud systems and define the characteristics of each class by extracting sets of common attributes. All of the aforementioned systems are cataloged into pure software and hybrid/hardware solutions. The system classification is used to identify approaches followed by the implementation of existing resource allocation strategies that are widely presented in the literature.« less

Development of a HIPAA-compliant environment for translational research data and analytics.

PubMed

Bradford, Wayne; Hurdle, John F; LaSalle, Bernie; Facelli, Julio C

2014-01-01

High-performance computing centers (HPC) traditionally have far less restrictive privacy management policies than those encountered in healthcare. We show how an HPC can be re-engineered to accommodate clinical data while retaining its utility in computationally intensive tasks such as data mining, machine learning, and statistics. We also discuss deploying protected virtual machines. A critical planning step was to engage the university's information security operations and the information security and privacy office. Access to the environment requires a double authentication mechanism. The first level of authentication requires access to the university's virtual private network and the second requires that the users be listed in the HPC network information service directory. The physical hardware resides in a data center with controlled room access. All employees of the HPC and its users take the university's local Health Insurance Portability and Accountability Act training series. In the first 3 years, researcher count has increased from 6 to 58.

Personalized cloud-based bioinformatics services for research and education: use cases and the elasticHPC package

PubMed Central

2012-01-01

Background Bioinformatics services have been traditionally provided in the form of a web-server that is hosted at institutional infrastructure and serves multiple users. This model, however, is not flexible enough to cope with the increasing number of users, increasing data size, and new requirements in terms of speed and availability of service. The advent of cloud computing suggests a new service model that provides an efficient solution to these problems, based on the concepts of "resources-on-demand" and "pay-as-you-go". However, cloud computing has not yet been introduced within bioinformatics servers due to the lack of usage scenarios and software layers that address the requirements of the bioinformatics domain. Results In this paper, we provide different use case scenarios for providing cloud computing based services, considering both the technical and financial aspects of the cloud computing service model. These scenarios are for individual users seeking computational power as well as bioinformatics service providers aiming at provision of personalized bioinformatics services to their users. We also present elasticHPC, a software package and a library that facilitates the use of high performance cloud computing resources in general and the implementation of the suggested bioinformatics scenarios in particular. Concrete examples that demonstrate the suggested use case scenarios with whole bioinformatics servers and major sequence analysis tools like BLAST are presented. Experimental results with large datasets are also included to show the advantages of the cloud model. Conclusions Our use case scenarios and the elasticHPC package are steps towards the provision of cloud based bioinformatics services, which would help in overcoming the data challenge of recent biological research. All resources related to elasticHPC and its web-interface are available at http://www.elasticHPC.org. PMID:23281941

Personalized cloud-based bioinformatics services for research and education: use cases and the elasticHPC package.

PubMed

El-Kalioby, Mohamed; Abouelhoda, Mohamed; Krüger, Jan; Giegerich, Robert; Sczyrba, Alexander; Wall, Dennis P; Tonellato, Peter

2012-01-01

Bioinformatics services have been traditionally provided in the form of a web-server that is hosted at institutional infrastructure and serves multiple users. This model, however, is not flexible enough to cope with the increasing number of users, increasing data size, and new requirements in terms of speed and availability of service. The advent of cloud computing suggests a new service model that provides an efficient solution to these problems, based on the concepts of "resources-on-demand" and "pay-as-you-go". However, cloud computing has not yet been introduced within bioinformatics servers due to the lack of usage scenarios and software layers that address the requirements of the bioinformatics domain. In this paper, we provide different use case scenarios for providing cloud computing based services, considering both the technical and financial aspects of the cloud computing service model. These scenarios are for individual users seeking computational power as well as bioinformatics service providers aiming at provision of personalized bioinformatics services to their users. We also present elasticHPC, a software package and a library that facilitates the use of high performance cloud computing resources in general and the implementation of the suggested bioinformatics scenarios in particular. Concrete examples that demonstrate the suggested use case scenarios with whole bioinformatics servers and major sequence analysis tools like BLAST are presented. Experimental results with large datasets are also included to show the advantages of the cloud model. Our use case scenarios and the elasticHPC package are steps towards the provision of cloud based bioinformatics services, which would help in overcoming the data challenge of recent biological research. All resources related to elasticHPC and its web-interface are available at http://www.elasticHPC.org.

Data Services in Support of High Performance Computing-Based Distributed Hydrologic Models

NASA Astrophysics Data System (ADS)

Tarboton, D. G.; Horsburgh, J. S.; Dash, P. K.; Gichamo, T.; Yildirim, A. A.; Jones, N.

2014-12-01

We have developed web-based data services to support the application of hydrologic models on High Performance Computing (HPC) systems. The purposes of these services are to provide hydrologic researchers, modelers, water managers, and users access to HPC resources without requiring them to become HPC experts and understanding the intrinsic complexities of the data services, so as to reduce the amount of time and effort spent in finding and organizing the data required to execute hydrologic models and data preprocessing tools on HPC systems. These services address some of the data challenges faced by hydrologic models that strive to take advantage of HPC. Needed data is often not in the form needed by such models, requiring researchers to spend time and effort on data preparation and preprocessing that inhibits or limits the application of these models. Another limitation is the difficult to use batch job control and queuing systems used by HPC systems. We have developed a REST-based gateway application programming interface (API) for authenticated access to HPC systems that abstracts away many of the details that are barriers to HPC use and enhances accessibility from desktop programming and scripting languages such as Python and R. We have used this gateway API to establish software services that support the delineation of watersheds to define a modeling domain, then extract terrain and land use information to automatically configure the inputs required for hydrologic models. These services support the Terrain Analysis Using Digital Elevation Model (TauDEM) tools for watershed delineation and generation of hydrology-based terrain information such as wetness index and stream networks. These services also support the derivation of inputs for the Utah Energy Balance snowmelt model used to address questions such as how climate, land cover and land use change may affect snowmelt inputs to runoff generation. To enhance access to the time varying climate data used to drive hydrologic models, we have developed services to downscale and re-grid nationally available climate analysis data from systems such as NLDAS and MERRA. These cases serve as examples for how this approach can be extended to other models to enhance the use of HPC for hydrologic modeling.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moreland, Kenneth; Sewell, Christopher; Usher, William

Here, one of the most critical challenges for high-performance computing (HPC) scientific visualization is execution on massively threaded processors. Of the many fundamental changes we are seeing in HPC systems, one of the most profound is a reliance on new processor types optimized for execution bandwidth over latency hiding. Our current production scientific visualization software is not designed for these new types of architectures. To address this issue, the VTK-m framework serves as a container for algorithms, provides flexible data representation, and simplifies the design of visualization algorithms on new and future computer architecture.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moreland, Kenneth; Sewell, Christopher; Usher, William

Execution on massively threaded processors is one of the most critical challenges for high-performance computing (HPC) scientific visualization. Of the many fundamental changes we are seeing in HPC systems, one of the most profound is a reliance on new processor types optimized for execution bandwidth over latency hiding. Moreover, our current production scientific visualization software is not designed for these new types of architectures. In order to address this issue, the VTK-m framework serves as a container for algorithms, provides flexible data representation, and simplifies the design of visualization algorithms on new and future computer architecture.

Implementation of Parallel Dynamic Simulation on Shared-Memory vs. Distributed-Memory Environments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jin, Shuangshuang; Chen, Yousu; Wu, Di

2015-12-09

Power system dynamic simulation computes the system response to a sequence of large disturbance, such as sudden changes in generation or load, or a network short circuit followed by protective branch switching operation. It consists of a large set of differential and algebraic equations, which is computational intensive and challenging to solve using single-processor based dynamic simulation solution. High-performance computing (HPC) based parallel computing is a very promising technology to speed up the computation and facilitate the simulation process. This paper presents two different parallel implementations of power grid dynamic simulation using Open Multi-processing (OpenMP) on shared-memory platform, and Messagemore » Passing Interface (MPI) on distributed-memory clusters, respectively. The difference of the parallel simulation algorithms and architectures of the two HPC technologies are illustrated, and their performances for running parallel dynamic simulation are compared and demonstrated.« less

A Modular Environment for Geophysical Inversion and Run-time Autotuning using Heterogeneous Computing Systems

NASA Astrophysics Data System (ADS)

Myre, Joseph M.

Heterogeneous computing systems have recently come to the forefront of the High-Performance Computing (HPC) community's interest. HPC computer systems that incorporate special purpose accelerators, such as Graphics Processing Units (GPUs), are said to be heterogeneous. Large scale heterogeneous computing systems have consistently ranked highly on the Top500 list since the beginning of the heterogeneous computing trend. By using heterogeneous computing systems that consist of both general purpose processors and special- purpose accelerators, the speed and problem size of many simulations could be dramatically increased. Ultimately this results in enhanced simulation capabilities that allows, in some cases for the first time, the execution of parameter space and uncertainty analyses, model optimizations, and other inverse modeling techniques that are critical for scientific discovery and engineering analysis. However, simplifying the usage and optimization of codes for heterogeneous computing systems remains a challenge. This is particularly true for scientists and engineers for whom understanding HPC architectures and undertaking performance analysis may not be primary research objectives. To enable scientists and engineers to remain focused on their primary research objectives, a modular environment for geophysical inversion and run-time autotuning on heterogeneous computing systems is presented. This environment is composed of three major components: 1) CUSH---a framework for reducing the complexity of programming heterogeneous computer systems, 2) geophysical inversion routines which can be used to characterize physical systems, and 3) run-time autotuning routines designed to determine configurations of heterogeneous computing systems in an attempt to maximize the performance of scientific and engineering codes. Using three case studies, a lattice-Boltzmann method, a non-negative least squares inversion, and a finite-difference fluid flow method, it is shown that this environment provides scientists and engineers with means to reduce the programmatic complexity of their applications, to perform geophysical inversions for characterizing physical systems, and to determine high-performing run-time configurations of heterogeneous computing systems using a run-time autotuner.

Final Report on the Proposal to Provide Asian Science and Technology Information

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kahaner, David K.

2003-07-23

The Asian Technology Information Program (ATIP) conducted a seven-month Asian science and technology information program for the Office:of Energy Research (ER), U.S: Department of Energy (DOE.) The seven-month program consists of 1) monitoring, analyzing, and dissemiuating science and technology trends and developments associated with Asian high performance computing and communications (HPC), networking, and associated topics, 2) access to ATIP's annual series of Asian S&T reports for ER and HPC related personnel and, 3) supporting DOE and ER designated visits to Asia to study and assess Asian HPC.

Clearing your Desk! Software and Data Services for Collaborative Web Based GIS Analysis

NASA Astrophysics Data System (ADS)

Tarboton, D. G.; Idaszak, R.; Horsburgh, J. S.; Ames, D. P.; Goodall, J. L.; Band, L. E.; Merwade, V.; Couch, A.; Hooper, R. P.; Maidment, D. R.; Dash, P. K.; Stealey, M.; Yi, H.; Gan, T.; Gichamo, T.; Yildirim, A. A.; Liu, Y.

2015-12-01

Can your desktop computer crunch the large GIS datasets that are becoming increasingly common across the geosciences? Do you have access to or the know-how to take advantage of advanced high performance computing (HPC) capability? Web based cyberinfrastructure takes work off your desk or laptop computer and onto infrastructure or "cloud" based data and processing servers. This talk will describe the HydroShare collaborative environment and web based services being developed to support the sharing and processing of hydrologic data and models. HydroShare supports the upload, storage, and sharing of a broad class of hydrologic data including time series, geographic features and raster datasets, multidimensional space-time data, and other structured collections of data. Web service tools and a Python client library provide researchers with access to HPC resources without requiring them to become HPC experts. This reduces the time and effort spent in finding and organizing the data required to prepare the inputs for hydrologic models and facilitates the management of online data and execution of models on HPC systems. This presentation will illustrate the use of web based data and computation services from both the browser and desktop client software. These web-based services implement the Terrain Analysis Using Digital Elevation Model (TauDEM) tools for watershed delineation, generation of hydrology-based terrain information, and preparation of hydrologic model inputs. They allow users to develop scripts on their desktop computer that call analytical functions that are executed completely in the cloud, on HPC resources using input datasets stored in the cloud, without installing specialized software, learning how to use HPC, or transferring large datasets back to the user's desktop. These cases serve as examples for how this approach can be extended to other models to enhance the use of web and data services in the geosciences.

Accessible high performance computing solutions for near real-time image processing for time critical applications

NASA Astrophysics Data System (ADS)

Bielski, Conrad; Lemoine, Guido; Syryczynski, Jacek

2009-09-01

High Performance Computing (HPC) hardware solutions such as grid computing and General Processing on a Graphics Processing Unit (GPGPU) are now accessible to users with general computing needs. Grid computing infrastructures in the form of computing clusters or blades are becoming common place and GPGPU solutions that leverage the processing power of the video card are quickly being integrated into personal workstations. Our interest in these HPC technologies stems from the need to produce near real-time maps from a combination of pre- and post-event satellite imagery in support of post-disaster management. Faster processing provides a twofold gain in this situation: 1. critical information can be provided faster and 2. more elaborate automated processing can be performed prior to providing the critical information. In our particular case, we test the use of the PANTEX index which is based on analysis of image textural measures extracted using anisotropic, rotation-invariant GLCM statistics. The use of this index, applied in a moving window, has been shown to successfully identify built-up areas in remotely sensed imagery. Built-up index image masks are important input to the structuring of damage assessment interpretation because they help optimise the workload. The performance of computing the PANTEX workflow is compared on two different HPC hardware architectures: (1) a blade server with 4 blades, each having dual quad-core CPUs and (2) a CUDA enabled GPU workstation. The reference platform is a dual CPU-quad core workstation and the PANTEX workflow total computing time is measured. Furthermore, as part of a qualitative evaluation, the differences in setting up and configuring various hardware solutions and the related software coding effort is presented.

Multi-Core Processor Memory Contention Benchmark Analysis Case Study

NASA Technical Reports Server (NTRS)

Simon, Tyler; McGalliard, James

2009-01-01

Multi-core processors dominate current mainframe, server, and high performance computing (HPC) systems. This paper provides synthetic kernel and natural benchmark results from an HPC system at the NASA Goddard Space Flight Center that illustrate the performance impacts of multi-core (dual- and quad-core) vs. single core processor systems. Analysis of processor design, application source code, and synthetic and natural test results all indicate that multi-core processors can suffer from significant memory subsystem contention compared to similar single-core processors.

HPC Access Using KVM over IP

DTIC Science & Technology

2007-06-08

Lightwave VDE /200 KVM-over-Fiber (Keyboard, Video and Mouse) devices installed throughout the TARDEC campus. Implementation of this system required...development effort through the pursuit of an Army-funded Phase-II Small Business Innovative Research (SBIR) effort with IP Video Systems (formerly known as...visualization capabilities of a DoD High- Performance Computing facility, many advanced features are necessary. TARDEC-HPC’s SBIR with IP Video Systems

Analysis of Application Power and Schedule Composition in a High Performance Computing Environment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Elmore, Ryan; Gruchalla, Kenny; Phillips, Caleb

As the capacity of high performance computing (HPC) systems continues to grow, small changes in energy management have the potential to produce significant energy savings. In this paper, we employ an extensive informatics system for aggregating and analyzing real-time performance and power use data to evaluate energy footprints of jobs running in an HPC data center. We look at the effects of algorithmic choices for a given job on the resulting energy footprints, and analyze application-specific power consumption, and summarize average power use in the aggregate. All of these views reveal meaningful power variance between classes of applications as wellmore » as chosen methods for a given job. Using these data, we discuss energy-aware cost-saving strategies based on reordering the HPC job schedule. Using historical job and power data, we present a hypothetical job schedule reordering that: (1) reduces the facility's peak power draw and (2) manages power in conjunction with a large-scale photovoltaic array. Lastly, we leverage this data to understand the practical limits on predicting key power use metrics at the time of submission.« less

Toward a Proof of Concept Cloud Framework for Physics Applications on Blue Gene Supercomputers

NASA Astrophysics Data System (ADS)

Dreher, Patrick; Scullin, William; Vouk, Mladen

2015-09-01

Traditional high performance supercomputers are capable of delivering large sustained state-of-the-art computational resources to physics applications over extended periods of time using batch processing mode operating environments. However, today there is an increasing demand for more complex workflows that involve large fluctuations in the levels of HPC physics computational requirements during the simulations. Some of the workflow components may also require a richer set of operating system features and schedulers than normally found in a batch oriented HPC environment. This paper reports on progress toward a proof of concept design that implements a cloud framework onto BG/P and BG/Q platforms at the Argonne Leadership Computing Facility. The BG/P implementation utilizes the Kittyhawk utility and the BG/Q platform uses an experimental heterogeneous FusedOS operating system environment. Both platforms use the Virtual Computing Laboratory as the cloud computing system embedded within the supercomputer. This proof of concept design allows a cloud to be configured so that it can capitalize on the specialized infrastructure capabilities of a supercomputer and the flexible cloud configurations without resorting to virtualization. Initial testing of the proof of concept system is done using the lattice QCD MILC code. These types of user reconfigurable environments have the potential to deliver experimental schedulers and operating systems within a working HPC environment for physics computations that may be different from the native OS and schedulers on production HPC supercomputers.

NREL's Building-Integrated Supercomputer Provides Heating and Efficient Computing (Fact Sheet)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

2014-09-01

NREL's Energy Systems Integration Facility (ESIF) is meant to investigate new ways to integrate energy sources so they work together efficiently, and one of the key tools to that investigation, a new supercomputer, is itself a prime example of energy systems integration. NREL teamed with Hewlett-Packard (HP) and Intel to develop the innovative warm-water, liquid-cooled Peregrine supercomputer, which not only operates efficiently but also serves as the primary source of building heat for ESIF offices and laboratories. This innovative high-performance computer (HPC) can perform more than a quadrillion calculations per second as part of the world's most energy-efficient HPC datamore » center.« less

Towards Portable Large-Scale Image Processing with High-Performance Computing.

PubMed

Huo, Yuankai; Blaber, Justin; Damon, Stephen M; Boyd, Brian D; Bao, Shunxing; Parvathaneni, Prasanna; Noguera, Camilo Bermudez; Chaganti, Shikha; Nath, Vishwesh; Greer, Jasmine M; Lyu, Ilwoo; French, William R; Newton, Allen T; Rogers, Baxter P; Landman, Bennett A

2018-05-03

High-throughput, large-scale medical image computing demands tight integration of high-performance computing (HPC) infrastructure for data storage, job distribution, and image processing. The Vanderbilt University Institute for Imaging Science (VUIIS) Center for Computational Imaging (CCI) has constructed a large-scale image storage and processing infrastructure that is composed of (1) a large-scale image database using the eXtensible Neuroimaging Archive Toolkit (XNAT), (2) a content-aware job scheduling platform using the Distributed Automation for XNAT pipeline automation tool (DAX), and (3) a wide variety of encapsulated image processing pipelines called "spiders." The VUIIS CCI medical image data storage and processing infrastructure have housed and processed nearly half-million medical image volumes with Vanderbilt Advanced Computing Center for Research and Education (ACCRE), which is the HPC facility at the Vanderbilt University. The initial deployment was natively deployed (i.e., direct installations on a bare-metal server) within the ACCRE hardware and software environments, which lead to issues of portability and sustainability. First, it could be laborious to deploy the entire VUIIS CCI medical image data storage and processing infrastructure to another HPC center with varying hardware infrastructure, library availability, and software permission policies. Second, the spiders were not developed in an isolated manner, which has led to software dependency issues during system upgrades or remote software installation. To address such issues, herein, we describe recent innovations using containerization techniques with XNAT/DAX which are used to isolate the VUIIS CCI medical image data storage and processing infrastructure from the underlying hardware and software environments. The newly presented XNAT/DAX solution has the following new features: (1) multi-level portability from system level to the application level, (2) flexible and dynamic software development and expansion, and (3) scalable spider deployment compatible with HPC clusters and local workstations.

Towards Cloud-based Asynchronous Elasticity for Iterative HPC Applications

NASA Astrophysics Data System (ADS)

da Rosa Righi, Rodrigo; Facco Rodrigues, Vinicius; André da Costa, Cristiano; Kreutz, Diego; Heiss, Hans-Ulrich

2015-10-01

Elasticity is one of the key features of cloud computing. It allows applications to dynamically scale computing and storage resources, avoiding over- and under-provisioning. In high performance computing (HPC), initiatives are normally modeled to handle bag-of-tasks or key-value applications through a load balancer and a loosely-coupled set of virtual machine (VM) instances. In the joint-field of Message Passing Interface (MPI) and tightly-coupled HPC applications, we observe the need of rewriting source codes, previous knowledge of the application and/or stop-reconfigure-and-go approaches to address cloud elasticity. Besides, there are problems related to how profit this new feature in the HPC scope, since in MPI 2.0 applications the programmers need to handle communicators by themselves, and a sudden consolidation of a VM, together with a process, can compromise the entire execution. To address these issues, we propose a PaaS-based elasticity model, named AutoElastic. It acts as a middleware that allows iterative HPC applications to take advantage of dynamic resource provisioning of cloud infrastructures without any major modification. AutoElastic provides a new concept denoted here as asynchronous elasticity, i.e., it provides a framework to allow applications to either increase or decrease their computing resources without blocking the current execution. The feasibility of AutoElastic is demonstrated through a prototype that runs a CPU-bound numerical integration application on top of the OpenNebula middleware. The results showed the saving of about 3 min at each scaling out operations, emphasizing the contribution of the new concept on contexts where seconds are precious.

JMS: An Open Source Workflow Management System and Web-Based Cluster Front-End for High Performance Computing.

PubMed

Brown, David K; Penkler, David L; Musyoka, Thommas M; Bishop, Özlem Tastan

2015-01-01

Complex computational pipelines are becoming a staple of modern scientific research. Often these pipelines are resource intensive and require days of computing time. In such cases, it makes sense to run them over high performance computing (HPC) clusters where they can take advantage of the aggregated resources of many powerful computers. In addition to this, researchers often want to integrate their workflows into their own web servers. In these cases, software is needed to manage the submission of jobs from the web interface to the cluster and then return the results once the job has finished executing. We have developed the Job Management System (JMS), a workflow management system and web interface for high performance computing (HPC). JMS provides users with a user-friendly web interface for creating complex workflows with multiple stages. It integrates this workflow functionality with the resource manager, a tool that is used to control and manage batch jobs on HPC clusters. As such, JMS combines workflow management functionality with cluster administration functionality. In addition, JMS provides developer tools including a code editor and the ability to version tools and scripts. JMS can be used by researchers from any field to build and run complex computational pipelines and provides functionality to include these pipelines in external interfaces. JMS is currently being used to house a number of bioinformatics pipelines at the Research Unit in Bioinformatics (RUBi) at Rhodes University. JMS is an open-source project and is freely available at https://github.com/RUBi-ZA/JMS.

JMS: An Open Source Workflow Management System and Web-Based Cluster Front-End for High Performance Computing

PubMed Central

Brown, David K.; Penkler, David L.; Musyoka, Thommas M.; Bishop, Özlem Tastan

2015-01-01

Complex computational pipelines are becoming a staple of modern scientific research. Often these pipelines are resource intensive and require days of computing time. In such cases, it makes sense to run them over high performance computing (HPC) clusters where they can take advantage of the aggregated resources of many powerful computers. In addition to this, researchers often want to integrate their workflows into their own web servers. In these cases, software is needed to manage the submission of jobs from the web interface to the cluster and then return the results once the job has finished executing. We have developed the Job Management System (JMS), a workflow management system and web interface for high performance computing (HPC). JMS provides users with a user-friendly web interface for creating complex workflows with multiple stages. It integrates this workflow functionality with the resource manager, a tool that is used to control and manage batch jobs on HPC clusters. As such, JMS combines workflow management functionality with cluster administration functionality. In addition, JMS provides developer tools including a code editor and the ability to version tools and scripts. JMS can be used by researchers from any field to build and run complex computational pipelines and provides functionality to include these pipelines in external interfaces. JMS is currently being used to house a number of bioinformatics pipelines at the Research Unit in Bioinformatics (RUBi) at Rhodes University. JMS is an open-source project and is freely available at https://github.com/RUBi-ZA/JMS. PMID:26280450

OCCAM: a flexible, multi-purpose and extendable HPC cluster

NASA Astrophysics Data System (ADS)

Aldinucci, M.; Bagnasco, S.; Lusso, S.; Pasteris, P.; Rabellino, S.; Vallero, S.

2017-10-01

The Open Computing Cluster for Advanced data Manipulation (OCCAM) is a multipurpose flexible HPC cluster designed and operated by a collaboration between the University of Torino and the Sezione di Torino of the Istituto Nazionale di Fisica Nucleare. It is aimed at providing a flexible, reconfigurable and extendable infrastructure to cater to a wide range of different scientific computing use cases, including ones from solid-state chemistry, high-energy physics, computer science, big data analytics, computational biology, genomics and many others. Furthermore, it will serve as a platform for R&D activities on computational technologies themselves, with topics ranging from GPU acceleration to Cloud Computing technologies. A heterogeneous and reconfigurable system like this poses a number of challenges related to the frequency at which heterogeneous hardware resources might change their availability and shareability status, which in turn affect methods and means to allocate, manage, optimize, bill, monitor VMs, containers, virtual farms, jobs, interactive bare-metal sessions, etc. This work describes some of the use cases that prompted the design and construction of the HPC cluster, its architecture and resource provisioning model, along with a first characterization of its performance by some synthetic benchmark tools and a few realistic use-case tests.

IN13B-1660: Analytics and Visualization Pipelines for Big Data on the NASA Earth Exchange (NEX) and OpenNEX

NASA Technical Reports Server (NTRS)

Chaudhary, Aashish; Votava, Petr; Nemani, Ramakrishna R.; Michaelis, Andrew; Kotfila, Chris

2016-01-01

We are developing capabilities for an integrated petabyte-scale Earth science collaborative analysis and visualization environment. The ultimate goal is to deploy this environment within the NASA Earth Exchange (NEX) and OpenNEX in order to enhance existing science data production pipelines in both high-performance computing (HPC) and cloud environments. Bridging of HPC and cloud is a fairly new concept under active research and this system significantly enhances the ability of the scientific community to accelerate analysis and visualization of Earth science data from NASA missions, model outputs and other sources. We have developed a web-based system that seamlessly interfaces with both high-performance computing (HPC) and cloud environments, providing tools that enable science teams to develop and deploy large-scale analysis, visualization and QA pipelines of both the production process and the data products, and enable sharing results with the community. Our project is developed in several stages each addressing separate challenge - workflow integration, parallel execution in either cloud or HPC environments and big-data analytics or visualization. This work benefits a number of existing and upcoming projects supported by NEX, such as the Web Enabled Landsat Data (WELD), where we are developing a new QA pipeline for the 25PB system.

Analytics and Visualization Pipelines for Big ­Data on the NASA Earth Exchange (NEX) and OpenNEX

NASA Astrophysics Data System (ADS)

Chaudhary, A.; Votava, P.; Nemani, R. R.; Michaelis, A.; Kotfila, C.

2016-12-01

We are developing capabilities for an integrated petabyte-scale Earth science collaborative analysis and visualization environment. The ultimate goal is to deploy this environment within the NASA Earth Exchange (NEX) and OpenNEX in order to enhance existing science data production pipelines in both high-performance computing (HPC) and cloud environments. Bridging of HPC and cloud is a fairly new concept under active research and this system significantly enhances the ability of the scientific community to accelerate analysis and visualization of Earth science data from NASA missions, model outputs and other sources. We have developed a web-based system that seamlessly interfaces with both high-performance computing (HPC) and cloud environments, providing tools that enable science teams to develop and deploy large-scale analysis, visualization and QA pipelines of both the production process and the data products, and enable sharing results with the community. Our project is developed in several stages each addressing separate challenge - workflow integration, parallel execution in either cloud or HPC environments and big-data analytics or visualization. This work benefits a number of existing and upcoming projects supported by NEX, such as the Web Enabled Landsat Data (WELD), where we are developing a new QA pipeline for the 25PB system.

In Situ Methods, Infrastructures, and Applications on High Performance Computing Platforms, a State-of-the-art (STAR) Report

DOE PAGES

Bethel, EW; Bauer, A; Abbasi, H; ...

2016-06-10

The considerable interest in the high performance computing (HPC) community regarding analyzing and visualization data without first writing to disk, i.e., in situ processing, is due to several factors. First is an I/O cost savings, where data is analyzed /visualized while being generated, without first storing to a filesystem. Second is the potential for increased accuracy, where fine temporal sampling of transient analysis might expose some complex behavior missed in coarse temporal sampling. Third is the ability to use all available resources, CPU’s and accelerators, in the computation of analysis products. This STAR paper brings together researchers, developers and practitionersmore » using in situ methods in extreme-scale HPC with the goal to present existing methods, infrastructures, and a range of computational science and engineering applications using in situ analysis and visualization.« less

The Convergence of High Performance Computing and Large Scale Data Analytics

NASA Astrophysics Data System (ADS)

Duffy, D.; Bowen, M. K.; Thompson, J. H.; Yang, C. P.; Hu, F.; Wills, B.

2015-12-01

As the combinations of remote sensing observations and model outputs have grown, scientists are increasingly burdened with both the necessity and complexity of large-scale data analysis. Scientists are increasingly applying traditional high performance computing (HPC) solutions to solve their "Big Data" problems. While this approach has the benefit of limiting data movement, the HPC system is not optimized to run analytics, which can create problems that permeate throughout the HPC environment. To solve these issues and to alleviate some of the strain on the HPC environment, the NASA Center for Climate Simulation (NCCS) has created the Advanced Data Analytics Platform (ADAPT), which combines both HPC and cloud technologies to create an agile system designed for analytics. Large, commonly used data sets are stored in this system in a write once/read many file system, such as Landsat, MODIS, MERRA, and NGA. High performance virtual machines are deployed and scaled according to the individual scientist's requirements specifically for data analysis. On the software side, the NCCS and GMU are working with emerging commercial technologies and applying them to structured, binary scientific data in order to expose the data in new ways. Native NetCDF data is being stored within a Hadoop Distributed File System (HDFS) enabling storage-proximal processing through MapReduce while continuing to provide accessibility of the data to traditional applications. Once the data is stored within HDFS, an additional indexing scheme is built on top of the data and placed into a relational database. This spatiotemporal index enables extremely fast mappings of queries to data locations to dramatically speed up analytics. These are some of the first steps toward a single unified platform that optimizes for both HPC and large-scale data analysis, and this presentation will elucidate the resulting and necessary exascale architectures required for future systems.

Time-efficient simulations of tight-binding electronic structures with Intel Xeon PhiTM many-core processors

NASA Astrophysics Data System (ADS)

Ryu, Hoon; Jeong, Yosang; Kang, Ji-Hoon; Cho, Kyu Nam

2016-12-01

Modelling of multi-million atomic semiconductor structures is important as it not only predicts properties of physically realizable novel materials, but can accelerate advanced device designs. This work elaborates a new Technology-Computer-Aided-Design (TCAD) tool for nanoelectronics modelling, which uses a sp3d5s∗ tight-binding approach to describe multi-million atomic structures, and simulate electronic structures with high performance computing (HPC), including atomic effects such as alloy and dopant disorders. Being named as Quantum simulation tool for Advanced Nanoscale Devices (Q-AND), the tool shows nice scalability on traditional multi-core HPC clusters implying the strong capability of large-scale electronic structure simulations, particularly with remarkable performance enhancement on latest clusters of Intel Xeon PhiTM coprocessors. A review of the recent modelling study conducted to understand an experimental work of highly phosphorus-doped silicon nanowires, is presented to demonstrate the utility of Q-AND. Having been developed via Intel Parallel Computing Center project, Q-AND will be open to public to establish a sound framework of nanoelectronics modelling with advanced HPC clusters of a many-core base. With details of the development methodology and exemplary study of dopant electronics, this work will present a practical guideline for TCAD development to researchers in the field of computational nanoelectronics.

Appropriate Use Policy | High-Performance Computing | NREL

Science.gov Websites

users of the National Renewable Energy Laboratory (NREL) High Performance Computing (HPC) resources government agency, National Laboratory, University, or private entity, the intellectual property terms (if issued a multifactor token which may be a physical token or a virtual token used with one-time password

Exploring the capabilities of support vector machines in detecting silent data corruptions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Subasi, Omer; Di, Sheng; Bautista-Gomez, Leonardo

As the exascale era approaches, the increasing capacity of high-performance computing (HPC) systems with targeted power and energy budget goals introduces significant challenges in reliability. Silent data corruptions (SDCs), or silent errors, are one of the major sources that corrupt the execution results of HPC applications without being detected. Here in this paper, we explore a set of novel SDC detectors – by leveraging epsilon-insensitive support vector machine regression – to detect SDCs that occur in HPC applications. The key contributions are threefold. (1) Our exploration takes temporal, spatial, and spatiotemporal features into account and analyzes different detectors based onmore » different features. (2) We provide an in-depth study on the detection ability and performance with different parameters, and we optimize the detection range carefully. (3) Experiments with eight real-world HPC applications show that support-vector-machine-based detectors can achieve detection sensitivity (i.e., recall) up to 99% yet suffer a less than 1% false positive rate for most cases. Our detectors incur low performance overhead, 5% on average, for all benchmarks studied in this work.« less

Exploring the capabilities of support vector machines in detecting silent data corruptions

DOE PAGES

Subasi, Omer; Di, Sheng; Bautista-Gomez, Leonardo; ...

2018-02-01

As the exascale era approaches, the increasing capacity of high-performance computing (HPC) systems with targeted power and energy budget goals introduces significant challenges in reliability. Silent data corruptions (SDCs), or silent errors, are one of the major sources that corrupt the execution results of HPC applications without being detected. Here in this paper, we explore a set of novel SDC detectors – by leveraging epsilon-insensitive support vector machine regression – to detect SDCs that occur in HPC applications. The key contributions are threefold. (1) Our exploration takes temporal, spatial, and spatiotemporal features into account and analyzes different detectors based onmore » different features. (2) We provide an in-depth study on the detection ability and performance with different parameters, and we optimize the detection range carefully. (3) Experiments with eight real-world HPC applications show that support-vector-machine-based detectors can achieve detection sensitivity (i.e., recall) up to 99% yet suffer a less than 1% false positive rate for most cases. Our detectors incur low performance overhead, 5% on average, for all benchmarks studied in this work.« less

Allocating Tactical High-Performance Computer (HPC) Resources to Offloaded Computation in Battlefield Scenarios

DTIC Science & Technology

2013-12-01

authors present a Computing on Dissemination with predictable contacts ( pCoD ) algorithm, since it is impossible to reserve task execution time in advance...Computing While Charging DAG Directed Acyclic Graph 18 TTL Time-to-live pCoD Predictable contacts CoD Computing on Dissemination upCoD Unpredictable

VTK-m: Accelerating the Visualization Toolkit for Massively Threaded Architectures

DOE PAGES

Moreland, Kenneth; Sewell, Christopher; Usher, William; ...

2016-05-09

Here, one of the most critical challenges for high-performance computing (HPC) scientific visualization is execution on massively threaded processors. Of the many fundamental changes we are seeing in HPC systems, one of the most profound is a reliance on new processor types optimized for execution bandwidth over latency hiding. Our current production scientific visualization software is not designed for these new types of architectures. To address this issue, the VTK-m framework serves as a container for algorithms, provides flexible data representation, and simplifies the design of visualization algorithms on new and future computer architecture.

VTK-m: Accelerating the Visualization Toolkit for Massively Threaded Architectures

DOE PAGES

Moreland, Kenneth; Sewell, Christopher; Usher, William; ...

2016-05-09

Execution on massively threaded processors is one of the most critical challenges for high-performance computing (HPC) scientific visualization. Of the many fundamental changes we are seeing in HPC systems, one of the most profound is a reliance on new processor types optimized for execution bandwidth over latency hiding. Moreover, our current production scientific visualization software is not designed for these new types of architectures. In order to address this issue, the VTK-m framework serves as a container for algorithms, provides flexible data representation, and simplifies the design of visualization algorithms on new and future computer architecture.

CyberShake: Running Seismic Hazard Workflows on Distributed HPC Resources

NASA Astrophysics Data System (ADS)

Callaghan, S.; Maechling, P. J.; Graves, R. W.; Gill, D.; Olsen, K. B.; Milner, K. R.; Yu, J.; Jordan, T. H.

2013-12-01

As part of its program of earthquake system science research, the Southern California Earthquake Center (SCEC) has developed a simulation platform, CyberShake, to perform physics-based probabilistic seismic hazard analysis (PSHA) using 3D deterministic wave propagation simulations. CyberShake performs PSHA by simulating a tensor-valued wavefield of Strain Green Tensors, and then using seismic reciprocity to calculate synthetic seismograms for about 415,000 events per site of interest. These seismograms are processed to compute ground motion intensity measures, which are then combined with probabilities from an earthquake rupture forecast to produce a site-specific hazard curve. Seismic hazard curves for hundreds of sites in a region can be used to calculate a seismic hazard map, representing the seismic hazard for a region. We present a recently completed PHSA study in which we calculated four CyberShake seismic hazard maps for the Southern California area to compare how CyberShake hazard results are affected by different SGT computational codes (AWP-ODC and AWP-RWG) and different community velocity models (Community Velocity Model - SCEC (CVM-S4) v11.11 and Community Velocity Model - Harvard (CVM-H) v11.9). We present our approach to running workflow applications on distributed HPC resources, including systems without support for remote job submission. We show how our approach extends the benefits of scientific workflows, such as job and data management, to large-scale applications on Track 1 and Leadership class open-science HPC resources. We used our distributed workflow approach to perform CyberShake Study 13.4 on two new NSF open-science HPC computing resources, Blue Waters and Stampede, executing over 470 million tasks to calculate physics-based hazard curves for 286 locations in the Southern California region. For each location, we calculated seismic hazard curves with two different community velocity models and two different SGT codes, resulting in over 1100 hazard curves. We will report on the performance of this CyberShake study, four times larger than previous studies. Additionally, we will examine the challenges we face applying these workflow techniques to additional open-science HPC systems and discuss whether our workflow solutions continue to provide value to our large-scale PSHA calculations.

First experience with particle-in-cell plasma physics code on ARM-based HPC systems

NASA Astrophysics Data System (ADS)

Sáez, Xavier; Soba, Alejandro; Sánchez, Edilberto; Mantsinen, Mervi; Mateo, Sergi; Cela, José M.; Castejón, Francisco

2015-09-01

In this work, we will explore the feasibility of porting a Particle-in-cell code (EUTERPE) to an ARM multi-core platform from the Mont-Blanc project. The used prototype is based on a system-on-chip Samsung Exynos 5 with an integrated GPU. It is the first prototype that could be used for High-Performance Computing (HPC), since it supports double precision and parallel programming languages.

System Connection via SSH Gateway | High-Performance Computing | NREL

Science.gov Websites

;@peregrine.hpc.nrel.gov First time logging in? If this is the first time you've logged in with your new account, you will password. You will be prompted to enter it a second time, then you will be logged off. Just reconnect with your HPC password at any time, you can simply use the passwd command. Remote Users If you're connecting

Data Provenance Hybridization Supporting Extreme-Scale Scientific WorkflowApplications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Elsethagen, Todd O.; Stephan, Eric G.; Raju, Bibi

As high performance computing (HPC) infrastructures continue to grow in capability and complexity, so do the applications that they serve. HPC and distributed-area computing (DAC) (e.g. grid and cloud) users are looking increasingly toward workflow solutions to orchestrate their complex application coupling, pre- and post-processing needs To gain insight and a more quantitative understanding of a workflow’s performance our method includes not only the capture of traditional provenance information, but also the capture and integration of system environment metrics helping to give context and explanation for a workflow’s execution. In this paper, we describe IPPD’s provenance management solution (ProvEn) andmore » its hybrid data store combining both of these data provenance perspectives.« less

SAME4HPC: A Promising Approach in Building a Scalable and Mobile Environment for High-Performance Computing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Karthik, Rajasekar

2014-01-01

In this paper, an architecture for building Scalable And Mobile Environment For High-Performance Computing with spatial capabilities called SAME4HPC is described using cutting-edge technologies and standards such as Node.js, HTML5, ECMAScript 6, and PostgreSQL 9.4. Mobile devices are increasingly becoming powerful enough to run high-performance apps. At the same time, there exist a significant number of low-end and older devices that rely heavily on the server or the cloud infrastructure to do the heavy lifting. Our architecture aims to support both of these types of devices to provide high-performance and rich user experience. A cloud infrastructure consisting of OpenStack withmore » Ubuntu, GeoServer, and high-performance JavaScript frameworks are some of the key open-source and industry standard practices that has been adopted in this architecture.« less

Platform for Automated Real-Time High Performance Analytics on Medical Image Data.

PubMed

Allen, William J; Gabr, Refaat E; Tefera, Getaneh B; Pednekar, Amol S; Vaughn, Matthew W; Narayana, Ponnada A

2018-03-01

Biomedical data are quickly growing in volume and in variety, providing clinicians an opportunity for better clinical decision support. Here, we demonstrate a robust platform that uses software automation and high performance computing (HPC) resources to achieve real-time analytics of clinical data, specifically magnetic resonance imaging (MRI) data. We used the Agave application programming interface to facilitate communication, data transfer, and job control between an MRI scanner and an off-site HPC resource. In this use case, Agave executed the graphical pipeline tool GRAphical Pipeline Environment (GRAPE) to perform automated, real-time, quantitative analysis of MRI scans. Same-session image processing will open the door for adaptive scanning and real-time quality control, potentially accelerating the discovery of pathologies and minimizing patient callbacks. We envision this platform can be adapted to other medical instruments, HPC resources, and analytics tools.

High-Performance Computing Act of 1990: Report of the Senate Committee on Commerce, Science, and Transportation on S. 1067.

ERIC Educational Resources Information Center

Congress of the U.S., Washington, DC. Senate Committee on Commerce, Science, and Transportation.

This committee report is intended to accompany S. 1067, a bill designed to provide for a coordinated federal research program in high-performance computing (HPC). The primary objective of the legislation is given as the acceleration of research, development, and application of the most advanced computing technology in research, education, and…

Process for selecting NEAMS applications for access to Idaho National Laboratory high performance computing resources

DOE Office of Scientific and Technical Information (OSTI.GOV)

Michael Pernice

2010-09-01

INL has agreed to provide participants in the Nuclear Energy Advanced Mod- eling and Simulation (NEAMS) program with access to its high performance computing (HPC) resources under sponsorship of the Enabling Computational Technologies (ECT) program element. This report documents the process used to select applications and the software stack in place at INL.

Large-scale optimization-based non-negative computational framework for diffusion equations: Parallel implementation and performance studies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chang, Justin; Karra, Satish; Nakshatrala, Kalyana B.

It is well-known that the standard Galerkin formulation, which is often the formulation of choice under the finite element method for solving self-adjoint diffusion equations, does not meet maximum principles and the non-negative constraint for anisotropic diffusion equations. Recently, optimization-based methodologies that satisfy maximum principles and the non-negative constraint for steady-state and transient diffusion-type equations have been proposed. To date, these methodologies have been tested only on small-scale academic problems. The purpose of this paper is to systematically study the performance of the non-negative methodology in the context of high performance computing (HPC). PETSc and TAO libraries are, respectively, usedmore » for the parallel environment and optimization solvers. For large-scale problems, it is important for computational scientists to understand the computational performance of current algorithms available in these scientific libraries. The numerical experiments are conducted on the state-of-the-art HPC systems, and a single-core performance model is used to better characterize the efficiency of the solvers. Furthermore, our studies indicate that the proposed non-negative computational framework for diffusion-type equations exhibits excellent strong scaling for real-world large-scale problems.« less

Large-scale optimization-based non-negative computational framework for diffusion equations: Parallel implementation and performance studies

DOE PAGES

Chang, Justin; Karra, Satish; Nakshatrala, Kalyana B.

2016-07-26

It is well-known that the standard Galerkin formulation, which is often the formulation of choice under the finite element method for solving self-adjoint diffusion equations, does not meet maximum principles and the non-negative constraint for anisotropic diffusion equations. Recently, optimization-based methodologies that satisfy maximum principles and the non-negative constraint for steady-state and transient diffusion-type equations have been proposed. To date, these methodologies have been tested only on small-scale academic problems. The purpose of this paper is to systematically study the performance of the non-negative methodology in the context of high performance computing (HPC). PETSc and TAO libraries are, respectively, usedmore » for the parallel environment and optimization solvers. For large-scale problems, it is important for computational scientists to understand the computational performance of current algorithms available in these scientific libraries. The numerical experiments are conducted on the state-of-the-art HPC systems, and a single-core performance model is used to better characterize the efficiency of the solvers. Furthermore, our studies indicate that the proposed non-negative computational framework for diffusion-type equations exhibits excellent strong scaling for real-world large-scale problems.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fadika, Zacharia; Dede, Elif; Govindaraju, Madhusudhan

MapReduce is increasingly becoming a popular framework, and a potent programming model. The most popular open source implementation of MapReduce, Hadoop, is based on the Hadoop Distributed File System (HDFS). However, as HDFS is not POSIX compliant, it cannot be fully leveraged by applications running on a majority of existing HPC environments such as Teragrid and NERSC. These HPC environments typicallysupport globally shared file systems such as NFS and GPFS. On such resourceful HPC infrastructures, the use of Hadoop not only creates compatibility issues, but also affects overall performance due to the added overhead of the HDFS. This paper notmore » only presents a MapReduce implementation directly suitable for HPC environments, but also exposes the design choices for better performance gains in those settings. By leveraging inherent distributed file systems' functions, and abstracting them away from its MapReduce framework, MARIANE (MApReduce Implementation Adapted for HPC Environments) not only allows for the use of the model in an expanding number of HPCenvironments, but also allows for better performance in such settings. This paper shows the applicability and high performance of the MapReduce paradigm through MARIANE, an implementation designed for clustered and shared-disk file systems and as such not dedicated to a specific MapReduce solution. The paper identifies the components and trade-offs necessary for this model, and quantifies the performance gains exhibited by our approach in distributed environments over Apache Hadoop in a data intensive setting, on the Magellan testbed at the National Energy Research Scientific Computing Center (NERSC).« less

An efficient framework for Java data processing systems in HPC environments

NASA Astrophysics Data System (ADS)

Fries, Aidan; Castañeda, Javier; Isasi, Yago; Taboada, Guillermo L.; Portell de Mora, Jordi; Sirvent, Raül

2011-11-01

Java is a commonly used programming language, although its use in High Performance Computing (HPC) remains relatively low. One of the reasons is a lack of libraries offering specific HPC functions to Java applications. In this paper we present a Java-based framework, called DpcbTools, designed to provide a set of functions that fill this gap. It includes a set of efficient data communication functions based on message-passing, thus providing, when a low latency network such as Myrinet is available, higher throughputs and lower latencies than standard solutions used by Java. DpcbTools also includes routines for the launching, monitoring and management of Java applications on several computing nodes by making use of JMX to communicate with remote Java VMs. The Gaia Data Processing and Analysis Consortium (DPAC) is a real case where scientific data from the ESA Gaia astrometric satellite will be entirely processed using Java. In this paper we describe the main elements of DPAC and its usage of the DpcbTools framework. We also assess the usefulness and performance of DpcbTools through its performance evaluation and the analysis of its impact on some DPAC systems deployed in the MareNostrum supercomputer (Barcelona Supercomputing Center).

Exploiting Parallel R in the Cloud with SPRINT

PubMed Central

Piotrowski, M.; McGilvary, G.A.; Sloan, T. M.; Mewissen, M.; Lloyd, A.D.; Forster, T.; Mitchell, L.; Ghazal, P.; Hill, J.

2012-01-01

Background Advances in DNA Microarray devices and next-generation massively parallel DNA sequencing platforms have led to an exponential growth in data availability but the arising opportunities require adequate computing resources. High Performance Computing (HPC) in the Cloud offers an affordable way of meeting this need. Objectives Bioconductor, a popular tool for high-throughput genomic data analysis, is distributed as add-on modules for the R statistical programming language but R has no native capabilities for exploiting multi-processor architectures. SPRINT is an R package that enables easy access to HPC for genomics researchers. This paper investigates: setting up and running SPRINT-enabled genomic analyses on Amazon’s Elastic Compute Cloud (EC2), the advantages of submitting applications to EC2 from different parts of the world and, if resource underutilization can improve application performance. Methods The SPRINT parallel implementations of correlation, permutation testing, partitioning around medoids and the multi-purpose papply have been benchmarked on data sets of various size on Amazon EC2. Jobs have been submitted from both the UK and Thailand to investigate monetary differences. Results It is possible to obtain good, scalable performance but the level of improvement is dependent upon the nature of algorithm. Resource underutilization can further improve the time to result. End-user’s location impacts on costs due to factors such as local taxation. Conclusions: Although not designed to satisfy HPC requirements, Amazon EC2 and cloud computing in general provides an interesting alternative and provides new possibilities for smaller organisations with limited funds. PMID:23223611

Exploiting parallel R in the cloud with SPRINT.

PubMed

Piotrowski, M; McGilvary, G A; Sloan, T M; Mewissen, M; Lloyd, A D; Forster, T; Mitchell, L; Ghazal, P; Hill, J

2013-01-01

Advances in DNA Microarray devices and next-generation massively parallel DNA sequencing platforms have led to an exponential growth in data availability but the arising opportunities require adequate computing resources. High Performance Computing (HPC) in the Cloud offers an affordable way of meeting this need. Bioconductor, a popular tool for high-throughput genomic data analysis, is distributed as add-on modules for the R statistical programming language but R has no native capabilities for exploiting multi-processor architectures. SPRINT is an R package that enables easy access to HPC for genomics researchers. This paper investigates: setting up and running SPRINT-enabled genomic analyses on Amazon's Elastic Compute Cloud (EC2), the advantages of submitting applications to EC2 from different parts of the world and, if resource underutilization can improve application performance. The SPRINT parallel implementations of correlation, permutation testing, partitioning around medoids and the multi-purpose papply have been benchmarked on data sets of various size on Amazon EC2. Jobs have been submitted from both the UK and Thailand to investigate monetary differences. It is possible to obtain good, scalable performance but the level of improvement is dependent upon the nature of the algorithm. Resource underutilization can further improve the time to result. End-user's location impacts on costs due to factors such as local taxation. Although not designed to satisfy HPC requirements, Amazon EC2 and cloud computing in general provides an interesting alternative and provides new possibilities for smaller organisations with limited funds.

[Design and study of parallel computing environment of Monte Carlo simulation for particle therapy planning using a public cloud-computing infrastructure].

PubMed

Yokohama, Noriya

2013-07-01

This report was aimed at structuring the design of architectures and studying performance measurement of a parallel computing environment using a Monte Carlo simulation for particle therapy using a high performance computing (HPC) instance within a public cloud-computing infrastructure. Performance measurements showed an approximately 28 times faster speed than seen with single-thread architecture, combined with improved stability. A study of methods of optimizing the system operations also indicated lower cost.

HPC Software Stack Testing Framework

DOE Office of Scientific and Technical Information (OSTI.GOV)

Garvey, Cormac

The HPC Software stack testing framework (hpcswtest) is used in the INL Scientific Computing Department to test the basic sanity and integrity of the HPC Software stack (Compilers, MPI, Numerical libraries and Applications) and to quickly discover hard failures, and as a by-product it will indirectly check the HPC infrastructure (network, PBS and licensing servers).

RELIABILITY, AVAILABILITY, AND SERVICEABILITY FOR PETASCALE HIGH-END COMPUTING AND BEYOND

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chokchai "Box" Leangsuksun

2011-05-31

Our project is a multi-institutional research effort that adopts interplay of RELIABILITY, AVAILABILITY, and SERVICEABILITY (RAS) aspects for solving resilience issues in highend scientific computing in the next generation of supercomputers. results lie in the following tracks: Failure prediction in a large scale HPC; Investigate reliability issues and mitigation techniques including in GPGPU-based HPC system; HPC resilience runtime & tools.

DOE Office of Scientific and Technical Information (OSTI.GOV)

East, D. R.; Sexton, J.

This was a collaborative effort between Lawrence Livermore National Security, LLC as manager and operator of Lawrence Livermore National Laboratory (LLNL) and IBM TJ Watson Research Center to research, assess feasibility and develop an implementation plan for a High Performance Computing Innovation Center (HPCIC) in the Livermore Valley Open Campus (LVOC). The ultimate goal of this work was to help advance the State of California and U.S. commercial competitiveness in the arena of High Performance Computing (HPC) by accelerating the adoption of computational science solutions, consistent with recent DOE strategy directives. The desired result of this CRADA was a well-researched,more » carefully analyzed market evaluation that would identify those firms in core sectors of the US economy seeking to adopt or expand their use of HPC to become more competitive globally, and to define how those firms could be helped by the HPCIC with IBM as an integral partner.« less

Playable Serious Games for Studying and Programming Computational STEM and Informatics Applications of Distributed and Parallel Computer Architectures

ERIC Educational Resources Information Center

Amenyo, John-Thones

2012-01-01

Carefully engineered playable games can serve as vehicles for students and practitioners to learn and explore the programming of advanced computer architectures to execute applications, such as high performance computing (HPC) and complex, inter-networked, distributed systems. The article presents families of playable games that are grounded in…

Self-service for software development projects and HPC activities

NASA Astrophysics Data System (ADS)

Husejko, M.; Høimyr, N.; Gonzalez, A.; Koloventzos, G.; Asbury, D.; Trzcinska, A.; Agtzidis, I.; Botrel, G.; Otto, J.

2014-05-01

This contribution describes how CERN has implemented several essential tools for agile software development processes, ranging from version control (Git) to issue tracking (Jira) and documentation (Wikis). Running such services in a large organisation like CERN requires many administrative actions both by users and service providers, such as creating software projects, managing access rights, users and groups, and performing tool-specific customisation. Dealing with these requests manually would be a time-consuming task. Another area of our CERN computing services that has required dedicated manual support has been clusters for specific user communities with special needs. Our aim is to move all our services to a layered approach, with server infrastructure running on the internal cloud computing infrastructure at CERN. This contribution illustrates how we plan to optimise the management of our of services by means of an end-user facing platform acting as a portal into all the related services for software projects, inspired by popular portals for open-source developments such as Sourceforge, GitHub and others. Furthermore, the contribution will discuss recent activities with tests and evaluations of High Performance Computing (HPC) applications on different hardware and software stacks, and plans to offer a dynamically scalable HPC service at CERN, based on affordable hardware.

PuTTY | High-Performance Computing | NREL

Science.gov Websites

PuTTY PuTTY Learn how to use PuTTY to connect to NREL's high-performance computing (HPC) systems . Connecting When you start the PuTTY app, the program will display PuTTY's Configuration menu. When this comes . When prompted, type your password again followed by . Note: to increase

Evolution of Embedded Processing for Wide Area Surveillance

DTIC Science & Technology

2014-01-01

future vision . 15. SUBJECT TERMS Embedded processing; high performance computing; general-purpose graphical processing units (GPGPUs) 16. SECURITY...recon- naissance (ISR) mission capabilities. The capabilities these advancements are achieving include the ability to provide persistent all...fighters to support and positively affect their mission . Significant improvements in high-performance computing (HPC) technology make it possible to

The Use of High Performance Computing (HPC) to Strengthen the Development of Army Systems

DTIC Science & Technology

2011-11-01

accurately predicting the supersonic magus effect about spinning cones, ogive- cylinders , and boat-tailed afterbodies. This work led to the successful...successful computer model of the proposed product or system, one can then build prototypes on the computer and study the effects on the performance of...needed. The NRC report discusses the requirements for effective use of such computing power. One needs “models, algorithms, software, hardware

Performance of hybrid programming models for multiscale cardiac simulations: preparing for petascale computation.

PubMed

Pope, Bernard J; Fitch, Blake G; Pitman, Michael C; Rice, John J; Reumann, Matthias

2011-10-01

Future multiscale and multiphysics models that support research into human disease, translational medical science, and treatment can utilize the power of high-performance computing (HPC) systems. We anticipate that computationally efficient multiscale models will require the use of sophisticated hybrid programming models, mixing distributed message-passing processes [e.g., the message-passing interface (MPI)] with multithreading (e.g., OpenMP, Pthreads). The objective of this study is to compare the performance of such hybrid programming models when applied to the simulation of a realistic physiological multiscale model of the heart. Our results show that the hybrid models perform favorably when compared to an implementation using only the MPI and, furthermore, that OpenMP in combination with the MPI provides a satisfactory compromise between performance and code complexity. Having the ability to use threads within MPI processes enables the sophisticated use of all processor cores for both computation and communication phases. Considering that HPC systems in 2012 will have two orders of magnitude more cores than what was used in this study, we believe that faster than real-time multiscale cardiac simulations can be achieved on these systems.

The NCI High Performance Computing (HPC) and High Performance Data (HPD) Platform to Support the Analysis of Petascale Environmental Data Collections

NASA Astrophysics Data System (ADS)

Evans, B. J. K.; Pugh, T.; Wyborn, L. A.; Porter, D.; Allen, C.; Smillie, J.; Antony, J.; Trenham, C.; Evans, B. J.; Beckett, D.; Erwin, T.; King, E.; Hodge, J.; Woodcock, R.; Fraser, R.; Lescinsky, D. T.

2014-12-01

The National Computational Infrastructure (NCI) has co-located a priority set of national data assets within a HPC research platform. This powerful in-situ computational platform has been created to help serve and analyse the massive amounts of data across the spectrum of environmental collections - in particular the climate, observational data and geoscientific domains. This paper examines the infrastructure, innovation and opportunity for this significant research platform. NCI currently manages nationally significant data collections (10+ PB) categorised as 1) earth system sciences, climate and weather model data assets and products, 2) earth and marine observations and products, 3) geosciences, 4) terrestrial ecosystem, 5) water management and hydrology, and 6) astronomy, social science and biosciences. The data is largely sourced from the NCI partners (who include the custodians of many of the national scientific records), major research communities, and collaborating overseas organisations. By co-locating these large valuable data assets, new opportunities have arisen by harmonising the data collections, making a powerful transdisciplinary research platformThe data is accessible within an integrated HPC-HPD environment - a 1.2 PFlop supercomputer (Raijin), a HPC class 3000 core OpenStack cloud system and several highly connected large scale and high-bandwidth Lustre filesystems. New scientific software, cloud-scale techniques, server-side visualisation and data services have been harnessed and integrated into the platform, so that analysis is performed seamlessly across the traditional boundaries of the underlying data domains. Characterisation of the techniques along with performance profiling ensures scalability of each software component, all of which can either be enhanced or replaced through future improvements. A Development-to-Operations (DevOps) framework has also been implemented to manage the scale of the software complexity alone. This ensures that software is both upgradable and maintainable, and can be readily reused with complexly integrated systems and become part of the growing global trusted community tools for cross-disciplinary research.

Coupled hydro-meteorological modelling on a HPC platform for high-resolution extreme weather impact study

NASA Astrophysics Data System (ADS)

Zhu, Dehua; Echendu, Shirley; Xuan, Yunqing; Webster, Mike; Cluckie, Ian

2016-11-01

Impact-focused studies of extreme weather require coupling of accurate simulations of weather and climate systems and impact-measuring hydrological models which themselves demand larger computer resources. In this paper, we present a preliminary analysis of a high-performance computing (HPC)-based hydrological modelling approach, which is aimed at utilizing and maximizing HPC power resources, to support the study on extreme weather impact due to climate change. Here, four case studies are presented through implementation on the HPC Wales platform of the UK mesoscale meteorological Unified Model (UM) with high-resolution simulation suite UKV, alongside a Linux-based hydrological model, Hydrological Predictions for the Environment (HYPE). The results of this study suggest that the coupled hydro-meteorological model was still able to capture the major flood peaks, compared with the conventional gauge- or radar-driving forecast, but with the added value of much extended forecast lead time. The high-resolution rainfall estimation produced by the UKV performs similarly to that of radar rainfall products in the first 2-3 days of tested flood events, but the uncertainties particularly increased as the forecast horizon goes beyond 3 days. This study takes a step forward to identify how the online mode approach can be used, where both numerical weather prediction and the hydrological model are executed, either simultaneously or on the same hardware infrastructures, so that more effective interaction and communication can be achieved and maintained between the models. But the concluding comments are that running the entire system on a reasonably powerful HPC platform does not yet allow for real-time simulations, even without the most complex and demanding data simulation part.

GPU Implementation of High Rayleigh Number Three-Dimensional Mantle Convection

NASA Astrophysics Data System (ADS)

Sanchez, D. A.; Yuen, D. A.; Wright, G. B.; Barnett, G. A.

2010-12-01

Although we have entered the age of petascale computing, many factors are still prohibiting high-performance computing (HPC) from infiltrating all suitable scientific disciplines. For this reason and others, application of GPU to HPC is gaining traction in the scientific world. With its low price point, high performance potential, and competitive scalability, GPU has been an option well worth considering for the last few years. Moreover with the advent of NVIDIA's Fermi architecture, which brings ECC memory, better double-precision performance, and more RAM to GPU, there is a strong message of corporate support for GPU in HPC. However many doubts linger concerning the practicality of using GPU for scientific computing. In particular, GPU has a reputation for being difficult to program and suitable for only a small subset of problems. Although inroads have been made in addressing these concerns, for many scientists GPU still has hurdles to clear before becoming an acceptable choice. We explore the applicability of GPU to geophysics by implementing a three-dimensional, second-order finite-difference model of Rayleigh-Benard thermal convection on an NVIDIA GPU using C for CUDA. Our code reaches sufficient resolution, on the order of 500x500x250 evenly-spaced finite-difference gridpoints, on a single GPU. We make extensive use of highly optimized CUBLAS routines, allowing us to achieve performance on the order of O( 0.1 ) µs per timestep*gridpoint at this resolution. This performance has allowed us to study high Rayleigh number simulations, on the order of 2x10^7, on a single GPU.

A Long History of Supercomputing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Grider, Gary

As part of its national security science mission, Los Alamos National Laboratory and HPC have a long, entwined history dating back to the earliest days of computing. From bringing the first problem to the nation’s first computer to building the first machine to break the petaflop barrier, Los Alamos holds many “firsts” in HPC breakthroughs. Today, supercomputers are integral to stockpile stewardship and the Laboratory continues to work with vendors in developing the future of HPC.

Advanced Biomedical Computing Center (ABCC) | DSITP

Cancer.gov

The Advanced Biomedical Computing Center (ABCC), located in Frederick Maryland (MD), provides HPC resources for both NIH/NCI intramural scientists and the extramural biomedical research community. Its mission is to provide HPC support, to provide collaborative research, and to conduct in-house research in various areas of computational biology and biomedical research.

Unified Performance and Power Modeling of Scientific Workloads

DOE Office of Scientific and Technical Information (OSTI.GOV)

Song, Shuaiwen; Barker, Kevin J.; Kerbyson, Darren J.

2013-11-17

It is expected that scientific applications executing on future large-scale HPC must be optimized not only in terms of performance, but also in terms of power consumption. As power and energy become increasingly constrained resources, researchers and developers must have access to tools that will allow for accurate prediction of both performance and power consumption. Reasoning about performance and power consumption in concert will be critical for achieving maximum utilization of limited resources on future HPC systems. To this end, we present a unified performance and power model for the Nek-Bone mini-application developed as part of the DOE's CESAR Exascalemore » Co-Design Center. Our models consider the impact of computation, point-to-point communication, and collective communication« less

Role of HPC in Advancing Computational Aeroelasticity

NASA Technical Reports Server (NTRS)

Guruswamy, Guru P.

2004-01-01

On behalf of the High Performance Computing and Modernization Program (HPCMP) and NASA Advanced Supercomputing Division (NAS) a study is conducted to assess the role of supercomputers on computational aeroelasticity of aerospace vehicles. The study is mostly based on the responses to a web based questionnaire that was designed to capture the nuances of high performance computational aeroelasticity, particularly on parallel computers. A procedure is presented to assign a fidelity-complexity index to each application. Case studies based on major applications using HPCMP resources are presented.

Final Report Extreme Computing and U.S. Competitiveness DOE Award. DE-FG02-11ER26087/DE-SC0008764

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mustain, Christopher J.

The Council has acted on each of the grant deliverables during the funding period. The deliverables are: (1) convening the Council’s High Performance Computing Advisory Committee (HPCAC) on a bi-annual basis; (2) broadening public awareness of high performance computing (HPC) and exascale developments; (3) assessing the industrial applications of extreme computing; and (4) establishing a policy and business case for an exascale economy.

A Framework for Debugging Geoscience Projects in a High Performance Computing Environment

NASA Astrophysics Data System (ADS)

Baxter, C.; Matott, L.

2012-12-01

High performance computing (HPC) infrastructure has become ubiquitous in today's world with the emergence of commercial cloud computing and academic supercomputing centers. Teams of geoscientists, hydrologists and engineers can take advantage of this infrastructure to undertake large research projects - for example, linking one or more site-specific environmental models with soft computing algorithms, such as heuristic global search procedures, to perform parameter estimation and predictive uncertainty analysis, and/or design least-cost remediation systems. However, the size, complexity and distributed nature of these projects can make identifying failures in the associated numerical experiments using conventional ad-hoc approaches both time- consuming and ineffective. To address these problems a multi-tiered debugging framework has been developed. The framework allows for quickly isolating and remedying a number of potential experimental failures, including: failures in the HPC scheduler; bugs in the soft computing code; bugs in the modeling code; and permissions and access control errors. The utility of the framework is demonstrated via application to a series of over 200,000 numerical experiments involving a suite of 5 heuristic global search algorithms and 15 mathematical test functions serving as cheap analogues for the simulation-based optimization of pump-and-treat subsurface remediation systems.

The Design of a High Performance Earth Imagery and Raster Data Management and Processing Platform

NASA Astrophysics Data System (ADS)

Xie, Qingyun

2016-06-01

This paper summarizes the general requirements and specific characteristics of both geospatial raster database management system and raster data processing platform from a domain-specific perspective as well as from a computing point of view. It also discusses the need of tight integration between the database system and the processing system. These requirements resulted in Oracle Spatial GeoRaster, a global scale and high performance earth imagery and raster data management and processing platform. The rationale, design, implementation, and benefits of Oracle Spatial GeoRaster are described. Basically, as a database management system, GeoRaster defines an integrated raster data model, supports image compression, data manipulation, general and spatial indices, content and context based queries and updates, versioning, concurrency, security, replication, standby, backup and recovery, multitenancy, and ETL. It provides high scalability using computer and storage clustering. As a raster data processing platform, GeoRaster provides basic operations, image processing, raster analytics, and data distribution featuring high performance computing (HPC). Specifically, HPC features include locality computing, concurrent processing, parallel processing, and in-memory computing. In addition, the APIs and the plug-in architecture are discussed.

Resilience Design Patterns - A Structured Approach to Resilience at Extreme Scale (version 1.0)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hukerikar, Saurabh; Engelmann, Christian

Reliability is a serious concern for future extreme-scale high-performance computing (HPC) systems. Projections based on the current generation of HPC systems and technology roadmaps suggest that very high fault rates in future systems. The errors resulting from these faults will propagate and generate various kinds of failures, which may result in outcomes ranging from result corruptions to catastrophic application crashes. Practical limits on power consumption in HPC systems will require future systems to embrace innovative architectures, increasing the levels of hardware and software complexities. The resilience challenge for extreme-scale HPC systems requires management of various hardware and software technologies thatmore » are capable of handling a broad set of fault models at accelerated fault rates. These techniques must seek to improve resilience at reasonable overheads to power consumption and performance. While the HPC community has developed various solutions, application-level as well as system-based solutions, the solution space of HPC resilience techniques remains fragmented. There are no formal methods and metrics to investigate and evaluate resilience holistically in HPC systems that consider impact scope, handling coverage, and performance & power eciency across the system stack. Additionally, few of the current approaches are portable to newer architectures and software ecosystems, which are expected to be deployed on future systems. In this document, we develop a structured approach to the management of HPC resilience based on the concept of resilience-based design patterns. A design pattern is a general repeatable solution to a commonly occurring problem. We identify the commonly occurring problems and solutions used to deal with faults, errors and failures in HPC systems. The catalog of resilience design patterns provides designers with reusable design elements. We define a design framework that enhances our understanding of the important constraints and opportunities for solutions deployed at various layers of the system stack. The framework may be used to establish mechanisms and interfaces to coordinate flexible fault management across hardware and software components. The framework also enables optimization of the cost-benefit trade-os among performance, resilience, and power consumption. The overall goal of this work is to enable a systematic methodology for the design and evaluation of resilience technologies in extreme-scale HPC systems that keep scientific applications running to a correct solution in a timely and cost-ecient manner in spite of frequent faults, errors, and failures of various types.« less

2011 Computation Directorate Annual Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Crawford, D L

2012-04-11

From its founding in 1952 until today, Lawrence Livermore National Laboratory (LLNL) has made significant strategic investments to develop high performance computing (HPC) and its application to national security and basic science. Now, 60 years later, the Computation Directorate and its myriad resources and capabilities have become a key enabler for LLNL programs and an integral part of the effort to support our nation's nuclear deterrent and, more broadly, national security. In addition, the technological innovation HPC makes possible is seen as vital to the nation's economic vitality. LLNL, along with other national laboratories, is working to make supercomputing capabilitiesmore » and expertise available to industry to boost the nation's global competitiveness. LLNL is on the brink of an exciting milestone with the 2012 deployment of Sequoia, the National Nuclear Security Administration's (NNSA's) 20-petaFLOP/s resource that will apply uncertainty quantification to weapons science. Sequoia will bring LLNL's total computing power to more than 23 petaFLOP/s-all brought to bear on basic science and national security needs. The computing systems at LLNL provide game-changing capabilities. Sequoia and other next-generation platforms will enable predictive simulation in the coming decade and leverage industry trends, such as massively parallel and multicore processors, to run petascale applications. Efficient petascale computing necessitates refining accuracy in materials property data, improving models for known physical processes, identifying and then modeling for missing physics, quantifying uncertainty, and enhancing the performance of complex models and algorithms in macroscale simulation codes. Nearly 15 years ago, NNSA's Accelerated Strategic Computing Initiative (ASCI), now called the Advanced Simulation and Computing (ASC) Program, was the critical element needed to shift from test-based confidence to science-based confidence. Specifically, ASCI/ASC accelerated the development of simulation capabilities necessary to ensure confidence in the nuclear stockpile-far exceeding what might have been achieved in the absence of a focused initiative. While stockpile stewardship research pushed LLNL scientists to develop new computer codes, better simulation methods, and improved visualization technologies, this work also stimulated the exploration of HPC applications beyond the standard sponsor base. As LLNL advances to a petascale platform and pursues exascale computing (1,000 times faster than Sequoia), ASC will be paramount to achieving predictive simulation and uncertainty quantification. Predictive simulation and quantifying the uncertainty of numerical predictions where little-to-no data exists demands exascale computing and represents an expanding area of scientific research important not only to nuclear weapons, but to nuclear attribution, nuclear reactor design, and understanding global climate issues, among other fields. Aside from these lofty goals and challenges, computing at LLNL is anything but 'business as usual.' International competition in supercomputing is nothing new, but the HPC community is now operating in an expanded, more aggressive climate of global competitiveness. More countries understand how science and technology research and development are inextricably linked to economic prosperity, and they are aggressively pursuing ways to integrate HPC technologies into their native industrial and consumer products. In the interest of the nation's economic security and the science and technology that underpins it, LLNL is expanding its portfolio and forging new collaborations. We must ensure that HPC remains an asymmetric engine of innovation for the Laboratory and for the U.S. and, in doing so, protect our research and development dynamism and the prosperity it makes possible. One untapped area of opportunity LLNL is pursuing is to help U.S. industry understand how supercomputing can benefit their business. Industrial investment in HPC applications has historically been limited by the prohibitive cost of entry, the inaccessibility of software to run the powerful systems, and the years it takes to grow the expertise to develop codes and run them in an optimal way. LLNL is helping industry better compete in the global market place by providing access to some of the world's most powerful computing systems, the tools to run them, and the experts who are adept at using them. Our scientists are collaborating side by side with industrial partners to develop solutions to some of industry's toughest problems. The goal of the Livermore Valley Open Campus High Performance Computing Innovation Center is to allow American industry the opportunity to harness the power of supercomputing by leveraging the scientific and computational expertise at LLNL in order to gain a competitive advantage in the global economy.« less

KITTEN Lightweight Kernel 0.1 Beta

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pedretti, Kevin; Levenhagen, Michael; Kelly, Suzanne

2007-12-12

The Kitten Lightweight Kernel is a simplified OS (operating system) kernel that is intended to manage a compute node's hardware resources. It provides a set of mechanisms to user-level applications for utilizing hardware resources (e.g., allocating memory, creating processes, accessing the network). Kitten is much simpler than general-purpose OS kernels, such as Linux or Windows, but includes all of the esssential functionality needed to support HPC (high-performance computing) MPI, PGAS and OpenMP applications. Kitten provides unique capabilities such as physically contiguous application memory, transparent large page support, and noise-free tick-less operation, which enable HPC applications to obtain greater efficiency andmore » scalability than with general purpose OS kernels.« less

Allocation Usage Tracking and Management | High-Performance Computing |

Science.gov Websites

NREL's high-performance computing (HPC) systems, learn how to track and manage your allocations. The alloc_tracker script (/usr/local/bin/alloc_tracker) may be used to see what allocations you have access to, how much of the allocation has been used, how much remains and how many node hours will be forfeited at the

Using Formal Grammars to Predict I/O Behaviors in HPC: The Omnisc'IO Approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dorier, Matthieu; Ibrahim, Shadi; Antoniu, Gabriel

2016-08-01

The increasing gap between the computation performance of post-petascale machines and the performance of their I/O subsystem has motivated many I/O optimizations including prefetching, caching, and scheduling. In order to further improve these techniques, modeling and predicting spatial and temporal I/O patterns of HPC applications as they run has become crucial. In this paper we present Omnisc'IO, an approach that builds a grammar-based model of the I/O behavior of HPC applications and uses it to predict when future I/O operations will occur, and where and how much data will be accessed. To infer grammars, Omnisc'IO is based on StarSequitur, amore » novel algorithm extending Nevill-Manning's Sequitur algorithm. Omnisc'IO is transparently integrated into the POSIX and MPI I/O stacks and does not require any modification in applications or higher-level I/O libraries. It works without any prior knowledge of the application and converges to accurate predictions of any N future I/O operations within a couple of iterations. Its implementation is efficient in both computation time and memory footprint.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Duro, Francisco Rodrigo; Blas, Javier Garcia; Isaila, Florin

The increasing volume of scientific data and the limited scalability and performance of storage systems are currently presenting a significant limitation for the productivity of the scientific workflows running on both high-performance computing (HPC) and cloud platforms. Clearly needed is better integration of storage systems and workflow engines to address this problem. This paper presents and evaluates a novel solution that leverages codesign principles for integrating Hercules—an in-memory data store—with a workflow management system. We consider four main aspects: workflow representation, task scheduling, task placement, and task termination. As a result, the experimental evaluation on both cloud and HPC systemsmore » demonstrates significant performance and scalability improvements over existing state-of-the-art approaches.« less

Bioinformatics and Astrophysics Cluster (BinAc)

NASA Astrophysics Data System (ADS)

Krüger, Jens; Lutz, Volker; Bartusch, Felix; Dilling, Werner; Gorska, Anna; Schäfer, Christoph; Walter, Thomas

2017-09-01

BinAC provides central high performance computing capacities for bioinformaticians and astrophysicists from the state of Baden-Württemberg. The bwForCluster BinAC is part of the implementation concept for scientific computing for the universities in Baden-Württemberg. Community specific support is offered through the bwHPC-C5 project.

Computing Platforms for Big Biological Data Analytics: Perspectives and Challenges.

PubMed

Yin, Zekun; Lan, Haidong; Tan, Guangming; Lu, Mian; Vasilakos, Athanasios V; Liu, Weiguo

2017-01-01

The last decade has witnessed an explosion in the amount of available biological sequence data, due to the rapid progress of high-throughput sequencing projects. However, the biological data amount is becoming so great that traditional data analysis platforms and methods can no longer meet the need to rapidly perform data analysis tasks in life sciences. As a result, both biologists and computer scientists are facing the challenge of gaining a profound insight into the deepest biological functions from big biological data. This in turn requires massive computational resources. Therefore, high performance computing (HPC) platforms are highly needed as well as efficient and scalable algorithms that can take advantage of these platforms. In this paper, we survey the state-of-the-art HPC platforms for big biological data analytics. We first list the characteristics of big biological data and popular computing platforms. Then we provide a taxonomy of different biological data analysis applications and a survey of the way they have been mapped onto various computing platforms. After that, we present a case study to compare the efficiency of different computing platforms for handling the classical biological sequence alignment problem. At last we discuss the open issues in big biological data analytics.

Develop feedback system for intelligent dynamic resource allocation to improve application performance.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gentile, Ann C.; Brandt, James M.; Tucker, Thomas

2011-09-01

This report provides documentation for the completion of the Sandia Level II milestone 'Develop feedback system for intelligent dynamic resource allocation to improve application performance'. This milestone demonstrates the use of a scalable data collection analysis and feedback system that enables insight into how an application is utilizing the hardware resources of a high performance computing (HPC) platform in a lightweight fashion. Further we demonstrate utilizing the same mechanisms used for transporting data for remote analysis and visualization to provide low latency run-time feedback to applications. The ultimate goal of this body of work is performance optimization in the facemore » of the ever increasing size and complexity of HPC systems.« less

Template Interfaces for Agile Parallel Data-Intensive Science

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ramakrishnan, Lavanya; Gunter, Daniel; Pastorello, Gilerto Z.

Tigres provides a programming library to compose and execute large-scale data-intensive scientific workflows from desktops to supercomputers. DOE User Facilities and large science collaborations are increasingly generating large enough data sets that it is no longer practical to download them to a desktop to operate on them. They are instead stored at centralized compute and storage resources such as high performance computing (HPC) centers. Analysis of this data requires an ability to run on these facilities, but with current technologies, scaling an analysis to an HPC center and to a large data set is difficult even for experts. Tigres ismore » addressing the challenge of enabling collaborative analysis of DOE Science data through a new concept of reusable "templates" that enable scientists to easily compose, run and manage collaborative computational tasks. These templates define common computation patterns used in analyzing a data set.« less

Computational Environments and Analysis methods available on the NCI High Performance Computing (HPC) and High Performance Data (HPD) Platform

NASA Astrophysics Data System (ADS)

Evans, B. J. K.; Foster, C.; Minchin, S. A.; Pugh, T.; Lewis, A.; Wyborn, L. A.; Evans, B. J.; Uhlherr, A.

2014-12-01

The National Computational Infrastructure (NCI) has established a powerful in-situ computational environment to enable both high performance computing and data-intensive science across a wide spectrum of national environmental data collections - in particular climate, observational data and geoscientific assets. This paper examines 1) the computational environments that supports the modelling and data processing pipelines, 2) the analysis environments and methods to support data analysis, and 3) the progress in addressing harmonisation of the underlying data collections for future transdisciplinary research that enable accurate climate projections. NCI makes available 10+ PB major data collections from both the government and research sectors based on six themes: 1) weather, climate, and earth system science model simulations, 2) marine and earth observations, 3) geosciences, 4) terrestrial ecosystems, 5) water and hydrology, and 6) astronomy, social and biosciences. Collectively they span the lithosphere, crust, biosphere, hydrosphere, troposphere, and stratosphere. The data is largely sourced from NCI's partners (which include the custodians of many of the national scientific records), major research communities, and collaborating overseas organisations. The data is accessible within an integrated HPC-HPD environment - a 1.2 PFlop supercomputer (Raijin), a HPC class 3000 core OpenStack cloud system and several highly connected large scale and high-bandwidth Lustre filesystems. This computational environment supports a catalogue of integrated reusable software and workflows from earth system and ecosystem modelling, weather research, satellite and other observed data processing and analysis. To enable transdisciplinary research on this scale, data needs to be harmonised so that researchers can readily apply techniques and software across the corpus of data available and not be constrained to work within artificial disciplinary boundaries. Future challenges will involve the further integration and analysis of this data across the social sciences to facilitate the impacts across the societal domain, including timely analysis to more accurately predict and forecast future climate and environmental state.

Climate Science Performance, Data and Productivity on Titan

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mayer, Benjamin W; Worley, Patrick H; Gaddis, Abigail L

2015-01-01

Climate Science models are flagship codes for the largest of high performance computing (HPC) resources, both in visibility, with the newly launched Department of Energy (DOE) Accelerated Climate Model for Energy (ACME) effort, and in terms of significant fractions of system usage. The performance of the DOE ACME model is captured with application level timers and examined through a sizeable run archive. Performance and variability of compute, queue time and ancillary services are examined. As Climate Science advances in the use of HPC resources there has been an increase in the required human and data systems to achieve programs goals.more » A description of current workflow processes (hardware, software, human) and planned automation of the workflow, along with historical and projected data in motion and at rest data usage, are detailed. The combination of these two topics motivates a description of future systems requirements for DOE Climate Modeling efforts, focusing on the growth of data storage and network and disk bandwidth required to handle data at an acceptable rate.« less

A Long History of Supercomputing

ScienceCinema

Grider, Gary

2018-06-13

As part of its national security science mission, Los Alamos National Laboratory and HPC have a long, entwined history dating back to the earliest days of computing. From bringing the first problem to the nation’s first computer to building the first machine to break the petaflop barrier, Los Alamos holds many “firsts” in HPC breakthroughs. Today, supercomputers are integral to stockpile stewardship and the Laboratory continues to work with vendors in developing the future of HPC.

Use of Massive Parallel Computing Libraries in the Context of Global Gravity Field Determination from Satellite Data

NASA Astrophysics Data System (ADS)

Brockmann, J. M.; Schuh, W.-D.

2011-07-01

The estimation of the global Earth's gravity field parametrized as a finite spherical harmonic series is computationally demanding. The computational effort depends on the one hand on the maximal resolution of the spherical harmonic expansion (i.e. the number of parameters to be estimated) and on the other hand on the number of observations (which are several millions for e.g. observations from the GOCE satellite missions). To circumvent these restrictions, a massive parallel software based on high-performance computing (HPC) libraries as ScaLAPACK, PBLAS and BLACS was designed in the context of GOCE HPF WP6000 and the GOCO consortium. A prerequisite for the use of these libraries is that all matrices are block-cyclic distributed on a processor grid comprised by a large number of (distributed memory) computers. Using this set of standard HPC libraries has the benefit that once the matrices are distributed across the computer cluster, a huge set of efficient and highly scalable linear algebra operations can be used.

Opportunities for nonvolatile memory systems in extreme-scale high-performance computing

DOE PAGES

Vetter, Jeffrey S.; Mittal, Sparsh

2015-01-12

For extreme-scale high-performance computing systems, system-wide power consumption has been identified as one of the key constraints moving forward, where DRAM main memory systems account for about 30 to 50 percent of a node's overall power consumption. As the benefits of device scaling for DRAM memory slow, it will become increasingly difficult to keep memory capacities balanced with increasing computational rates offered by next-generation processors. However, several emerging memory technologies related to nonvolatile memory (NVM) devices are being investigated as an alternative for DRAM. Moving forward, NVM devices could offer solutions for HPC architectures. Researchers are investigating how to integratemore » these emerging technologies into future extreme-scale HPC systems and how to expose these capabilities in the software stack and applications. In addition, current results show several of these strategies could offer high-bandwidth I/O, larger main memory capacities, persistent data structures, and new approaches for application resilience and output postprocessing, such as transaction-based incremental checkpointing and in situ visualization, respectively.« less

Workload Characterization of a Leadership Class Storage Cluster

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, Youngjae; Gunasekaran, Raghul; Shipman, Galen M

2010-01-01

Understanding workload characteristics is critical for optimizing and improving the performance of current systems and software, and architecting new storage systems based on observed workload patterns. In this paper, we characterize the scientific workloads of the world s fastest HPC (High Performance Computing) storage cluster, Spider, at the Oak Ridge Leadership Computing Facility (OLCF). Spider provides an aggregate bandwidth of over 240 GB/s with over 10 petabytes of RAID 6 formatted capacity. OLCFs flagship petascale simulation platform, Jaguar, and other large HPC clusters, in total over 250 thousands compute cores, depend on Spider for their I/O needs. We characterize themore » system utilization, the demands of reads and writes, idle time, and the distribution of read requests to write requests for the storage system observed over a period of 6 months. From this study we develop synthesized workloads and we show that the read and write I/O bandwidth usage as well as the inter-arrival time of requests can be modeled as a Pareto distribution.« less

Using the Eclipse Parallel Tools Platform to Assist Earth Science Model Development and Optimization on High Performance Computers

NASA Astrophysics Data System (ADS)

Alameda, J. C.

2011-12-01

Development and optimization of computational science models, particularly on high performance computers, and with the advent of ubiquitous multicore processor systems, practically on every system, has been accomplished with basic software tools, typically, command-line based compilers, debuggers, performance tools that have not changed substantially from the days of serial and early vector computers. However, model complexity, including the complexity added by modern message passing libraries such as MPI, and the need for hybrid code models (such as openMP and MPI) to be able to take full advantage of high performance computers with an increasing core count per shared memory node, has made development and optimization of such codes an increasingly arduous task. Additional architectural developments, such as many-core processors, only complicate the situation further. In this paper, we describe how our NSF-funded project, "SI2-SSI: A Productive and Accessible Development Workbench for HPC Applications Using the Eclipse Parallel Tools Platform" (WHPC) seeks to improve the Eclipse Parallel Tools Platform, an environment designed to support scientific code development targeted at a diverse set of high performance computing systems. Our WHPC project to improve Eclipse PTP takes an application-centric view to improve PTP. We are using a set of scientific applications, each with a variety of challenges, and using PTP to drive further improvements to both the scientific application, as well as to understand shortcomings in Eclipse PTP from an application developer perspective, to drive our list of improvements we seek to make. We are also partnering with performance tool providers, to drive higher quality performance tool integration. We have partnered with the Cactus group at Louisiana State University to improve Eclipse's ability to work with computational frameworks and extremely complex build systems, as well as to develop educational materials to incorporate into computational science and engineering codes. Finally, we are partnering with the lead PTP developers at IBM, to ensure we are as effective as possible within the Eclipse community development. We are also conducting training and outreach to our user community, including conference BOF sessions, monthly user calls, and an annual user meeting, so that we can best inform the improvements we make to Eclipse PTP. With these activities we endeavor to encourage use of modern software engineering practices, as enabled through the Eclipse IDE, with computational science and engineering applications. These practices include proper use of source code repositories, tracking and rectifying issues, measuring and monitoring code performance changes against both optimizations as well as ever-changing software stacks and configurations on HPC systems, as well as ultimately encouraging development and maintenance of testing suites -- things that have become commonplace in many software endeavors, but have lagged in the development of science applications. We view that the challenge with the increased complexity of both HPC systems and science applications demands the use of better software engineering methods, preferably enabled by modern tools such as Eclipse PTP, to help the computational science community thrive as we evolve the HPC landscape.

Cognitive Model Exploration and Optimization: A New Challenge for Computational Science

DTIC Science & Technology

2010-01-01

Introduction Research in cognitive science often involves the generation and analysis of computational cognitive models to explain various...HPC) clusters and volunteer computing for large-scale computational resources. The majority of applications on the Department of Defense HPC... clusters focus on solving partial differential equations (Post, 2009). These tend to be lean, fast models with little noise. While we lack specific

SharP

DOE Office of Scientific and Technical Information (OSTI.GOV)

Venkata, Manjunath Gorentla; Aderholdt, William F

The pre-exascale systems are expected to have a significant amount of hierarchical and heterogeneous on-node memory, and this trend of system architecture in extreme-scale systems is expected to continue into the exascale era. along with hierarchical-heterogeneous memory, the system typically has a high-performing network ad a compute accelerator. This system architecture is not only effective for running traditional High Performance Computing (HPC) applications (Big-Compute), but also for running data-intensive HPC applications and Big-Data applications. As a consequence, there is a growing desire to have a single system serve the needs of both Big-Compute and Big-Data applications. Though the system architecturemore » supports the convergence of the Big-Compute and Big-Data, the programming models and software layer have yet to evolve to support either hierarchical-heterogeneous memory systems or the convergence. A programming abstraction to address this problem. The programming abstraction is implemented as a software library and runs on pre-exascale and exascale systems supporting current and emerging system architecture. Using distributed data-structures as a central concept, it provides (1) a simple, usable, and portable abstraction for hierarchical-heterogeneous memory and (2) a unified programming abstraction for Big-Compute and Big-Data applications.« less

SoAx: A generic C++ Structure of Arrays for handling particles in HPC codes

NASA Astrophysics Data System (ADS)

Homann, Holger; Laenen, Francois

2018-03-01

The numerical study of physical problems often require integrating the dynamics of a large number of particles evolving according to a given set of equations. Particles are characterized by the information they are carrying such as an identity, a position other. There are generally speaking two different possibilities for handling particles in high performance computing (HPC) codes. The concept of an Array of Structures (AoS) is in the spirit of the object-oriented programming (OOP) paradigm in that the particle information is implemented as a structure. Here, an object (realization of the structure) represents one particle and a set of many particles is stored in an array. In contrast, using the concept of a Structure of Arrays (SoA), a single structure holds several arrays each representing one property (such as the identity) of the whole set of particles. The AoS approach is often implemented in HPC codes due to its handiness and flexibility. For a class of problems, however, it is known that the performance of SoA is much better than that of AoS. We confirm this observation for our particle problem. Using a benchmark we show that on modern Intel Xeon processors the SoA implementation is typically several times faster than the AoS one. On Intel's MIC co-processors the performance gap even attains a factor of ten. The same is true for GPU computing, using both computational and multi-purpose GPUs. Combining performance and handiness, we present the library SoAx that has optimal performance (on CPUs, MICs, and GPUs) while providing the same handiness as AoS. For this, SoAx uses modern C++ design techniques such template meta programming that allows to automatically generate code for user defined heterogeneous data structures.

Understanding I/O workload characteristics of a Peta-scale storage system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, Youngjae; Gunasekaran, Raghul

2015-01-01

Understanding workload characteristics is critical for optimizing and improving the performance of current systems and software, and architecting new storage systems based on observed workload patterns. In this paper, we characterize the I/O workloads of scientific applications of one of the world s fastest high performance computing (HPC) storage cluster, Spider, at the Oak Ridge Leadership Computing Facility (OLCF). OLCF flagship petascale simulation platform, Titan, and other large HPC clusters, in total over 250 thousands compute cores, depend on Spider for their I/O needs. We characterize the system utilization, the demands of reads and writes, idle time, storage space utilization,more » and the distribution of read requests to write requests for the Peta-scale Storage Systems. From this study, we develop synthesized workloads, and we show that the read and write I/O bandwidth usage as well as the inter-arrival time of requests can be modeled as a Pareto distribution. We also study the I/O load imbalance problems using I/O performance data collected from the Spider storage system.« less

Arc4nix: A cross-platform geospatial analytical library for cluster and cloud computing

NASA Astrophysics Data System (ADS)

Tang, Jingyin; Matyas, Corene J.

2018-02-01

Big Data in geospatial technology is a grand challenge for processing capacity. The ability to use a GIS for geospatial analysis on Cloud Computing and High Performance Computing (HPC) clusters has emerged as a new approach to provide feasible solutions. However, users lack the ability to migrate existing research tools to a Cloud Computing or HPC-based environment because of the incompatibility of the market-dominating ArcGIS software stack and Linux operating system. This manuscript details a cross-platform geospatial library "arc4nix" to bridge this gap. Arc4nix provides an application programming interface compatible with ArcGIS and its Python library "arcpy". Arc4nix uses a decoupled client-server architecture that permits geospatial analytical functions to run on the remote server and other functions to run on the native Python environment. It uses functional programming and meta-programming language to dynamically construct Python codes containing actual geospatial calculations, send them to a server and retrieve results. Arc4nix allows users to employ their arcpy-based script in a Cloud Computing and HPC environment with minimal or no modification. It also supports parallelizing tasks using multiple CPU cores and nodes for large-scale analyses. A case study of geospatial processing of a numerical weather model's output shows that arcpy scales linearly in a distributed environment. Arc4nix is open-source software.

An interactive physics-based unmanned ground vehicle simulator leveraging open source gaming technology: progress in the development and application of the virtual autonomous navigation environment (VANE) desktop

NASA Astrophysics Data System (ADS)

Rohde, Mitchell M.; Crawford, Justin; Toschlog, Matthew; Iagnemma, Karl D.; Kewlani, Guarav; Cummins, Christopher L.; Jones, Randolph A.; Horner, David A.

2009-05-01

It is widely recognized that simulation is pivotal to vehicle development, whether manned or unmanned. There are few dedicated choices, however, for those wishing to perform realistic, end-to-end simulations of unmanned ground vehicles (UGVs). The Virtual Autonomous Navigation Environment (VANE), under development by US Army Engineer Research and Development Center (ERDC), provides such capabilities but utilizes a High Performance Computing (HPC) Computational Testbed (CTB) and is not intended for on-line, real-time performance. A product of the VANE HPC research is a real-time desktop simulation application under development by the authors that provides a portal into the HPC environment as well as interaction with wider-scope semi-automated force simulations (e.g. OneSAF). This VANE desktop application, dubbed the Autonomous Navigation Virtual Environment Laboratory (ANVEL), enables analysis and testing of autonomous vehicle dynamics and terrain/obstacle interaction in real-time with the capability to interact within the HPC constructive geo-environmental CTB for high fidelity sensor evaluations. ANVEL leverages rigorous physics-based vehicle and vehicle-terrain interaction models in conjunction with high-quality, multimedia visualization techniques to form an intuitive, accurate engineering tool. The system provides an adaptable and customizable simulation platform that allows developers a controlled, repeatable testbed for advanced simulations. ANVEL leverages several key technologies not common to traditional engineering simulators, including techniques from the commercial video-game industry. These enable ANVEL to run on inexpensive commercial, off-the-shelf (COTS) hardware. In this paper, the authors describe key aspects of ANVEL and its development, as well as several initial applications of the system.

Unmet needs for analyzing biological big data: A survey of 704 NSF principal investigators.

PubMed

Barone, Lindsay; Williams, Jason; Micklos, David

2017-10-01

In a 2016 survey of 704 National Science Foundation (NSF) Biological Sciences Directorate principal investigators (BIO PIs), nearly 90% indicated they are currently or will soon be analyzing large data sets. BIO PIs considered a range of computational needs important to their work, including high performance computing (HPC), bioinformatics support, multistep workflows, updated analysis software, and the ability to store, share, and publish data. Previous studies in the United States and Canada emphasized infrastructure needs. However, BIO PIs said the most pressing unmet needs are training in data integration, data management, and scaling analyses for HPC-acknowledging that data science skills will be required to build a deeper understanding of life. This portends a growing data knowledge gap in biology and challenges institutions and funding agencies to redouble their support for computational training in biology.

Running R Statistical Computing Environment Software on the Peregrine

Science.gov Websites

for the development of new statistical methodologies and enjoys a large user base. Please consult the distribution details. Natural language support but running in an English locale R is a collaborative project programming paradigms to better leverage modern HPC systems. The CRAN task view for High Performance Computing

High-Performance Happy

ERIC Educational Resources Information Center

O'Hanlon, Charlene

2007-01-01

Traditionally, the high-performance computing (HPC) systems used to conduct research at universities have amounted to silos of technology scattered across the campus and falling under the purview of the researchers themselves. This article reports that a growing number of universities are now taking over the management of those systems and…

New Challenges of the Computation of Multiple Sequence Alignments in the High-Throughput Era (2010 JGI/ANL HPC Workshop)

ScienceCinema

Notredame, Cedric

2018-05-02

Cedric Notredame from the Centre for Genomic Regulation gives a presentation on New Challenges of the Computation of Multiple Sequence Alignments in the High-Throughput Era at the JGI/Argonne HPC Workshop on January 26, 2010.

NCI's High Performance Computing (HPC) and High Performance Data (HPD) Computing Platform for Environmental and Earth System Data Science

NASA Astrophysics Data System (ADS)

Evans, Ben; Allen, Chris; Antony, Joseph; Bastrakova, Irina; Gohar, Kashif; Porter, David; Pugh, Tim; Santana, Fabiana; Smillie, Jon; Trenham, Claire; Wang, Jingbo; Wyborn, Lesley

2015-04-01

The National Computational Infrastructure (NCI) has established a powerful and flexible in-situ petascale computational environment to enable both high performance computing and Data-intensive Science across a wide spectrum of national environmental and earth science data collections - in particular climate, observational data and geoscientific assets. This paper examines 1) the computational environments that supports the modelling and data processing pipelines, 2) the analysis environments and methods to support data analysis, and 3) the progress so far to harmonise the underlying data collections for future interdisciplinary research across these large volume data collections. NCI has established 10+ PBytes of major national and international data collections from both the government and research sectors based on six themes: 1) weather, climate, and earth system science model simulations, 2) marine and earth observations, 3) geosciences, 4) terrestrial ecosystems, 5) water and hydrology, and 6) astronomy, social and biosciences. Collectively they span the lithosphere, crust, biosphere, hydrosphere, troposphere, and stratosphere. The data is largely sourced from NCI's partners (which include the custodians of many of the major Australian national-scale scientific collections), leading research communities, and collaborating overseas organisations. New infrastructures created at NCI mean the data collections are now accessible within an integrated High Performance Computing and Data (HPC-HPD) environment - a 1.2 PFlop supercomputer (Raijin), a HPC class 3000 core OpenStack cloud system and several highly connected large-scale high-bandwidth Lustre filesystems. The hardware was designed at inception to ensure that it would allow the layered software environment to flexibly accommodate the advancement of future data science. New approaches to software technology and data models have also had to be developed to enable access to these large and exponentially increasing data volumes at NCI. Traditional HPC and data environments are still made available in a way that flexibly provides the tools, services and supporting software systems on these new petascale infrastructures. But to enable the research to take place at this scale, the data, metadata and software now need to evolve together - creating a new integrated high performance infrastructure. The new infrastructure at NCI currently supports a catalogue of integrated, reusable software and workflows from earth system and ecosystem modelling, weather research, satellite and other observed data processing and analysis. One of the challenges for NCI has been to support existing techniques and methods, while carefully preparing the underlying infrastructure for the transition needed for the next class of Data-intensive Science. In doing so, a flexible range of techniques and software can be made available for application across the corpus of data collections available, and to provide a new infrastructure for future interdisciplinary research.

Secure Enclaves: An Isolation-centric Approach for Creating Secure High Performance Computing Environments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aderholdt, Ferrol; Caldwell, Blake A.; Hicks, Susan Elaine

High performance computing environments are often used for a wide variety of workloads ranging from simulation, data transformation and analysis, and complex workflows to name just a few. These systems may process data at various security levels but in so doing are often enclaved at the highest security posture. This approach places significant restrictions on the users of the system even when processing data at a lower security level and exposes data at higher levels of confidentiality to a much broader population than otherwise necessary. The traditional approach of isolation, while effective in establishing security enclaves poses significant challenges formore » the use of shared infrastructure in HPC environments. This report details current state-of-the-art in virtualization, reconfigurable network enclaving via Software Defined Networking (SDN), and storage architectures and bridging techniques for creating secure enclaves in HPC environments.« less

On-demand provisioning of HEP compute resources on cloud sites and shared HPC centers

NASA Astrophysics Data System (ADS)

Erli, G.; Fischer, F.; Fleig, G.; Giffels, M.; Hauth, T.; Quast, G.; Schnepf, M.; Heese, J.; Leppert, K.; Arnaez de Pedro, J.; Sträter, R.

2017-10-01

This contribution reports on solutions, experiences and recent developments with the dynamic, on-demand provisioning of remote computing resources for analysis and simulation workflows. Local resources of a physics institute are extended by private and commercial cloud sites, ranging from the inclusion of desktop clusters over institute clusters to HPC centers. Rather than relying on dedicated HEP computing centers, it is nowadays more reasonable and flexible to utilize remote computing capacity via virtualization techniques or container concepts. We report on recent experience from incorporating a remote HPC center (NEMO Cluster, Freiburg University) and resources dynamically requested from the commercial provider 1&1 Internet SE into our intitute’s computing infrastructure. The Freiburg HPC resources are requested via the standard batch system, allowing HPC and HEP applications to be executed simultaneously, such that regular batch jobs run side by side to virtual machines managed via OpenStack [1]. For the inclusion of the 1&1 commercial resources, a Python API and SDK as well as the possibility to upload images were available. Large scale tests prove the capability to serve the scientific use case in the European 1&1 datacenters. The described environment at the Institute of Experimental Nuclear Physics (IEKP) at KIT serves the needs of researchers participating in the CMS and Belle II experiments. In total, resources exceeding half a million CPU hours have been provided by remote sites.

Resilience Design Patterns - A Structured Approach to Resilience at Extreme Scale (version 1.1)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hukerikar, Saurabh; Engelmann, Christian

Reliability is a serious concern for future extreme-scale high-performance computing (HPC) systems. Projections based on the current generation of HPC systems and technology roadmaps suggest the prevalence of very high fault rates in future systems. The errors resulting from these faults will propagate and generate various kinds of failures, which may result in outcomes ranging from result corruptions to catastrophic application crashes. Therefore the resilience challenge for extreme-scale HPC systems requires management of various hardware and software technologies that are capable of handling a broad set of fault models at accelerated fault rates. Also, due to practical limits on powermore » consumption in HPC systems future systems are likely to embrace innovative architectures, increasing the levels of hardware and software complexities. As a result the techniques that seek to improve resilience must navigate the complex trade-off space between resilience and the overheads to power consumption and performance. While the HPC community has developed various resilience solutions, application-level techniques as well as system-based solutions, the solution space of HPC resilience techniques remains fragmented. There are no formal methods and metrics to investigate and evaluate resilience holistically in HPC systems that consider impact scope, handling coverage, and performance & power efficiency across the system stack. Additionally, few of the current approaches are portable to newer architectures and software environments that will be deployed on future systems. In this document, we develop a structured approach to the management of HPC resilience using the concept of resilience-based design patterns. A design pattern is a general repeatable solution to a commonly occurring problem. We identify the commonly occurring problems and solutions used to deal with faults, errors and failures in HPC systems. Each established solution is described in the form of a pattern that addresses concrete problems in the design of resilient systems. The complete catalog of resilience design patterns provides designers with reusable design elements. We also define a framework that enhances a designer's understanding of the important constraints and opportunities for the design patterns to be implemented and deployed at various layers of the system stack. This design framework may be used to establish mechanisms and interfaces to coordinate flexible fault management across hardware and software components. The framework also supports optimization of the cost-benefit trade-offs among performance, resilience, and power consumption. The overall goal of this work is to enable a systematic methodology for the design and evaluation of resilience technologies in extreme-scale HPC systems that keep scientific applications running to a correct solution in a timely and cost-efficient manner in spite of frequent faults, errors, and failures of various types.« less

Toward Transparent Data Management in Multi-layer Storage Hierarchy for HPC Systems

DOE PAGES

Wadhwa, Bharti; Byna, Suren; Butt, Ali R.

2018-04-17

Upcoming exascale high performance computing (HPC) systems are expected to comprise multi-tier storage hierarchy, and thus will necessitate innovative storage and I/O mechanisms. Traditional disk and block-based interfaces and file systems face severe challenges in utilizing capabilities of storage hierarchies due to the lack of hierarchy support and semantic interfaces. Object-based and semantically-rich data abstractions for scientific data management on large scale systems offer a sustainable solution to these challenges. Such data abstractions can also simplify users involvement in data movement. Here, we take the first steps of realizing such an object abstraction and explore storage mechanisms for these objectsmore » to enhance I/O performance, especially for scientific applications. We explore how an object-based interface can facilitate next generation scalable computing systems by presenting the mapping of data I/O from two real world HPC scientific use cases: a plasma physics simulation code (VPIC) and a cosmology simulation code (HACC). Our storage model stores data objects in different physical organizations to support data movement across layers of memory/storage hierarchy. Our implementation sclaes well to 16K parallel processes, and compared to the state of the art, such as MPI-IO and HDF5, our object-based data abstractions and data placement strategy in multi-level storage hierarchy achieves up to 7 X I/O performance improvement for scientific data.« less

Toward Transparent Data Management in Multi-layer Storage Hierarchy for HPC Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wadhwa, Bharti; Byna, Suren; Butt, Ali R.

Upcoming exascale high performance computing (HPC) systems are expected to comprise multi-tier storage hierarchy, and thus will necessitate innovative storage and I/O mechanisms. Traditional disk and block-based interfaces and file systems face severe challenges in utilizing capabilities of storage hierarchies due to the lack of hierarchy support and semantic interfaces. Object-based and semantically-rich data abstractions for scientific data management on large scale systems offer a sustainable solution to these challenges. Such data abstractions can also simplify users involvement in data movement. Here, we take the first steps of realizing such an object abstraction and explore storage mechanisms for these objectsmore » to enhance I/O performance, especially for scientific applications. We explore how an object-based interface can facilitate next generation scalable computing systems by presenting the mapping of data I/O from two real world HPC scientific use cases: a plasma physics simulation code (VPIC) and a cosmology simulation code (HACC). Our storage model stores data objects in different physical organizations to support data movement across layers of memory/storage hierarchy. Our implementation sclaes well to 16K parallel processes, and compared to the state of the art, such as MPI-IO and HDF5, our object-based data abstractions and data placement strategy in multi-level storage hierarchy achieves up to 7 X I/O performance improvement for scientific data.« less

BrainFrame: a node-level heterogeneous accelerator platform for neuron simulations

NASA Astrophysics Data System (ADS)

Smaragdos, Georgios; Chatzikonstantis, Georgios; Kukreja, Rahul; Sidiropoulos, Harry; Rodopoulos, Dimitrios; Sourdis, Ioannis; Al-Ars, Zaid; Kachris, Christoforos; Soudris, Dimitrios; De Zeeuw, Chris I.; Strydis, Christos

2017-12-01

Objective. The advent of high-performance computing (HPC) in recent years has led to its increasing use in brain studies through computational models. The scale and complexity of such models are constantly increasing, leading to challenging computational requirements. Even though modern HPC platforms can often deal with such challenges, the vast diversity of the modeling field does not permit for a homogeneous acceleration platform to effectively address the complete array of modeling requirements. Approach. In this paper we propose and build BrainFrame, a heterogeneous acceleration platform that incorporates three distinct acceleration technologies, an Intel Xeon-Phi CPU, a NVidia GP-GPU and a Maxeler Dataflow Engine. The PyNN software framework is also integrated into the platform. As a challenging proof of concept, we analyze the performance of BrainFrame on different experiment instances of a state-of-the-art neuron model, representing the inferior-olivary nucleus using a biophysically-meaningful, extended Hodgkin-Huxley representation. The model instances take into account not only the neuronal-network dimensions but also different network-connectivity densities, which can drastically affect the workload’s performance characteristics. Main results. The combined use of different HPC technologies demonstrates that BrainFrame is better able to cope with the modeling diversity encountered in realistic experiments while at the same time running on significantly lower energy budgets. Our performance analysis clearly shows that the model directly affects performance and all three technologies are required to cope with all the model use cases. Significance. The BrainFrame framework is designed to transparently configure and select the appropriate back-end accelerator technology for use per simulation run. The PyNN integration provides a familiar bridge to the vast number of models already available. Additionally, it gives a clear roadmap for extending the platform support beyond the proof of concept, with improved usability and directly useful features to the computational-neuroscience community, paving the way for wider adoption.

BrainFrame: a node-level heterogeneous accelerator platform for neuron simulations.

PubMed

Smaragdos, Georgios; Chatzikonstantis, Georgios; Kukreja, Rahul; Sidiropoulos, Harry; Rodopoulos, Dimitrios; Sourdis, Ioannis; Al-Ars, Zaid; Kachris, Christoforos; Soudris, Dimitrios; De Zeeuw, Chris I; Strydis, Christos

2017-12-01

The advent of high-performance computing (HPC) in recent years has led to its increasing use in brain studies through computational models. The scale and complexity of such models are constantly increasing, leading to challenging computational requirements. Even though modern HPC platforms can often deal with such challenges, the vast diversity of the modeling field does not permit for a homogeneous acceleration platform to effectively address the complete array of modeling requirements. In this paper we propose and build BrainFrame, a heterogeneous acceleration platform that incorporates three distinct acceleration technologies, an Intel Xeon-Phi CPU, a NVidia GP-GPU and a Maxeler Dataflow Engine. The PyNN software framework is also integrated into the platform. As a challenging proof of concept, we analyze the performance of BrainFrame on different experiment instances of a state-of-the-art neuron model, representing the inferior-olivary nucleus using a biophysically-meaningful, extended Hodgkin-Huxley representation. The model instances take into account not only the neuronal-network dimensions but also different network-connectivity densities, which can drastically affect the workload's performance characteristics. The combined use of different HPC technologies demonstrates that BrainFrame is better able to cope with the modeling diversity encountered in realistic experiments while at the same time running on significantly lower energy budgets. Our performance analysis clearly shows that the model directly affects performance and all three technologies are required to cope with all the model use cases. The BrainFrame framework is designed to transparently configure and select the appropriate back-end accelerator technology for use per simulation run. The PyNN integration provides a familiar bridge to the vast number of models already available. Additionally, it gives a clear roadmap for extending the platform support beyond the proof of concept, with improved usability and directly useful features to the computational-neuroscience community, paving the way for wider adoption.

Implementation of the NAS Parallel Benchmarks in Java

NASA Technical Reports Server (NTRS)

Frumkin, Michael A.; Schultz, Matthew; Jin, Haoqiang; Yan, Jerry; Biegel, Bryan (Technical Monitor)

2002-01-01

Several features make Java an attractive choice for High Performance Computing (HPC). In order to gauge the applicability of Java to Computational Fluid Dynamics (CFD), we have implemented the NAS (NASA Advanced Supercomputing) Parallel Benchmarks in Java. The performance and scalability of the benchmarks point out the areas where improvement in Java compiler technology and in Java thread implementation would position Java closer to Fortran in the competition for CFD applications.

Petascale computation performance of lightweight multiscale cardiac models using hybrid programming models.

PubMed

Pope, Bernard J; Fitch, Blake G; Pitman, Michael C; Rice, John J; Reumann, Matthias

2011-01-01

Future multiscale and multiphysics models must use the power of high performance computing (HPC) systems to enable research into human disease, translational medical science, and treatment. Previously we showed that computationally efficient multiscale models will require the use of sophisticated hybrid programming models, mixing distributed message passing processes (e.g. the message passing interface (MPI)) with multithreading (e.g. OpenMP, POSIX pthreads). The objective of this work is to compare the performance of such hybrid programming models when applied to the simulation of a lightweight multiscale cardiac model. Our results show that the hybrid models do not perform favourably when compared to an implementation using only MPI which is in contrast to our results using complex physiological models. Thus, with regards to lightweight multiscale cardiac models, the user may not need to increase programming complexity by using a hybrid programming approach. However, considering that model complexity will increase as well as the HPC system size in both node count and number of cores per node, it is still foreseeable that we will achieve faster than real time multiscale cardiac simulations on these systems using hybrid programming models.

Providing a parallel and distributed capability for JMASS using SPEEDES

NASA Astrophysics Data System (ADS)

Valinski, Maria; Driscoll, Jonathan; McGraw, Robert M.; Meyer, Bob

2002-07-01

The Joint Modeling And Simulation System (JMASS) is a Tri-Service simulation environment that supports engineering and engagement-level simulations. As JMASS is expanded to support other Tri-Service domains, the current set of modeling services must be expanded for High Performance Computing (HPC) applications by adding support for advanced time-management algorithms, parallel and distributed topologies, and high speed communications. By providing support for these services, JMASS can better address modeling domains requiring parallel computationally intense calculations such clutter, vulnerability and lethality calculations, and underwater-based scenarios. A risk reduction effort implementing some HPC services for JMASS using the SPEEDES (Synchronous Parallel Environment for Emulation and Discrete Event Simulation) Simulation Framework has recently concluded. As an artifact of the JMASS-SPEEDES integration, not only can HPC functionality be brought to the JMASS program through SPEEDES, but an additional HLA-based capability can be demonstrated that further addresses interoperability issues. The JMASS-SPEEDES integration provided a means of adding HLA capability to preexisting JMASS scenarios through an implementation of the standard JMASS port communication mechanism that allows players to communicate.

System-Level Virtualization for High Performance Computing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vallee, Geoffroy R; Naughton, III, Thomas J; Engelmann, Christian

2008-01-01

System-level virtualization has been a research topic since the 70's but regained popularity during the past few years because of the availability of efficient solution such as Xen and the implementation of hardware support in commodity processors (e.g. Intel-VT, AMD-V). However, a majority of system-level virtualization projects is guided by the server consolidation market. As a result, current virtualization solutions appear to not be suitable for high performance computing (HPC) which is typically based on large-scale systems. On another hand there is significant interest in exploiting virtual machines (VMs) within HPC for a number of other reasons. By virtualizing themore » machine, one is able to run a variety of operating systems and environments as needed by the applications. Virtualization allows users to isolate workloads, improving security and reliability. It is also possible to support non-native environments and/or legacy operating environments through virtualization. In addition, it is possible to balance work loads, use migration techniques to relocate applications from failing machines, and isolate fault systems for repair. This document presents the challenges for the implementation of a system-level virtualization solution for HPC. It also presents a brief survey of the different approaches and techniques to address these challenges.« less

Application of the Linux cluster for exhaustive window haplotype analysis using the FBAT and Unphased programs.

PubMed

Mishima, Hiroyuki; Lidral, Andrew C; Ni, Jun

2008-05-28

Genetic association studies have been used to map disease-causing genes. A newly introduced statistical method, called exhaustive haplotype association study, analyzes genetic information consisting of different numbers and combinations of DNA sequence variations along a chromosome. Such studies involve a large number of statistical calculations and subsequently high computing power. It is possible to develop parallel algorithms and codes to perform the calculations on a high performance computing (HPC) system. However, most existing commonly-used statistic packages for genetic studies are non-parallel versions. Alternatively, one may use the cutting-edge technology of grid computing and its packages to conduct non-parallel genetic statistical packages on a centralized HPC system or distributed computing systems. In this paper, we report the utilization of a queuing scheduler built on the Grid Engine and run on a Rocks Linux cluster for our genetic statistical studies. Analysis of both consecutive and combinational window haplotypes was conducted by the FBAT (Laird et al., 2000) and Unphased (Dudbridge, 2003) programs. The dataset consisted of 26 loci from 277 extended families (1484 persons). Using the Rocks Linux cluster with 22 compute-nodes, FBAT jobs performed about 14.4-15.9 times faster, while Unphased jobs performed 1.1-18.6 times faster compared to the accumulated computation duration. Execution of exhaustive haplotype analysis using non-parallel software packages on a Linux-based system is an effective and efficient approach in terms of cost and performance.

Application of the Linux cluster for exhaustive window haplotype analysis using the FBAT and Unphased programs

PubMed Central

Mishima, Hiroyuki; Lidral, Andrew C; Ni, Jun

2008-01-01

Background Genetic association studies have been used to map disease-causing genes. A newly introduced statistical method, called exhaustive haplotype association study, analyzes genetic information consisting of different numbers and combinations of DNA sequence variations along a chromosome. Such studies involve a large number of statistical calculations and subsequently high computing power. It is possible to develop parallel algorithms and codes to perform the calculations on a high performance computing (HPC) system. However, most existing commonly-used statistic packages for genetic studies are non-parallel versions. Alternatively, one may use the cutting-edge technology of grid computing and its packages to conduct non-parallel genetic statistical packages on a centralized HPC system or distributed computing systems. In this paper, we report the utilization of a queuing scheduler built on the Grid Engine and run on a Rocks Linux cluster for our genetic statistical studies. Results Analysis of both consecutive and combinational window haplotypes was conducted by the FBAT (Laird et al., 2000) and Unphased (Dudbridge, 2003) programs. The dataset consisted of 26 loci from 277 extended families (1484 persons). Using the Rocks Linux cluster with 22 compute-nodes, FBAT jobs performed about 14.4–15.9 times faster, while Unphased jobs performed 1.1–18.6 times faster compared to the accumulated computation duration. Conclusion Execution of exhaustive haplotype analysis using non-parallel software packages on a Linux-based system is an effective and efficient approach in terms of cost and performance. PMID:18541045

Hierarchically porous carbon/polyaniline hybrid for use in supercapacitors.

PubMed

Joo, Min Jae; Yun, Young Soo; Jin, Hyoung-Joon

2014-12-01

A hierarchically porous carbon (HPC)/polyaniline (PANI) hybrid electrode was prepared by the polymerization of PANI on the surface of the HPC via rapid-mixing polymerization. The surface morphologies and chemical composition of the HPC/PANI hybrid electrode were characterized using transmission electron microscopy and X-ray photoelectron spectroscopy (XPS), respectively. The surface morphologies and XPS results for the HPC, PANI and HPC/PANI hybrids indicate that PANI is coated on the surface of HPC in the HPC/PANI hybrids which have two different nitrogen groups as a benzenoid amine (-NH-) peak and positively charged nitrogen (N+) peak. The electrochemical performances of the HPC/PANI hybrids were analyzed by performing cyclic voltammetry and galvanostatic charge-discharge tests. The HPC/PANI hybrids showed a better specific capacitance (222 F/g) than HPC (111 F/g) because of effect of pseudocapacitor behavior. In addition, good cycle stabilities were maintained over 1000 cycles.

HPC USER WORKSHOP - JUNE 12TH | High-Performance Computing | NREL

Science.gov Websites

to CentOS 7, changes to modules management, Singularity and containers on Peregrine, and using of changes, with the remaining two hours dedicated to demos and one-on-one interaction as needed

Innovative HPC architectures for the study of planetary plasma environments

NASA Astrophysics Data System (ADS)

Amaya, Jorge; Wolf, Anna; Lembège, Bertrand; Zitz, Anke; Alvarez, Damian; Lapenta, Giovanni

2016-04-01

DEEP-ER is an European Commission founded project that develops a new type of High Performance Computer architecture. The revolutionary system is currently used by KU Leuven to study the effects of the solar wind on the global environments of the Earth and Mercury. The new architecture combines the versatility of Intel Xeon computing nodes with the power of the upcoming Intel Xeon Phi accelerators. Contrary to classical heterogeneous HPC architectures, where it is customary to find CPU and accelerators in the same computing nodes, in the DEEP-ER system CPU nodes are grouped together (Cluster) and independently from the accelerator nodes (Booster). The system is equipped with a state of the art interconnection network, a highly scalable and fast I/O and a fail recovery resiliency system. The final objective of the project is to introduce a scalable system that can be used to create the next generation of exascale supercomputers. The code iPic3D from KU Leuven is being adapted to this new architecture. This particle-in-cell code can now perform the computation of the electromagnetic fields in the Cluster while the particles are moved in the Booster side. Using fast and scalable Xeon Phi accelerators in the Booster we can introduce many more particles per cell in the simulation than what is possible in the current generation of HPC systems, allowing to calculate fully kinetic plasmas with very low interpolation noise. The system will be used to perform fully kinetic, low noise, 3D simulations of the interaction of the solar wind with the magnetosphere of the Earth and Mercury. Preliminary simulations have been performed in other HPC centers in order to compare the results in different systems. In this presentation we show the complexity of the plasma flow around the planets, including the development of hydrodynamic instabilities at the flanks, the presence of the collision-less shock, the magnetosheath, the magnetopause, reconnection zones, the formation of the plasma sheet and the magnetotail, and the variation of ion/electron plasma flows when crossing these frontiers. The simulations also give access to detailed information about the particle dynamics and their velocity distribution at locations that can be used for comparison with satellite data.

PerSEUS: Ultra-Low-Power High Performance Computing for Plasma Simulations

NASA Astrophysics Data System (ADS)

Doxas, I.; Andreou, A.; Lyon, J.; Angelopoulos, V.; Lu, S.; Pritchett, P. L.

2017-12-01

Peta-op SupErcomputing Unconventional System (PerSEUS) aims to explore the use for High Performance Scientific Computing (HPC) of ultra-low-power mixed signal unconventional computational elements developed by Johns Hopkins University (JHU), and demonstrate that capability on both fluid and particle Plasma codes. We will describe the JHU Mixed-signal Unconventional Supercomputing Elements (MUSE), and report initial results for the Lyon-Fedder-Mobarry (LFM) global magnetospheric MHD code, and a UCLA general purpose relativistic Particle-In-Cell (PIC) code.

Toward performance portability of the Albany finite element analysis code using the Kokkos library

DOE Office of Scientific and Technical Information (OSTI.GOV)

Demeshko, Irina; Watkins, Jerry; Tezaur, Irina K.

Performance portability on heterogeneous high-performance computing (HPC) systems is a major challenge faced today by code developers: parallel code needs to be executed correctly as well as with high performance on machines with different architectures, operating systems, and software libraries. The finite element method (FEM) is a popular and flexible method for discretizing partial differential equations arising in a wide variety of scientific, engineering, and industrial applications that require HPC. This paper presents some preliminary results pertaining to our development of a performance portable implementation of the FEM-based Albany code. Performance portability is achieved using the Kokkos library. We presentmore » performance results for the Aeras global atmosphere dynamical core module in Albany. Finally, numerical experiments show that our single code implementation gives reasonable performance across three multicore/many-core architectures: NVIDIA General Processing Units (GPU’s), Intel Xeon Phis, and multicore CPUs.« less

Toward performance portability of the Albany finite element analysis code using the Kokkos library

DOE PAGES

Demeshko, Irina; Watkins, Jerry; Tezaur, Irina K.; ...

2018-02-05

Performance portability on heterogeneous high-performance computing (HPC) systems is a major challenge faced today by code developers: parallel code needs to be executed correctly as well as with high performance on machines with different architectures, operating systems, and software libraries. The finite element method (FEM) is a popular and flexible method for discretizing partial differential equations arising in a wide variety of scientific, engineering, and industrial applications that require HPC. This paper presents some preliminary results pertaining to our development of a performance portable implementation of the FEM-based Albany code. Performance portability is achieved using the Kokkos library. We presentmore » performance results for the Aeras global atmosphere dynamical core module in Albany. Finally, numerical experiments show that our single code implementation gives reasonable performance across three multicore/many-core architectures: NVIDIA General Processing Units (GPU’s), Intel Xeon Phis, and multicore CPUs.« less

An Integrated Software Package to Enable Predictive Simulation Capabilities

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Yousu; Fitzhenry, Erin B.; Jin, Shuangshuang

The power grid is increasing in complexity due to the deployment of smart grid technologies. Such technologies vastly increase the size and complexity of power grid systems for simulation and modeling. This increasing complexity necessitates not only the use of high-performance-computing (HPC) techniques, but a smooth, well-integrated interplay between HPC applications. This paper presents a new integrated software package that integrates HPC applications and a web-based visualization tool based on a middleware framework. This framework can support the data communication between different applications. Case studies with a large power system demonstrate the predictive capability brought by the integrated software package,more » as well as the better situational awareness provided by the web-based visualization tool in a live mode. Test results validate the effectiveness and usability of the integrated software package.« less

Spatial Support Vector Regression to Detect Silent Errors in the Exascale Era

DOE Office of Scientific and Technical Information (OSTI.GOV)

Subasi, Omer; Di, Sheng; Bautista-Gomez, Leonardo

As the exascale era approaches, the increasing capacity of high-performance computing (HPC) systems with targeted power and energy budget goals introduces significant challenges in reliability. Silent data corruptions (SDCs) or silent errors are one of the major sources that corrupt the executionresults of HPC applications without being detected. In this work, we explore a low-memory-overhead SDC detector, by leveraging epsilon-insensitive support vector machine regression, to detect SDCs that occur in HPC applications that can be characterized by an impact error bound. The key contributions are three fold. (1) Our design takes spatialfeatures (i.e., neighbouring data values for each data pointmore » in a snapshot) into training data, such that little memory overhead (less than 1%) is introduced. (2) We provide an in-depth study on the detection ability and performance with different parameters, and we optimize the detection range carefully. (3) Experiments with eight real-world HPC applications show thatour detector can achieve the detection sensitivity (i.e., recall) up to 99% yet suffer a less than 1% of false positive rate for most cases. Our detector incurs low performance overhead, 5% on average, for all benchmarks studied in the paper. Compared with other state-of-the-art techniques, our detector exhibits the best tradeoff considering the detection ability and overheads.« less

Enabling parallel simulation of large-scale HPC network systems

DOE PAGES

Mubarak, Misbah; Carothers, Christopher D.; Ross, Robert B.; ...

2016-04-07

Here, with the increasing complexity of today’s high-performance computing (HPC) architectures, simulation has become an indispensable tool for exploring the design space of HPC systems—in particular, networks. In order to make effective design decisions, simulations of these systems must possess the following properties: (1) have high accuracy and fidelity, (2) produce results in a timely manner, and (3) be able to analyze a broad range of network workloads. Most state-of-the-art HPC network simulation frameworks, however, are constrained in one or more of these areas. In this work, we present a simulation framework for modeling two important classes of networks usedmore » in today’s IBM and Cray supercomputers: torus and dragonfly networks. We use the Co-Design of Multi-layer Exascale Storage Architecture (CODES) simulation framework to simulate these network topologies at a flit-level detail using the Rensselaer Optimistic Simulation System (ROSS) for parallel discrete-event simulation. Our simulation framework meets all the requirements of a practical network simulation and can assist network designers in design space exploration. First, it uses validated and detailed flit-level network models to provide an accurate and high-fidelity network simulation. Second, instead of relying on serial time-stepped or traditional conservative discrete-event simulations that limit simulation scalability and efficiency, we use the optimistic event-scheduling capability of ROSS to achieve efficient and scalable HPC network simulations on today’s high-performance cluster systems. Third, our models give network designers a choice in simulating a broad range of network workloads, including HPC application workloads using detailed network traces, an ability that is rarely offered in parallel with high-fidelity network simulations« less

Enabling parallel simulation of large-scale HPC network systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mubarak, Misbah; Carothers, Christopher D.; Ross, Robert B.

Here, with the increasing complexity of today’s high-performance computing (HPC) architectures, simulation has become an indispensable tool for exploring the design space of HPC systems—in particular, networks. In order to make effective design decisions, simulations of these systems must possess the following properties: (1) have high accuracy and fidelity, (2) produce results in a timely manner, and (3) be able to analyze a broad range of network workloads. Most state-of-the-art HPC network simulation frameworks, however, are constrained in one or more of these areas. In this work, we present a simulation framework for modeling two important classes of networks usedmore » in today’s IBM and Cray supercomputers: torus and dragonfly networks. We use the Co-Design of Multi-layer Exascale Storage Architecture (CODES) simulation framework to simulate these network topologies at a flit-level detail using the Rensselaer Optimistic Simulation System (ROSS) for parallel discrete-event simulation. Our simulation framework meets all the requirements of a practical network simulation and can assist network designers in design space exploration. First, it uses validated and detailed flit-level network models to provide an accurate and high-fidelity network simulation. Second, instead of relying on serial time-stepped or traditional conservative discrete-event simulations that limit simulation scalability and efficiency, we use the optimistic event-scheduling capability of ROSS to achieve efficient and scalable HPC network simulations on today’s high-performance cluster systems. Third, our models give network designers a choice in simulating a broad range of network workloads, including HPC application workloads using detailed network traces, an ability that is rarely offered in parallel with high-fidelity network simulations« less

Distributed Accounting on the Grid

NASA Technical Reports Server (NTRS)

Thigpen, William; Hacker, Thomas J.; McGinnis, Laura F.; Athey, Brian D.

2001-01-01

By the late 1990s, the Internet was adequately equipped to move vast amounts of data between HPC (High Performance Computing) systems, and efforts were initiated to link together the national infrastructure of high performance computational and data storage resources together into a general computational utility 'grid', analogous to the national electrical power grid infrastructure. The purpose of the Computational grid is to provide dependable, consistent, pervasive, and inexpensive access to computational resources for the computing community in the form of a computing utility. This paper presents a fully distributed view of Grid usage accounting and a methodology for allocating Grid computational resources for use on a Grid computing system.

DCL System Using Deep Learning Approaches for Land-based or Ship-based Real-Time Recognition and Localization of Marine Mammals

DTIC Science & Technology

2012-09-30

platform (HPC) was developed, called the HPC-Acoustic Data Accelerator, or HPC-ADA for short. The HPC-ADA was designed based on fielded systems [1-4...software (Detection cLassificaiton for MAchine learning - High Peformance Computing). The software package was designed to utilize parallel and...Sedna [7] and is designed using a parallel architecture2, allowing existing algorithms to distribute to the various processing nodes with minimal changes

Connecting to HPC VPN | High-Performance Computing | NREL

Science.gov Websites

and password will match your NREL network account login/password. From OS X or Linux, open a terminal finalized. Open a Remote Desktop connection using server name WINHPC02 (this is the login node). Mac Mac

Resilience Design Patterns: A Structured Approach to Resilience at Extreme Scale

DOE PAGES

Engelmann, Christian; Hukerikar, Saurabh

2017-09-01

Reliability is a serious concern for future extreme-scale high-performance computing (HPC) systems. Projections based on the current generation of HPC systems and technology roadmaps suggest the prevalence of very high fault rates in future systems. While the HPC community has developed various resilience solutions, application-level techniques as well as system-based solutions, the solution space remains fragmented. There are no formal methods and metrics to integrate the various HPC resilience techniques into composite solutions, nor are there methods to holistically evaluate the adequacy and efficacy of such solutions in terms of their protection coverage, and their performance \\& power efficiency characteristics.more » Additionally, few of the current approaches are portable to newer architectures and software environments that will be deployed on future systems. In this paper, we develop a structured approach to the design, evaluation and optimization of HPC resilience using the concept of design patterns. A design pattern is a general repeatable solution to a commonly occurring problem. We identify the problems caused by various types of faults, errors and failures in HPC systems and the techniques used to deal with these events. Each well-known solution that addresses a specific HPC resilience challenge is described in the form of a pattern. We develop a complete catalog of such resilience design patterns, which may be used by system architects, system software and tools developers, application programmers, as well as users and operators as essential building blocks when designing and deploying resilience solutions. We also develop a design framework that enhances a designer's understanding the opportunities for integrating multiple patterns across layers of the system stack and the important constraints during implementation of the individual patterns. It is also useful for defining mechanisms and interfaces to coordinate flexible fault management across hardware and software components. The resilience patterns and the design framework also enable exploration and evaluation of design alternatives and support optimization of the cost-benefit trade-offs among performance, protection coverage, and power consumption of resilience solutions. Here, the overall goal of this work is to establish a systematic methodology for the design and evaluation of resilience technologies in extreme-scale HPC systems that keep scientific applications running to a correct solution in a timely and cost-efficient manner despite frequent faults, errors, and failures of various types.« less

Resilience Design Patterns: A Structured Approach to Resilience at Extreme Scale

DOE Office of Scientific and Technical Information (OSTI.GOV)

Engelmann, Christian; Hukerikar, Saurabh

Reliability is a serious concern for future extreme-scale high-performance computing (HPC) systems. Projections based on the current generation of HPC systems and technology roadmaps suggest the prevalence of very high fault rates in future systems. While the HPC community has developed various resilience solutions, application-level techniques as well as system-based solutions, the solution space remains fragmented. There are no formal methods and metrics to integrate the various HPC resilience techniques into composite solutions, nor are there methods to holistically evaluate the adequacy and efficacy of such solutions in terms of their protection coverage, and their performance \\& power efficiency characteristics.more » Additionally, few of the current approaches are portable to newer architectures and software environments that will be deployed on future systems. In this paper, we develop a structured approach to the design, evaluation and optimization of HPC resilience using the concept of design patterns. A design pattern is a general repeatable solution to a commonly occurring problem. We identify the problems caused by various types of faults, errors and failures in HPC systems and the techniques used to deal with these events. Each well-known solution that addresses a specific HPC resilience challenge is described in the form of a pattern. We develop a complete catalog of such resilience design patterns, which may be used by system architects, system software and tools developers, application programmers, as well as users and operators as essential building blocks when designing and deploying resilience solutions. We also develop a design framework that enhances a designer's understanding the opportunities for integrating multiple patterns across layers of the system stack and the important constraints during implementation of the individual patterns. It is also useful for defining mechanisms and interfaces to coordinate flexible fault management across hardware and software components. The resilience patterns and the design framework also enable exploration and evaluation of design alternatives and support optimization of the cost-benefit trade-offs among performance, protection coverage, and power consumption of resilience solutions. Here, the overall goal of this work is to establish a systematic methodology for the design and evaluation of resilience technologies in extreme-scale HPC systems that keep scientific applications running to a correct solution in a timely and cost-efficient manner despite frequent faults, errors, and failures of various types.« less

Climate simulations and services on HPC, Cloud and Grid infrastructures

NASA Astrophysics Data System (ADS)

Cofino, Antonio S.; Blanco, Carlos; Minondo Tshuma, Antonio

2017-04-01

Cloud, Grid and High Performance Computing have changed the accessibility and availability of computing resources for Earth Science research communities, specially for Climate community. These paradigms are modifying the way how climate applications are being executed. By using these technologies the number, variety and complexity of experiments and resources are increasing substantially. But, although computational capacity is increasing, traditional applications and tools used by the community are not good enough to manage this large volume and variety of experiments and computing resources. In this contribution, we evaluate the challenges to run climate simulations and services on Grid, Cloud and HPC infrestructures and how to tackle them. The Grid and Cloud infrastructures provided by EGI's VOs ( esr , earth.vo.ibergrid and fedcloud.egi.eu) will be evaluated, as well as HPC resources from PRACE infrastructure and institutional clusters. To solve those challenges, solutions using DRM4G framework will be shown. DRM4G provides a good framework to manage big volume and variety of computing resources for climate experiments. This work has been supported by the Spanish National R&D Plan under projects WRF4G (CGL2011-28864), INSIGNIA (CGL2016-79210-R) and MULTI-SDM (CGL2015-66583-R) ; the IS-ENES2 project from the 7FP of the European Commission (grant agreement no. 312979); the European Regional Development Fund—ERDF and the Programa de Personal Investigador en Formación Predoctoral from Universidad de Cantabria and Government of Cantabria.

Software Issues in High-Performance Computing and a Framework for the Development of HPC Applications

DTIC Science & Technology

1995-01-01

possible to determine communication points. For this version, a C program spawning Posix threads and using semaphores to synchronize would have to...performance such as the time required for network communication and synchronization as well as issues of asynchrony and memory hierarchy. For example...enhances reusability. Process (or task) parallel computations can also be succinctly expressed with a small set of process creation and synchronization

High Performance Computing Operations Review Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cupps, Kimberly C.

2013-12-19

The High Performance Computing Operations Review (HPCOR) meeting—requested by the ASC and ASCR program headquarters at DOE—was held November 5 and 6, 2013, at the Marriott Hotel in San Francisco, CA. The purpose of the review was to discuss the processes and practices for HPC integration and its related software and facilities. Experiences and lessons learned from the most recent systems deployed were covered in order to benefit the deployment of new systems.

Implementation of NAS Parallel Benchmarks in Java

NASA Technical Reports Server (NTRS)

Frumkin, Michael; Schultz, Matthew; Jin, Hao-Qiang; Yan, Jerry

2000-01-01

A number of features make Java an attractive but a debatable choice for High Performance Computing (HPC). In order to gauge the applicability of Java to the Computational Fluid Dynamics (CFD) we have implemented NAS Parallel Benchmarks in Java. The performance and scalability of the benchmarks point out the areas where improvement in Java compiler technology and in Java thread implementation would move Java closer to Fortran in the competition for CFD applications.

Experimental evaluation of a flexible I/O architecture for accelerating workflow engines in ultrascale environments

DOE PAGES

Duro, Francisco Rodrigo; Blas, Javier Garcia; Isaila, Florin; ...

2016-10-06

The increasing volume of scientific data and the limited scalability and performance of storage systems are currently presenting a significant limitation for the productivity of the scientific workflows running on both high-performance computing (HPC) and cloud platforms. Clearly needed is better integration of storage systems and workflow engines to address this problem. This paper presents and evaluates a novel solution that leverages codesign principles for integrating Hercules—an in-memory data store—with a workflow management system. We consider four main aspects: workflow representation, task scheduling, task placement, and task termination. As a result, the experimental evaluation on both cloud and HPC systemsmore » demonstrates significant performance and scalability improvements over existing state-of-the-art approaches.« less

A Heterogeneous High-Performance System for Computational and Computer Science

DTIC Science & Technology

2016-11-15

Patents Submitted Patents Awarded Awards Graduate Students Names of Post Doctorates Names of Faculty Supported Names of Under Graduate students supported...team of research faculty from the departments of computer science and natural science at Bowie State University. The supercomputer is not only to...accelerated HPC systems. The supercomputer is also ideal for the research conducted in the Department of Natural Science, as research faculty work on

Adaptation of a Multi-Block Structured Solver for Effective Use in a Hybrid CPU/GPU Massively Parallel Environment

NASA Astrophysics Data System (ADS)

Gutzwiller, David; Gontier, Mathieu; Demeulenaere, Alain

2014-11-01

Multi-Block structured solvers hold many advantages over their unstructured counterparts, such as a smaller memory footprint and efficient serial performance. Historically, multi-block structured solvers have not been easily adapted for use in a High Performance Computing (HPC) environment, and the recent trend towards hybrid GPU/CPU architectures has further complicated the situation. This paper will elaborate on developments and innovations applied to the NUMECA FINE/Turbo solver that have allowed near-linear scalability with real-world problems on over 250 hybrid GPU/GPU cluster nodes. Discussion will focus on the implementation of virtual partitioning and load balancing algorithms using a novel meta-block concept. This implementation is transparent to the user, allowing all pre- and post-processing steps to be performed using a simple, unpartitioned grid topology. Additional discussion will elaborate on developments that have improved parallel performance, including fully parallel I/O with the ADIOS API and the GPU porting of the computationally heavy CPUBooster convergence acceleration module. Head of HPC and Release Management, Numeca International.

FLAME: A platform for high performance computing of complex systems, applied for three case studies

DOE PAGES

Kiran, Mariam; Bicak, Mesude; Maleki-Dizaji, Saeedeh; ...

2011-01-01

FLAME allows complex models to be automatically parallelised on High Performance Computing (HPC) grids enabling large number of agents to be simulated over short periods of time. Modellers are hindered by complexities of porting models on parallel platforms and time taken to run large simulations on a single machine, which FLAME overcomes. Three case studies from different disciplines were modelled using FLAME, and are presented along with their performance results on a grid.

Computational Fluid Dynamics Ventilation Study for the Human Powered Centrifuge at the International Space Station

NASA Technical Reports Server (NTRS)

Son, Chang H.

2012-01-01

The Human Powered Centrifuge (HPC) is a facility that is planned to be installed on board the International Space Station (ISS) to enable crew exercises under the artificial gravity conditions. The HPC equipment includes a "bicycle" for long-term exercises of a crewmember that provides power for rotation of HPC at a speed of 30 rpm. The crewmember exercising vigorously on the centrifuge generates the amount of carbon dioxide of about two times higher than a crewmember in ordinary conditions. The goal of the study is to analyze the airflow and carbon dioxide distribution within Pressurized Multipurpose Module (PMM) cabin when HPC is operating. A full unsteady formulation is used for airflow and CO2 transport CFD-based modeling with the so-called sliding mesh concept when the HPC equipment with the adjacent Bay 4 cabin volume is considered in the rotating reference frame while the rest of the cabin volume is considered in the stationary reference frame. The rotating part of the computational domain includes also a human body model. Localized effects of carbon dioxide dispersion are examined. Strong influence of the rotating HPC equipment on the CO2 distribution detected is discussed.

A new deadlock resolution protocol and message matching algorithm for the extreme-scale simulator

DOE PAGES

Engelmann, Christian; Naughton, III, Thomas J.

2016-03-22

Investigating the performance of parallel applications at scale on future high-performance computing (HPC) architectures and the performance impact of different HPC architecture choices is an important component of HPC hardware/software co-design. The Extreme-scale Simulator (xSim) is a simulation toolkit for investigating the performance of parallel applications at scale. xSim scales to millions of simulated Message Passing Interface (MPI) processes. The overhead introduced by a simulation tool is an important performance and productivity aspect. This paper documents two improvements to xSim: (1)~a new deadlock resolution protocol to reduce the parallel discrete event simulation overhead and (2)~a new simulated MPI message matchingmore » algorithm to reduce the oversubscription management overhead. The results clearly show a significant performance improvement. The simulation overhead for running the NAS Parallel Benchmark suite was reduced from 102% to 0% for the embarrassingly parallel (EP) benchmark and from 1,020% to 238% for the conjugate gradient (CG) benchmark. xSim offers a highly accurate simulation mode for better tracking of injected MPI process failures. Furthermore, with highly accurate simulation, the overhead was reduced from 3,332% to 204% for EP and from 37,511% to 13,808% for CG.« less

Performance of high performance concrete (HPC) in low pH and sulfate environment : [technical summary].

DOT National Transportation Integrated Search

2013-01-01

High-performance concrete (HPC) refers to any concrete formulation with enhanced characteristics, compared to normal concrete. One might think this refers to strength, but in Florida, the HPC standard emphasizes withstanding aggressive environments, ...

Leveraging the Power of High Performance Computing for Next Generation Sequencing Data Analysis: Tricks and Twists from a High Throughput Exome Workflow

PubMed Central

Wonczak, Stephan; Thiele, Holger; Nieroda, Lech; Jabbari, Kamel; Borowski, Stefan; Sinha, Vishal; Gunia, Wilfried; Lang, Ulrich; Achter, Viktor; Nürnberg, Peter

2015-01-01

Next generation sequencing (NGS) has been a great success and is now a standard method of research in the life sciences. With this technology, dozens of whole genomes or hundreds of exomes can be sequenced in rather short time, producing huge amounts of data. Complex bioinformatics analyses are required to turn these data into scientific findings. In order to run these analyses fast, automated workflows implemented on high performance computers are state of the art. While providing sufficient compute power and storage to meet the NGS data challenge, high performance computing (HPC) systems require special care when utilized for high throughput processing. This is especially true if the HPC system is shared by different users. Here, stability, robustness and maintainability are as important for automated workflows as speed and throughput. To achieve all of these aims, dedicated solutions have to be developed. In this paper, we present the tricks and twists that we utilized in the implementation of our exome data processing workflow. It may serve as a guideline for other high throughput data analysis projects using a similar infrastructure. The code implementing our solutions is provided in the supporting information files. PMID:25942438

Create full-scale predictive economic models on ROI and innovation with performance computing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Joseph, Earl C.; Conway, Steve

The U.S. Department of Energy (DOE), the world's largest buyer and user of supercomputers, awarded IDC Research, Inc. a grant to create two macroeconomic models capable of quantifying, respectively, financial and non-financial (innovation) returns on investments in HPC resources. Following a 2013 pilot study in which we created the models and tested them on about 200 real-world HPC cases, DOE authorized us to conduct a full-out, three-year grant study to collect and measure many more examples, a process that would also subject the methodology to further testing and validation. A secondary, "stretch" goal of the full-out study was to advancemore » the methodology from association toward (but not all the way to) causation, by eliminating the effects of some of the other factors that might be contributing, along with HPC investments, to the returns produced in the investigated projects.« less

Ventral, but not dorsal, hippocampus inactivation impairs reward memory expression and retrieval in contexts defined by proximal cues.

PubMed

Riaz, Sadia; Schumacher, Anett; Sivagurunathan, Seyon; Van Der Meer, Matthijs; Ito, Rutsuko

2017-07-01

The hippocampus (HPC) has been widely implicated in the contextual control of appetitive and aversive conditioning. However, whole hippocampal lesions do not invariably impair all forms of contextual processing, as in the case of complex biconditional context discrimination, leading to contention over the exact nature of the contribution of the HPC in contextual processing. Moreover, the increasingly well-established functional dissociation between the dorsal (dHPC) and ventral (vHPC) subregions of the HPC has been largely overlooked in the existing literature on hippocampal-based contextual memory processing in appetitively motivated tasks. Thus, the present study sought to investigate the individual roles of the dHPC and the vHPC in contextual biconditional discrimination (CBD) performance and memory retrieval. To this end, we examined the effects of transient post-acquisition pharmacological inactivation (using a combination of GABA A and GABA B receptor agonists muscimol and baclofen) of functionally distinct subregions of the HPC (CA1/CA3 subfields of the dHPC and vHPC) on CBD memory retrieval. Additional behavioral assays including novelty preference, light-dark box and locomotor activity test were also performed to confirm that the respective sites of inactivation were functionally silent. We observed robust deficits in CBD performance and memory retrieval following inactivation of the vHPC, but not the dHPC. Our data provides novel insight into the differential roles of the ventral and dorsal HPC in reward contextual processing, under conditions in which the context is defined by proximal cues. © 2017 Wiley Periodicals, Inc.

High-Bandwidth Tactical-Network Data Analysis in a High-Performance-Computing (HPC) Environment: Transport Protocol (Transmission Control Protocol/User Datagram Protocol [TCP/UDP]) Analysis

DTIC Science & Technology

2015-09-01

the network Mac8 Medium Access Control ( Mac ) (Ethernet) address observed as destination for outgoing packets subsessionid8 Zero-based index of...15. SUBJECT TERMS tactical networks, data reduction, high-performance computing, data analysis, big data 16. SECURITY CLASSIFICATION OF: 17...Integer index of row cts_deid Device (instrument) Identifier where observation took place cts_collpt Collection point or logical observation point on

Comparative performance of conventional OPC concrete and HPC designed by densified mixture design algorithm

NASA Astrophysics Data System (ADS)

Huynh, Trong-Phuoc; Hwang, Chao-Lung; Yang, Shu-Ti

2017-12-01

This experimental study evaluated the performance of normal ordinary Portland cement (OPC) concrete and high-performance concrete (HPC) that were designed by the conventional method (ACI) and densified mixture design algorithm (DMDA) method, respectively. Engineering properties and durability performance of both the OPC and HPC samples were studied using the tests of workability, compressive strength, water absorption, ultrasonic pulse velocity, and electrical surface resistivity. Test results show that the HPC performed good fresh property and further showed better performance in terms of strength and durability as compared to the OPC.

NASA Center for Climate Simulation (NCCS) Advanced Technology AT5 Virtualized Infiniband Report

NASA Technical Reports Server (NTRS)

Thompson, John H.; Bledsoe, Benjamin C.; Wagner, Mark; Shakshober, John; Fromkin, Russ

2013-01-01

The NCCS is part of the Computational and Information Sciences and Technology Office (CISTO) of Goddard Space Flight Center's (GSFC) Sciences and Exploration Directorate. The NCCS's mission is to enable scientists to increase their understanding of the Earth, the solar system, and the universe by supplying state-of-the-art high performance computing (HPC) solutions. To accomplish this mission, the NCCS (https://www.nccs.nasa.gov) provides high performance compute engines, mass storage, and network solutions to meet the specialized needs of the Earth and space science user communities

Sepsis reconsidered: Identifying novel metrics for behavioral landscape characterization with a high-performance computing implementation of an agent-based model.

PubMed

Cockrell, Chase; An, Gary

2017-10-07

Sepsis affects nearly 1 million people in the United States per year, has a mortality rate of 28-50% and requires more than $20 billion a year in hospital costs. Over a quarter century of research has not yielded a single reliable diagnostic test or a directed therapeutic agent for sepsis. Central to this insufficiency is the fact that sepsis remains a clinical/physiological diagnosis representing a multitude of molecularly heterogeneous pathological trajectories. Advances in computational capabilities offered by High Performance Computing (HPC) platforms call for an evolution in the investigation of sepsis to attempt to define the boundaries of traditional research (bench, clinical and computational) through the use of computational proxy models. We present a novel investigatory and analytical approach, derived from how HPC resources and simulation are used in the physical sciences, to identify the epistemic boundary conditions of the study of clinical sepsis via the use of a proxy agent-based model of systemic inflammation. Current predictive models for sepsis use correlative methods that are limited by patient heterogeneity and data sparseness. We address this issue by using an HPC version of a system-level validated agent-based model of sepsis, the Innate Immune Response ABM (IIRBM), as a proxy system in order to identify boundary conditions for the possible behavioral space for sepsis. We then apply advanced analysis derived from the study of Random Dynamical Systems (RDS) to identify novel means for characterizing system behavior and providing insight into the tractability of traditional investigatory methods. The behavior space of the IIRABM was examined by simulating over 70 million sepsis patients for up to 90 days in a sweep across the following parameters: cardio-respiratory-metabolic resilience; microbial invasiveness; microbial toxigenesis; and degree of nosocomial exposure. In addition to using established methods for describing parameter space, we developed two novel methods for characterizing the behavior of a RDS: Probabilistic Basins of Attraction (PBoA) and Stochastic Trajectory Analysis (STA). Computationally generated behavioral landscapes demonstrated attractor structures around stochastic regions of behavior that could be described in a complementary fashion through use of PBoA and STA. The stochasticity of the boundaries of the attractors highlights the challenge for correlative attempts to characterize and classify clinical sepsis. HPC simulations of models like the IIRABM can be used to generate approximations of the behavior space of sepsis to both establish "boundaries of futility" with respect to existing investigatory approaches and apply system engineering principles to investigate the general dynamic properties of sepsis to provide a pathway for developing control strategies. The issues that bedevil the study and treatment of sepsis, namely clinical data sparseness and inadequate experimental sampling of system behavior space, are fundamental to nearly all biomedical research, manifesting in the "Crisis of Reproducibility" at all levels. HPC-augmented simulation-based research offers an investigatory strategy more consistent with that seen in the physical sciences (which combine experiment, theory and simulation), and an opportunity to utilize the leading advances in HPC, namely deep machine learning and evolutionary computing, to form the basis of an iterative scientific process to meet the full promise of Precision Medicine (right drug, right patient, right time). Copyright © 2017. Published by Elsevier Ltd.

Hierarchical parallelisation of functional renormalisation group calculations - hp-fRG

NASA Astrophysics Data System (ADS)

Rohe, Daniel

2016-10-01

The functional renormalisation group (fRG) has evolved into a versatile tool in condensed matter theory for studying important aspects of correlated electron systems. Practical applications of the method often involve a high numerical effort, motivating the question in how far High Performance Computing (HPC) can leverage the approach. In this work we report on a multi-level parallelisation of the underlying computational machinery and show that this can speed up the code by several orders of magnitude. This in turn can extend the applicability of the method to otherwise inaccessible cases. We exploit three levels of parallelisation: Distributed computing by means of Message Passing (MPI), shared-memory computing using OpenMP, and vectorisation by means of SIMD units (single-instruction-multiple-data). Results are provided for two distinct High Performance Computing (HPC) platforms, namely the IBM-based BlueGene/Q system JUQUEEN and an Intel Sandy-Bridge-based development cluster. We discuss how certain issues and obstacles were overcome in the course of adapting the code. Most importantly, we conclude that this vast improvement can actually be accomplished by introducing only moderate changes to the code, such that this strategy may serve as a guideline for other researcher to likewise improve the efficiency of their codes.

Using NERSC High-Performance Computing (HPC) systems for high-energy nuclear physics applications with ALICE

NASA Astrophysics Data System (ADS)

Fasel, Markus

2016-10-01

High-Performance Computing Systems are powerful tools tailored to support large- scale applications that rely on low-latency inter-process communications to run efficiently. By design, these systems often impose constraints on application workflows, such as limited external network connectivity and whole node scheduling, that make more general-purpose computing tasks, such as those commonly found in high-energy nuclear physics applications, more difficult to carry out. In this work, we present a tool designed to simplify access to such complicated environments by handling the common tasks of job submission, software management, and local data management, in a framework that is easily adaptable to the specific requirements of various computing systems. The tool, initially constructed to process stand-alone ALICE simulations for detector and software development, was successfully deployed on the NERSC computing systems, Carver, Hopper and Edison, and is being configured to provide access to the next generation NERSC system, Cori. In this report, we describe the tool and discuss our experience running ALICE applications on NERSC HPC systems. The discussion will include our initial benchmarks of Cori compared to other systems and our attempts to leverage the new capabilities offered with Cori to support data-intensive applications, with a future goal of full integration of such systems into ALICE grid operations.

Near Real-Time Probabilistic Damage Diagnosis Using Surrogate Modeling and High Performance Computing

NASA Technical Reports Server (NTRS)

Warner, James E.; Zubair, Mohammad; Ranjan, Desh

2017-01-01

This work investigates novel approaches to probabilistic damage diagnosis that utilize surrogate modeling and high performance computing (HPC) to achieve substantial computational speedup. Motivated by Digital Twin, a structural health management (SHM) paradigm that integrates vehicle-specific characteristics with continual in-situ damage diagnosis and prognosis, the methods studied herein yield near real-time damage assessments that could enable monitoring of a vehicle's health while it is operating (i.e. online SHM). High-fidelity modeling and uncertainty quantification (UQ), both critical to Digital Twin, are incorporated using finite element method simulations and Bayesian inference, respectively. The crux of the proposed Bayesian diagnosis methods, however, is the reformulation of the numerical sampling algorithms (e.g. Markov chain Monte Carlo) used to generate the resulting probabilistic damage estimates. To this end, three distinct methods are demonstrated for rapid sampling that utilize surrogate modeling and exploit various degrees of parallelism for leveraging HPC. The accuracy and computational efficiency of the methods are compared on the problem of strain-based crack identification in thin plates. While each approach has inherent problem-specific strengths and weaknesses, all approaches are shown to provide accurate probabilistic damage diagnoses and several orders of magnitude computational speedup relative to a baseline Bayesian diagnosis implementation.

Scalable Energy Efficiency with Resilience for High Performance Computing Systems: A Quantitative Methodology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tan, Li; Chen, Zizhong; Song, Shuaiwen

2016-01-18

Energy efficiency and resilience are two crucial challenges for HPC systems to reach exascale. While energy efficiency and resilience issues have been extensively studied individually, little has been done to understand the interplay between energy efficiency and resilience for HPC systems. Decreasing the supply voltage associated with a given operating frequency for processors and other CMOS-based components can significantly reduce power consumption. However, this often raises system failure rates and consequently increases application execution time. In this work, we present an energy saving undervolting approach that leverages the mainstream resilience techniques to tolerate the increased failures caused by undervolting.

Investigating the Interplay between Energy Efficiency and Resilience in High Performance Computing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tan, Li; Song, Shuaiwen; Wu, Panruo

2015-05-29

Energy efficiency and resilience are two crucial challenges for HPC systems to reach exascale. While energy efficiency and resilience issues have been extensively studied individually, little has been done to understand the interplay between energy efficiency and resilience for HPC systems. Decreasing the supply voltage associated with a given operating frequency for processors and other CMOS-based components can significantly reduce power consumption. However, this often raises system failure rates and consequently increases application execution time. In this work, we present an energy saving undervolting approach that leverages the mainstream resilience techniques to tolerate the increased failures caused by undervolting.

Scalable Energy Efficiency with Resilience for High Performance Computing Systems: A Quantitative Methodology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tan, Li; Chen, Zizhong; Song, Shuaiwen Leon

2015-11-16

Energy efficiency and resilience are two crucial challenges for HPC systems to reach exascale. While energy efficiency and resilience issues have been extensively studied individually, little has been done to understand the interplay between energy efficiency and resilience for HPC systems. Decreasing the supply voltage associated with a given operating frequency for processors and other CMOS-based components can significantly reduce power consumption. However, this often raises system failure rates and consequently increases application execution time. In this work, we present an energy saving undervolting approach that leverages the mainstream resilience techniques to tolerate the increased failures caused by undervolting.

Exploiting opportunistic resources for ATLAS with ARC CE and the Event Service

NASA Astrophysics Data System (ADS)

Cameron, D.; Filipčič, A.; Guan, W.; Tsulaia, V.; Walker, R.; Wenaus, T.; ATLAS Collaboration

2017-10-01

With ever-greater computing needs and fixed budgets, big scientific experiments are turning to opportunistic resources as a means to add much-needed extra computing power. These resources can be very different in design from those that comprise the Grid computing of most experiments, therefore exploiting them requires a change in strategy for the experiment. They may be highly restrictive in what can be run or in connections to the outside world, or tolerate opportunistic usage only on condition that tasks may be terminated without warning. The Advanced Resource Connector Computing Element (ARC CE) with its nonintrusive architecture is designed to integrate resources such as High Performance Computing (HPC) systems into a computing Grid. The ATLAS experiment developed the ATLAS Event Service (AES) primarily to address the issue of jobs that can be terminated at any point when opportunistic computing capacity is needed by someone else. This paper describes the integration of these two systems in order to exploit opportunistic resources for ATLAS in a restrictive environment. In addition to the technical details, results from deployment of this solution in the SuperMUC HPC centre in Munich are shown.

Facilitating Co-Design for Extreme-Scale Systems Through Lightweight Simulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Engelmann, Christian; Lauer, Frank

This work focuses on tools for investigating algorithm performance at extreme scale with millions of concurrent threads and for evaluating the impact of future architecture choices to facilitate the co-design of high-performance computing (HPC) architectures and applications. The approach focuses on lightweight simulation of extreme-scale HPC systems with the needed amount of accuracy. The prototype presented in this paper is able to provide this capability using a parallel discrete event simulation (PDES), such that a Message Passing Interface (MPI) application can be executed at extreme scale, and its performance properties can be evaluated. The results of an initial prototype aremore » encouraging as a simple 'hello world' MPI program could be scaled up to 1,048,576 virtual MPI processes on a four-node cluster, and the performance properties of two MPI programs could be evaluated at up to 16,384 virtual MPI processes on the same system.« less

Integrating the Apache Big Data Stack with HPC for Big Data

NASA Astrophysics Data System (ADS)

Fox, G. C.; Qiu, J.; Jha, S.

2014-12-01

There is perhaps a broad consensus as to important issues in practical parallel computing as applied to large scale simulations; this is reflected in supercomputer architectures, algorithms, libraries, languages, compilers and best practice for application development. However, the same is not so true for data intensive computing, even though commercially clouds devote much more resources to data analytics than supercomputers devote to simulations. We look at a sample of over 50 big data applications to identify characteristics of data intensive applications and to deduce needed runtime and architectures. We suggest a big data version of the famous Berkeley dwarfs and NAS parallel benchmarks and use these to identify a few key classes of hardware/software architectures. Our analysis builds on combining HPC and ABDS the Apache big data software stack that is well used in modern cloud computing. Initial results on clouds and HPC systems are encouraging. We propose the development of SPIDAL - Scalable Parallel Interoperable Data Analytics Library -- built on system aand data abstractions suggested by the HPC-ABDS architecture. We discuss how it can be used in several application areas including Polar Science.

Scalability improvements to NRLMOL for DFT calculations of large molecules

NASA Astrophysics Data System (ADS)

Diaz, Carlos Manuel

Advances in high performance computing (HPC) have provided a way to treat large, computationally demanding tasks using thousands of processors. With the development of more powerful HPC architectures, the need to create efficient and scalable code has grown more important. Electronic structure calculations are valuable in understanding experimental observations and are routinely used for new materials predictions. For the electronic structure calculations, the memory and computation time are proportional to the number of atoms. Memory requirements for these calculations scale as N2, where N is the number of atoms. While the recent advances in HPC offer platforms with large numbers of cores, the limited amount of memory available on a given node and poor scalability of the electronic structure code hinder their efficient usage of these platforms. This thesis will present some developments to overcome these bottlenecks in order to study large systems. These developments, which are implemented in the NRLMOL electronic structure code, involve the use of sparse matrix storage formats and the use of linear algebra using sparse and distributed matrices. These developments along with other related development now allow ground state density functional calculations using up to 25,000 basis functions and the excited state calculations using up to 17,000 basis functions while utilizing all cores on a node. An example on a light-harvesting triad molecule is described. Finally, future plans to further improve the scalability will be presented.

DOE Office of Scientific and Technical Information (OSTI.GOV)

McCaskey, Alexander J.

There is a lack of state-of-the-art HPC simulation tools for simulating general quantum computing. Furthermore, there are no real software tools that integrate current quantum computers into existing classical HPC workflows. This product, the Quantum Virtual Machine (QVM), solves this problem by providing an extensible framework for pluggable virtual, or physical, quantum processing units (QPUs). It enables the execution of low level quantum assembly codes and returns the results of such executions.

Advances in Parallel Computing and Databases for Digital Pathology in Cancer Research

DTIC Science & Technology

2016-11-13

these technologies and how we have used them in the past. We are interested in learning more about the needs of clinical pathologists as we continue to...such as image processing and correlation. Further, High Performance Computing (HPC) paradigms such as the Message Passing Interface (MPI) have been...Defense for Research and Engineering. such as pMatlab [4], or bcMPI [5] can significantly reduce the need for deep knowledge of parallel computing. In

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aderholdt, Ferrol; Caldwell, Blake A.; Hicks, Susan Elaine

High performance computing environments are often used for a wide variety of workloads ranging from simulation, data transformation and analysis, and complex workflows to name just a few. These systems may process data at various security levels but in so doing are often enclaved at the highest security posture. This approach places significant restrictions on the users of the system even when processing data at a lower security level and exposes data at higher levels of confidentiality to a much broader population than otherwise necessary. The traditional approach of isolation, while effective in establishing security enclaves poses significant challenges formore » the use of shared infrastructure in HPC environments. This report details current state-of-the-art in reconfigurable network enclaving through Software Defined Networking (SDN) and Network Function Virtualization (NFV) and their applicability to secure enclaves in HPC environments. SDN and NFV methods are based on a solid foundation of system wide virtualization. The purpose of which is very straight forward, the system administrator can deploy networks that are more amenable to customer needs, and at the same time achieve increased scalability making it easier to increase overall capacity as needed without negatively affecting functionality. The network administration of both the server system and the virtual sub-systems is simplified allowing control of the infrastructure through well-defined APIs (Application Programming Interface). While SDN and NFV technologies offer significant promise in meeting these goals, they also provide the ability to address a significant component of the multi-tenant challenge in HPC environments, namely resource isolation. Traditional HPC systems are built upon scalable high-performance networking technologies designed to meet specific application requirements. Dynamic isolation of resources within these environments has remained difficult to achieve. SDN and NFV methodology provide us with relevant concepts and available open standards based APIs that isolate compute and storage resources within an otherwise common networking infrastructure. Additionally, the integration of the networking APIs within larger system frameworks such as OpenStack provide the tools necessary to establish isolated enclaves dynamically allowing the benefits of HPC while providing a controlled security structure surrounding these systems.« less

GridKit

DOE Office of Scientific and Technical Information (OSTI.GOV)

Peles, Slaven

2016-11-06

GridKit is a software development kit for interfacing power systems and power grid application software with high performance computing (HPC) libraries developed at National Labs and academia. It is also intended as interoperability layer between different numerical libraries. GridKit is not a standalone application, but comes with a suite of test examples illustrating possible usage.

Programming for 1.6 Millon cores: Early experiences with IBM's BG/Q SMP architecture

NASA Astrophysics Data System (ADS)

Glosli, James

2013-03-01

With the stall in clock cycle improvements a decade ago, the drive for computational performance has continues along a path of increasing core counts on a processor. The multi-core evolution has been expressed in both a symmetric multi processor (SMP) architecture and cpu/GPU architecture. Debates rage in the high performance computing (HPC) community which architecture best serves HPC. In this talk I will not attempt to resolve that debate but perhaps fuel it. I will discuss the experience of exploiting Sequoia, a 98304 node IBM Blue Gene/Q SMP at Lawrence Livermore National Laboratory. The advantages and challenges of leveraging the computational power BG/Q will be detailed through the discussion of two applications. The first application is a Molecular Dynamics code called ddcMD. This is a code developed over the last decade at LLNL and ported to BG/Q. The second application is a cardiac modeling code called Cardioid. This is a code that was recently designed and developed at LLNL to exploit the fine scale parallelism of BG/Q's SMP architecture. Through the lenses of these efforts I'll illustrate the need to rethink how we express and implement our computational approaches. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

An Approach to Integrate a Space-Time GIS Data Model with High Performance Computers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Dali; Zhao, Ziliang; Shaw, Shih-Lung

2011-01-01

In this paper, we describe an approach to integrate a Space-Time GIS data model on a high performance computing platform. The Space-Time GIS data model has been developed on a desktop computing environment. We use the Space-Time GIS data model to generate GIS module, which organizes a series of remote sensing data. We are in the process of porting the GIS module into an HPC environment, in which the GIS modules handle large dataset directly via parallel file system. Although it is an ongoing project, authors hope this effort can inspire further discussions on the integration of GIS on highmore » performance computing platforms.« less

Quantifying effectiveness of failure prediction and response in HPC systems : methodology and example.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mayo, Jackson R.; Chen, Frank Xiaoxiao; Pebay, Philippe Pierre

2010-06-01

Effective failure prediction and mitigation strategies in high-performance computing systems could provide huge gains in resilience of tightly coupled large-scale scientific codes. These gains would come from prediction-directed process migration and resource servicing, intelligent resource allocation, and checkpointing driven by failure predictors rather than at regular intervals based on nominal mean time to failure. Given probabilistic associations of outlier behavior in hardware-related metrics with eventual failure in hardware, system software, and/or applications, this paper explores approaches for quantifying the effects of prediction and mitigation strategies and demonstrates these using actual production system data. We describe context-relevant methodologies for determining themore » accuracy and cost-benefit of predictors. While many research studies have quantified the expected impact of growing system size, and the associated shortened mean time to failure (MTTF), on application performance in large-scale high-performance computing (HPC) platforms, there has been little if any work to quantify the possible gains from predicting system resource failures with significant but imperfect accuracy. This possibly stems from HPC system complexity and the fact that, to date, no one has established any good predictors of failure in these systems. Our work in the OVIS project aims to discover these predictors via a variety of data collection techniques and statistical analysis methods that yield probabilistic predictions. The question then is, 'How good or useful are these predictions?' We investigate methods for answering this question in a general setting, and illustrate them using a specific failure predictor discovered on a production system at Sandia.« less

Self-desiccation mechanism of high-performance concrete.

PubMed

Yang, Quan-Bing; Zhang, Shu-Qing

2004-12-01

Investigations on the effects of W/C ratio and silica fume on the autogenous shrinkage and internal relative humidity of high performance concrete (HPC), and analysis of the self-desiccation mechanisms of HPC showed that the autogenous shrinkage and internal relative humidity of HPC increases and decreases with the reduction of W/C respectively; and that these phenomena were amplified by the addition of silica fume. Theoretical analyses indicated that the reduction of RH in HPC was not due to shortage of water, but due to the fact that the evaporable water in HPC was not evaporated freely. The reduction of internal relative humidity or the so-called self-desiccation of HPC was chiefly caused by the increase in mole concentration of soluble ions in HPC and the reduction of pore size or the increase in the fraction of micro-pore water in the total evaporable water (T(r)/T(te) ratio).

Avi Purkayastha | NREL

Science.gov Websites

Austin, from 2001 to 2007. There he was principal in HPC applications and user support, as well as in research and development in large-scale scientific applications and different HPC systems and technologies Interests HPC applications performance and optimizations|HPC systems and accelerator technologies|Scientific

Computational challenges in atomic, molecular and optical physics.

PubMed

Taylor, Kenneth T

2002-06-15

Six challenges are discussed. These are the laser-driven helium atom; the laser-driven hydrogen molecule and hydrogen molecular ion; electron scattering (with ionization) from one-electron atoms; the vibrational and rotational structure of molecules such as H(3)(+) and water at their dissociation limits; laser-heated clusters; and quantum degeneracy and Bose-Einstein condensation. The first four concern fundamental few-body systems where use of high-performance computing (HPC) is currently making possible accurate modelling from first principles. This leads to reliable predictions and support for laboratory experiment as well as true understanding of the dynamics. Important aspects of these challenges addressable only via a terascale facility are set out. Such a facility makes the last two challenges in the above list meaningfully accessible for the first time, and the scientific interest together with the prospective role for HPC in these is emphasized.

Experience Paper: Software Engineering and Community Codes Track in ATPESC

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dubey, Anshu; Riley, Katherine M.

Argonne Training Program in Extreme Scale Computing (ATPESC) was started by the Argonne National Laboratory with the objective of expanding the ranks of better prepared users of high performance computing (HPC) machines. One of the unique aspects of the program was inclusion of software engineering and community codes track. The inclusion was motivated by the observation that the projects with a good scientific and software process were better able to meet their scientific goals. In this paper we present our experience of running the software track from the beginning of the program until now. We discuss the motivations, the reception,more » and the evolution of the track over the years. We welcome discussion and input from the community to enhance the track in ATPESC, and also to facilitate inclusion of similar tracks in other HPC oriented training programs.« less

The OptIPuter microscopy demonstrator: enabling science through a transatlantic lightpath

PubMed Central

Ellisman, M.; Hutton, T.; Kirkland, A.; Lin, A.; Lin, C.; Molina, T.; Peltier, S.; Singh, R.; Tang, K.; Trefethen, A.E.; Wallom, D.C.H.; Xiong, X.

2009-01-01

The OptIPuter microscopy demonstrator project has been designed to enable concurrent and remote usage of world-class electron microscopes located in Oxford and San Diego. The project has constructed a network consisting of microscopes and computational and data resources that are all connected by a dedicated network infrastructure using the UK Lightpath and US Starlight systems. Key science drivers include examples from both materials and biological science. The resulting system is now a permanent link between the Oxford and San Diego microscopy centres. This will form the basis of further projects between the sites and expansion of the types of systems that can be remotely controlled, including optical, as well as electron, microscopy. Other improvements will include the updating of the Microsoft cluster software to the high performance computing (HPC) server 2008, which includes the HPC basic profile implementation that will enable the development of interoperable clients. PMID:19487201

The OptIPuter microscopy demonstrator: enabling science through a transatlantic lightpath.

PubMed

Ellisman, M; Hutton, T; Kirkland, A; Lin, A; Lin, C; Molina, T; Peltier, S; Singh, R; Tang, K; Trefethen, A E; Wallom, D C H; Xiong, X

2009-07-13

The OptIPuter microscopy demonstrator project has been designed to enable concurrent and remote usage of world-class electron microscopes located in Oxford and San Diego. The project has constructed a network consisting of microscopes and computational and data resources that are all connected by a dedicated network infrastructure using the UK Lightpath and US Starlight systems. Key science drivers include examples from both materials and biological science. The resulting system is now a permanent link between the Oxford and San Diego microscopy centres. This will form the basis of further projects between the sites and expansion of the types of systems that can be remotely controlled, including optical, as well as electron, microscopy. Other improvements will include the updating of the Microsoft cluster software to the high performance computing (HPC) server 2008, which includes the HPC basic profile implementation that will enable the development of interoperable clients.

Computational Aspects of Data Assimilation and the ESMF

NASA Technical Reports Server (NTRS)

daSilva, A.

2003-01-01

The scientific challenge of developing advanced data assimilation applications is a daunting task. Independently developed components may have incompatible interfaces or may be written in different computer languages. The high-performance computer (HPC) platforms required by numerically intensive Earth system applications are complex, varied, rapidly evolving and multi-part systems themselves. Since the market for high-end platforms is relatively small, there is little robust middleware available to buffer the modeler from the difficulties of HPC programming. To complicate matters further, the collaborations required to develop large Earth system applications often span initiatives, institutions and agencies, involve geoscience, software engineering, and computer science communities, and cross national borders.The Earth System Modeling Framework (ESMF) project is a concerted response to these challenges. Its goal is to increase software reuse, interoperability, ease of use and performance in Earth system models through the use of a common software framework, developed in an open manner by leaders in the modeling community. The ESMF addresses the technical and to some extent the cultural - aspects of Earth system modeling, laying the groundwork for addressing the more difficult scientific aspects, such as the physical compatibility of components, in the future. In this talk we will discuss the general philosophy and architecture of the ESMF, focussing on those capabilities useful for developing advanced data assimilation applications.

Cloud CPFP: a shotgun proteomics data analysis pipeline using cloud and high performance computing.

PubMed

Trudgian, David C; Mirzaei, Hamid

2012-12-07

We have extended the functionality of the Central Proteomics Facilities Pipeline (CPFP) to allow use of remote cloud and high performance computing (HPC) resources for shotgun proteomics data processing. CPFP has been modified to include modular local and remote scheduling for data processing jobs. The pipeline can now be run on a single PC or server, a local cluster, a remote HPC cluster, and/or the Amazon Web Services (AWS) cloud. We provide public images that allow easy deployment of CPFP in its entirety in the AWS cloud. This significantly reduces the effort necessary to use the software, and allows proteomics laboratories to pay for compute time ad hoc, rather than obtaining and maintaining expensive local server clusters. Alternatively the Amazon cloud can be used to increase the throughput of a local installation of CPFP as necessary. We demonstrate that cloud CPFP allows users to process data at higher speed than local installations but with similar cost and lower staff requirements. In addition to the computational improvements, the web interface to CPFP is simplified, and other functionalities are enhanced. The software is under active development at two leading institutions and continues to be released under an open-source license at http://cpfp.sourceforge.net.

High Performance Concrete in Washington State SR 18/SR 516 Overcrossing: Interim Report on Girder Monitoring

DOT National Transportation Integrated Search

2000-04-01

In the mid 1990s the Federal Highway Administration (FHWA) established a High Performance Concrete (HPC) program aimed at demonstrating the positive effects of utilizing HPC in bridges. Research on the benefits of using HPC for bridges has shown a nu...

High performance concrete in Washington state SR 18/SR 516 overcrossing : interim report on materials tests

DOT National Transportation Integrated Search

2000-04-01

In the mid 1990s the Federal Highway Administration (FHWA) established a High Performance Concrete (HPC) program aimed at demonstrating the positive effects of utilizing HPC in bridges. Research on the benefits of using HPC for bridges has shown a nu...

OpenTopography: Addressing Big Data Challenges Using Cloud Computing, HPC, and Data Analytics

NASA Astrophysics Data System (ADS)

Crosby, C. J.; Nandigam, V.; Phan, M.; Youn, C.; Baru, C.; Arrowsmith, R.

2014-12-01

OpenTopography (OT) is a geoinformatics-based data facility initiated in 2009 for democratizing access to high-resolution topographic data, derived products, and tools. Hosted at the San Diego Supercomputer Center (SDSC), OT utilizes cyberinfrastructure, including large-scale data management, high-performance computing, and service-oriented architectures to provide efficient Web based access to large, high-resolution topographic datasets. OT collocates data with processing tools to enable users to quickly access custom data and derived products for their application. OT's ongoing R&D efforts aim to solve emerging technical challenges associated with exponential growth in data, higher order data products, as well as user base. Optimization of data management strategies can be informed by a comprehensive set of OT user access metrics that allows us to better understand usage patterns with respect to the data. By analyzing the spatiotemporal access patterns within the datasets, we can map areas of the data archive that are highly active (hot) versus the ones that are rarely accessed (cold). This enables us to architect a tiered storage environment consisting of high performance disk storage (SSD) for the hot areas and less expensive slower disk for the cold ones, thereby optimizing price to performance. From a compute perspective, OT is looking at cloud based solutions such as the Microsoft Azure platform to handle sudden increases in load. An OT virtual machine image in Microsoft's VM Depot can be invoked and deployed quickly in response to increased system demand. OT has also integrated SDSC HPC systems like the Gordon supercomputer into our infrastructure tier to enable compute intensive workloads like parallel computation of hydrologic routing on high resolution topography. This capability also allows OT to scale to HPC resources during high loads to meet user demand and provide more efficient processing. With a growing user base and maturing scientific user community comes new requests for algorithms and processing capabilities. To address this demand, OT is developing an extensible service based architecture for integrating community-developed software. This "plugable" approach to Web service deployment will enable new processing and analysis tools to run collocated with OT hosted data.

High Energy Physics Exascale Requirements Review. An Office of Science review sponsored jointly by Advanced Scientific Computing Research and High Energy Physics, June 10-12, 2015, Bethesda, Maryland

DOE Office of Scientific and Technical Information (OSTI.GOV)

Habib, Salman; Roser, Robert; Gerber, Richard

The U.S. Department of Energy (DOE) Office of Science (SC) Offices of High Energy Physics (HEP) and Advanced Scientific Computing Research (ASCR) convened a programmatic Exascale Requirements Review on June 10–12, 2015, in Bethesda, Maryland. This report summarizes the findings, results, and recommendations derived from that meeting. The high-level findings and observations are as follows. Larger, more capable computing and data facilities are needed to support HEP science goals in all three frontiers: Energy, Intensity, and Cosmic. The expected scale of the demand at the 2025 timescale is at least two orders of magnitude — and in some cases greatermore » — than that available currently. The growth rate of data produced by simulations is overwhelming the current ability of both facilities and researchers to store and analyze it. Additional resources and new techniques for data analysis are urgently needed. Data rates and volumes from experimental facilities are also straining the current HEP infrastructure in its ability to store and analyze large and complex data volumes. Appropriately configured leadership-class facilities can play a transformational role in enabling scientific discovery from these datasets. A close integration of high-performance computing (HPC) simulation and data analysis will greatly aid in interpreting the results of HEP experiments. Such an integration will minimize data movement and facilitate interdependent workflows. Long-range planning between HEP and ASCR will be required to meet HEP’s research needs. To best use ASCR HPC resources, the experimental HEP program needs (1) an established, long-term plan for access to ASCR computational and data resources, (2) the ability to map workflows to HPC resources, (3) the ability for ASCR facilities to accommodate workflows run by collaborations potentially comprising thousands of individual members, (4) to transition codes to the next-generation HPC platforms that will be available at ASCR facilities, (5) to build up and train a workforce capable of developing and using simulations and analysis to support HEP scientific research on next-generation systems.« less

Towards Test Driven Development for Computational Science with pFUnit

NASA Technical Reports Server (NTRS)

Rilee, Michael L.; Clune, Thomas L.

2014-01-01

Developers working in Computational Science & Engineering (CSE)/High Performance Computing (HPC) must contend with constant change due to advances in computing technology and science. Test Driven Development (TDD) is a methodology that mitigates software development risks due to change at the cost of adding comprehensive and continuous testing to the development process. Testing frameworks tailored for CSE/HPC, like pFUnit, can lower the barriers to such testing, yet CSE software faces unique constraints foreign to the broader software engineering community. Effective testing of numerical software requires a comprehensive suite of oracles, i.e., use cases with known answers, as well as robust estimates for the unavoidable numerical errors associated with implementation with finite-precision arithmetic. At first glance these concerns often seem exceedingly challenging or even insurmountable for real-world scientific applications. However, we argue that this common perception is incorrect and driven by (1) a conflation between model validation and software verification and (2) the general tendency in the scientific community to develop relatively coarse-grained, large procedures that compound numerous algorithmic steps.We believe TDD can be applied routinely to numerical software if developers pursue fine-grained implementations that permit testing, neatly side-stepping concerns about needing nontrivial oracles as well as the accumulation of errors. We present an example of a successful, complex legacy CSE/HPC code whose development process shares some aspects with TDD, which we contrast with current and potential capabilities. A mix of our proposed methodology and framework support should enable everyday use of TDD by CSE-expert developers.

BOWS (bioinformatics open web services) to centralize bioinformatics tools in web services.

PubMed

Velloso, Henrique; Vialle, Ricardo A; Ortega, J Miguel

2015-06-02

Bioinformaticians face a range of difficulties to get locally-installed tools running and producing results; they would greatly benefit from a system that could centralize most of the tools, using an easy interface for input and output. Web services, due to their universal nature and widely known interface, constitute a very good option to achieve this goal. Bioinformatics open web services (BOWS) is a system based on generic web services produced to allow programmatic access to applications running on high-performance computing (HPC) clusters. BOWS intermediates the access to registered tools by providing front-end and back-end web services. Programmers can install applications in HPC clusters in any programming language and use the back-end service to check for new jobs and their parameters, and then to send the results to BOWS. Programs running in simple computers consume the BOWS front-end service to submit new processes and read results. BOWS compiles Java clients, which encapsulate the front-end web service requisitions, and automatically creates a web page that disposes the registered applications and clients. Bioinformatics open web services registered applications can be accessed from virtually any programming language through web services, or using standard java clients. The back-end can run in HPC clusters, allowing bioinformaticians to remotely run high-processing demand applications directly from their machines.

A high performance scientific cloud computing environment for materials simulations

NASA Astrophysics Data System (ADS)

Jorissen, K.; Vila, F. D.; Rehr, J. J.

2012-09-01

We describe the development of a scientific cloud computing (SCC) platform that offers high performance computation capability. The platform consists of a scientific virtual machine prototype containing a UNIX operating system and several materials science codes, together with essential interface tools (an SCC toolset) that offers functionality comparable to local compute clusters. In particular, our SCC toolset provides automatic creation of virtual clusters for parallel computing, including tools for execution and monitoring performance, as well as efficient I/O utilities that enable seamless connections to and from the cloud. Our SCC platform is optimized for the Amazon Elastic Compute Cloud (EC2). We present benchmarks for prototypical scientific applications and demonstrate performance comparable to local compute clusters. To facilitate code execution and provide user-friendly access, we have also integrated cloud computing capability in a JAVA-based GUI. Our SCC platform may be an alternative to traditional HPC resources for materials science or quantum chemistry applications.

Data Retention Policy | High-Performance Computing | NREL

Science.gov Websites

HPC Data Retention Policy. File storage areas on Peregrine and Gyrfalcon are either user-centric to reclaim storage. We can make special arrangements for permanent storage, if needed. User-Centric > is 3 months after the last project ends. During this retention period, the user may log in to

Peregrine Software Toolchains | High-Performance Computing | NREL

Science.gov Websites

toolchain is an open-source alternative against which many technical applications are natively developed and tested. The Portland Group compilers are not fully supported, but are available to the HPC community. Use Group (PGI) C/C++ and Fortran (partially supported) The PGI Accelerator compilers include NVIDIA GPU

High Performance Computing and Storage Requirements for Nuclear Physics: Target 2017

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gerber, Richard; Wasserman, Harvey

2014-04-30

In April 2014, NERSC, ASCR, and the DOE Office of Nuclear Physics (NP) held a review to characterize high performance computing (HPC) and storage requirements for NP research through 2017. This review is the 12th in a series of reviews held by NERSC and Office of Science program offices that began in 2009. It is the second for NP, and the final in the second round of reviews that covered the six Office of Science program offices. This report is the result of that review

Evolving Storage and Cyber Infrastructure at the NASA Center for Climate Simulation

NASA Technical Reports Server (NTRS)

Salmon, Ellen; Duffy, Daniel; Spear, Carrie; Sinno, Scott; Vaughan, Garrison; Bowen, Michael

2018-01-01

This talk will describe recent developments at the NASA Center for Climate Simulation, which is funded by NASAs Science Mission Directorate, and supports the specialized data storage and computational needs of weather, ocean, and climate researchers, as well as astrophysicists, heliophysicists, and planetary scientists. To meet requirements for higher-resolution, higher-fidelity simulations, the NCCS augments its High Performance Computing (HPC) and storage retrieval environment. As the petabytes of model and observational data grow, the NCCS is broadening data services offerings and deploying and expanding virtualization resources for high performance analytics.

Directional hippocampal-prefrontal interactions during working memory.

PubMed

Liu, Tiaotiao; Bai, Wenwen; Xia, Mi; Tian, Xin

2018-02-15

Working memory refers to a system that is essential for performing complex cognitive tasks such as reasoning, comprehension and learning. Evidence shows that hippocampus (HPC) and prefrontal cortex (PFC) play important roles in working memory. The HPC-PFC interaction via theta-band oscillatory synchronization is critical for successful execution of working memory. However, whether one brain region is leading or lagging relative to another is still unclear. Therefore, in the present study, we simultaneously recorded local field potentials (LFPs) from rat ventral hippocampus (vHPC) and medial prefrontal cortex (mPFC) and while the rats performed a Y-maze working memory task. We then applied instantaneous amplitudes cross-correlation method to calculate the time lag between PFC and vHPC to explore the functional dynamics of the HPC-PFC interaction. Our results showed a strong lead from vHPC to mPFC preceded an animal's correct choice during the working memory task. These findings suggest the vHPC-leading interaction contributes to the successful execution of working memory. Copyright © 2017. Published by Elsevier B.V.

Assessment of current cybersecurity practices in the public domain : cyber indications and warnings domain.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hamlet, Jason R.; Keliiaa, Curtis M.

This report assesses current public domain cyber security practices with respect to cyber indications and warnings. It describes cybersecurity industry and government activities, including cybersecurity tools, methods, practices, and international and government-wide initiatives known to be impacting current practice. Of particular note are the U.S. Government's Trusted Internet Connection (TIC) and 'Einstein' programs, which are serving to consolidate the Government's internet access points and to provide some capability to monitor and mitigate cyber attacks. Next, this report catalogs activities undertaken by various industry and government entities. In addition, it assesses the benchmarks of HPC capability and other HPC attributes thatmore » may lend themselves to assist in the solution of this problem. This report draws few conclusions, as it is intended to assess current practice in preparation for future work, however, no explicit references to HPC usage for the purpose of analyzing cyber infrastructure in near-real-time were found in the current practice. This report and a related SAND2010-4766 National Cyber Defense High Performance Computing and Analysis: Concepts, Planning and Roadmap report are intended to provoke discussion throughout a broad audience about developing a cohesive HPC centric solution to wide-area cybersecurity problems.« less

National Energy Research Scientific Computing Center

Science.gov Websites

Overview NERSC Mission Contact us Staff Org Chart NERSC History NERSC Stakeholders Usage and User HPC Requirements Reviews NERSC HPC Achievement Awards User Submitted Research Citations NERSC User data archive NERSC Resources Table For Users Live Status User Announcements My NERSC Getting Started

Seismic waveform modeling over cloud

NASA Astrophysics Data System (ADS)

Luo, Cong; Friederich, Wolfgang

2016-04-01

With the fast growing computational technologies, numerical simulation of seismic wave propagation achieved huge successes. Obtaining the synthetic waveforms through numerical simulation receives an increasing amount of attention from seismologists. However, computational seismology is a data-intensive research field, and the numerical packages usually come with a steep learning curve. Users are expected to master considerable amount of computer knowledge and data processing skills. Training users to use the numerical packages, correctly access and utilize the computational resources is a troubled task. In addition to that, accessing to HPC is also a common difficulty for many users. To solve these problems, a cloud based solution dedicated on shallow seismic waveform modeling has been developed with the state-of-the-art web technologies. It is a web platform integrating both software and hardware with multilayer architecture: a well designed SQL database serves as the data layer, HPC and dedicated pipeline for it is the business layer. Through this platform, users will no longer need to compile and manipulate various packages on the local machine within local network to perform a simulation. By providing users professional access to the computational code through its interfaces and delivering our computational resources to the users over cloud, users can customize the simulation at expert-level, submit and run the job through it.

Final Report Scalable Analysis Methods and In Situ Infrastructure for Extreme Scale Knowledge Discovery

DOE Office of Scientific and Technical Information (OSTI.GOV)

O'Leary, Patrick

The primary challenge motivating this project is the widening gap between the ability to compute information and to store it for subsequent analysis. This gap adversely impacts science code teams, who can perform analysis only on a small fraction of the data they calculate, resulting in the substantial likelihood of lost or missed science, when results are computed but not analyzed. Our approach is to perform as much analysis or visualization processing on data while it is still resident in memory, which is known as in situ processing. The idea in situ processing was not new at the time ofmore » the start of this effort in 2014, but efforts in that space were largely ad hoc, and there was no concerted effort within the research community that aimed to foster production-quality software tools suitable for use by Department of Energy (DOE) science projects. Our objective was to produce and enable the use of production-quality in situ methods and infrastructure, at scale, on DOE high-performance computing (HPC) facilities, though we expected to have an impact beyond DOE due to the widespread nature of the challenges, which affect virtually all large-scale computational science efforts. To achieve this objective, we engaged in software technology research and development (R&D), in close partnerships with DOE science code teams, to produce software technologies that were shown to run efficiently at scale on DOE HPC platforms.« less

Towards real-time remote processing of laparoscopic video

NASA Astrophysics Data System (ADS)

Ronaghi, Zahra; Duffy, Edward B.; Kwartowitz, David M.

2015-03-01

Laparoscopic surgery is a minimally invasive surgical technique where surgeons insert a small video camera into the patient's body to visualize internal organs and small tools to perform surgical procedures. However, the benefit of small incisions has a drawback of limited visualization of subsurface tissues, which can lead to navigational challenges in the delivering of therapy. Image-guided surgery (IGS) uses images to map subsurface structures and can reduce the limitations of laparoscopic surgery. One particular laparoscopic camera system of interest is the vision system of the daVinci-Si robotic surgical system (Intuitive Surgical, Sunnyvale, CA, USA). The video streams generate approximately 360 megabytes of data per second, demonstrating a trend towards increased data sizes in medicine, primarily due to higher-resolution video cameras and imaging equipment. Processing this data on a bedside PC has become challenging and a high-performance computing (HPC) environment may not always be available at the point of care. To process this data on remote HPC clusters at the typical 30 frames per second (fps) rate, it is required that each 11.9 MB video frame be processed by a server and returned within 1/30th of a second. The ability to acquire, process and visualize data in real-time is essential for performance of complex tasks as well as minimizing risk to the patient. As a result, utilizing high-speed networks to access computing clusters will lead to real-time medical image processing and improve surgical experiences by providing real-time augmented laparoscopic data. We aim to develop a medical video processing system using an OpenFlow software defined network that is capable of connecting to multiple remote medical facilities and HPC servers.

Mining Software Usage with the Automatic Library Tracking Database (ALTD)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hadri, Bilel; Fahey, Mark R

2013-01-01

Tracking software usage is important for HPC centers, computer vendors, code developers and funding agencies to provide more efficient and targeted software support, and to forecast needs and guide HPC software effort towards the Exascale era. However, accurately tracking software usage on HPC systems has been a challenging task. In this paper, we present a tool called Automatic Library Tracking Database (ALTD) that has been developed and put in production on several Cray systems. The ALTD infrastructure prototype automatically and transparently stores information about libraries linked into an application at compilation time and also the executables launched in a batchmore » job. We will illustrate the usage of libraries, compilers and third party software applications on a system managed by the National Institute for Computational Sciences.« less

Intracranial meningeal hemangiopericytoma: Recurrences at the initial and distant intracranial sites and extraneural metastases to multiple organs

PubMed Central

WEI, GUANGQUAN; KANG, XIAOWEI; LIU, XIANPING; TANG, XING; LI, QINLONG; HAN, JUNTAO; YIN, HONG

2015-01-01

Regardless of the controversial pathogenesis, intracranial meningeal hemangiopericytoma (M-HPC) is a rare, highly cellular and vascularized mesenchymal tumor that is characterized by a high tendency for recurrence and extraneural metastasis, despite radical excision and postoperative radiotherapy. M-HPC shares similar clinical manifestations and radiological findings with meningioma, which causes difficulty in differentiation of this entity from those prognostically favorable mimics prior to surgery. Treatment of M-HPC, particularly in metastatic settings, remains a challenge. A case is described of primary M-HPC with recurrence at the initial and distant intracranial sites and extraneural multiple-organ metastases in a 36-year-old female. The metastasis of M-HPC was extremely extensive, and to the best of our knowledge this is the first case of M-HPC with delayed metastasis to the bilateral kidneys. The data suggests that preoperative computed tomography and magnetic resonance imaging could provide certain diagnostic clues and useful information for more optimal treatment planning. The results may imply that novel drugs, such as temozolomide and bevacizumab, as a component of multimodality therapy of M-HPC may deserve further investigation. PMID:26171177

Implementation of BT, SP, LU, and FT of NAS Parallel Benchmarks in Java

NASA Technical Reports Server (NTRS)

Schultz, Matthew; Frumkin, Michael; Jin, Hao-Qiang; Yan, Jerry

2000-01-01

A number of Java features make it an attractive but a debatable choice for High Performance Computing. We have implemented benchmarks working on single structured grid BT,SP,LU and FT in Java. The performance and scalability of the Java code shows that a significant improvement in Java compiler technology and in Java thread implementation are necessary for Java to compete with Fortran in HPC applications.

Position Paper - pFLogger: The Parallel Fortran Logging framework for HPC Applications

NASA Technical Reports Server (NTRS)

Clune, Thomas L.; Cruz, Carlos A.

2017-01-01

In the context of high performance computing (HPC), software investments in support of text-based diagnostics, which monitor a running application, are typically limited compared to those for other types of IO. Examples of such diagnostics include reiteration of configuration parameters, progress indicators, simple metrics (e.g., mass conservation, convergence of solvers, etc.), and timers. To some degree, this difference in priority is justifiable as other forms of output are the primary products of a scientific model and, due to their large data volume, much more likely to be a significant performance concern. In contrast, text-based diagnostic content is generally not shared beyond the individual or group running an application and is most often used to troubleshoot when something goes wrong. We suggest that a more systematic approach enabled by a logging facility (or logger) similar to those routinely used by many communities would provide significant value to complex scientific applications. In the context of high-performance computing, an appropriate logger would provide specialized support for distributed and shared-memory parallelism and have low performance overhead. In this paper, we present our prototype implementation of pFlogger a parallel Fortran-based logging framework, and assess its suitability for use in a complex scientific application.

POSITION PAPER - pFLogger: The Parallel Fortran Logging Framework for HPC Applications

NASA Technical Reports Server (NTRS)

Clune, Thomas L.; Cruz, Carlos A.

2017-01-01

In the context of high performance computing (HPC), software investments in support of text-based diagnostics, which monitor a running application, are typically limited compared to those for other types of IO. Examples of such diagnostics include reiteration of configuration parameters, progress indicators, simple metrics (e.g., mass conservation, convergence of solvers, etc.), and timers. To some degree, this difference in priority is justifiable as other forms of output are the primary products of a scientific model and, due to their large data volume, much more likely to be a significant performance concern. In contrast, text-based diagnostic content is generally not shared beyond the individual or group running an application and is most often used to troubleshoot when something goes wrong. We suggest that a more systematic approach enabled by a logging facility (or 'logger') similar to those routinely used by many communities would provide significant value to complex scientific applications. In the context of high-performance computing, an appropriate logger would provide specialized support for distributed and shared-memory parallelism and have low performance overhead. In this paper, we present our prototype implementation of pFlogger - a parallel Fortran-based logging framework, and assess its suitability for use in a complex scientific application.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Madduri, Kamesh; Im, Eun-Jin; Ibrahim, Khaled Z.

The next decade of high-performance computing (HPC) systems will see a rapid evolution and divergence of multi- and manycore architectures as power and cooling constraints limit increases in microprocessor clock speeds. Understanding efficient optimization methodologies on diverse multicore designs in the context of demanding numerical methods is one of the greatest challenges faced today by the HPC community. In this paper, we examine the efficient multicore optimization of GTC, a petascale gyrokinetic toroidal fusion code for studying plasma microturbulence in tokamak devices. For GTC’s key computational components (charge deposition and particle push), we explore efficient parallelization strategies across a broadmore » range of emerging multicore designs, including the recently-released Intel Nehalem-EX, the AMD Opteron Istanbul, and the highly multithreaded Sun UltraSparc T2+. We also present the first study on tuning gyrokinetic particle-in-cell (PIC) algorithms for graphics processors, using the NVIDIA C2050 (Fermi). Our work discusses several novel optimization approaches for gyrokinetic PIC, including mixed-precision computation, particle binning and decomposition strategies, grid replication, SIMDized atomic floating-point operations, and effective GPU texture memory utilization. Overall, we achieve significant performance improvements of 1.3–4.7× on these complex PIC kernels, despite the inherent challenges of data dependency and locality. Finally, our work also points to several architectural and programming features that could significantly enhance PIC performance and productivity on next-generation architectures.« less

Large Scale Computing and Storage Requirements for High Energy Physics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gerber, Richard A.; Wasserman, Harvey

2010-11-24

The National Energy Research Scientific Computing Center (NERSC) is the leading scientific computing facility for the Department of Energy's Office of Science, providing high-performance computing (HPC) resources to more than 3,000 researchers working on about 400 projects. NERSC provides large-scale computing resources and, crucially, the support and expertise needed for scientists to make effective use of them. In November 2009, NERSC, DOE's Office of Advanced Scientific Computing Research (ASCR), and DOE's Office of High Energy Physics (HEP) held a workshop to characterize the HPC resources needed at NERSC to support HEP research through the next three to five years. Themore » effort is part of NERSC's legacy of anticipating users needs and deploying resources to meet those demands. The workshop revealed several key points, in addition to achieving its goal of collecting and characterizing computing requirements. The chief findings: (1) Science teams need access to a significant increase in computational resources to meet their research goals; (2) Research teams need to be able to read, write, transfer, store online, archive, analyze, and share huge volumes of data; (3) Science teams need guidance and support to implement their codes on future architectures; and (4) Projects need predictable, rapid turnaround of their computational jobs to meet mission-critical time constraints. This report expands upon these key points and includes others. It also presents a number of case studies as representative of the research conducted within HEP. Workshop participants were asked to codify their requirements in this case study format, summarizing their science goals, methods of solution, current and three-to-five year computing requirements, and software and support needs. Participants were also asked to describe their strategy for computing in the highly parallel, multi-core environment that is expected to dominate HPC architectures over the next few years. The report includes a section that describes efforts already underway or planned at NERSC that address requirements collected at the workshop. NERSC has many initiatives in progress that address key workshop findings and are aligned with NERSC's strategic plans.« less

Using CyberShake Workflows to Manage Big Seismic Hazard Data on Large-Scale Open-Science HPC Resources

NASA Astrophysics Data System (ADS)

Callaghan, S.; Maechling, P. J.; Juve, G.; Vahi, K.; Deelman, E.; Jordan, T. H.

2015-12-01

The CyberShake computational platform, developed by the Southern California Earthquake Center (SCEC), is an integrated collection of scientific software and middleware that performs 3D physics-based probabilistic seismic hazard analysis (PSHA) for Southern California. CyberShake integrates large-scale and high-throughput research codes to produce probabilistic seismic hazard curves for individual locations of interest and hazard maps for an entire region. A recent CyberShake calculation produced about 500,000 two-component seismograms for each of 336 locations, resulting in over 300 million synthetic seismograms in a Los Angeles-area probabilistic seismic hazard model. CyberShake calculations require a series of scientific software programs. Early computational stages produce data used as inputs by later stages, so we describe CyberShake calculations using a workflow definition language. Scientific workflow tools automate and manage the input and output data and enable remote job execution on large-scale HPC systems. To satisfy the requests of broad impact users of CyberShake data, such as seismologists, utility companies, and building code engineers, we successfully completed CyberShake Study 15.4 in April and May 2015, calculating a 1 Hz urban seismic hazard map for Los Angeles. We distributed the calculation between the NSF Track 1 system NCSA Blue Waters, the DOE Leadership-class system OLCF Titan, and USC's Center for High Performance Computing. This study ran for over 5 weeks, burning about 1.1 million node-hours and producing over half a petabyte of data. The CyberShake Study 15.4 results doubled the maximum simulated seismic frequency from 0.5 Hz to 1.0 Hz as compared to previous studies, representing a factor of 16 increase in computational complexity. We will describe how our workflow tools supported splitting the calculation across multiple systems. We will explain how we modified CyberShake software components, including GPU implementations and migrating from file-based communication to MPI messaging, to greatly reduce the I/O demands and node-hour requirements of CyberShake. We will also present performance metrics from CyberShake Study 15.4, and discuss challenges that producers of Big Data on open-science HPC resources face moving forward.

A Parallel Neuromorphic Text Recognition System and Its Implementation on a Heterogeneous High-Performance Computing Cluster

DTIC Science & Technology

2013-01-01

M. Ahmadi, and M. Shridhar, “ Handwritten Numeral Recognition with Multiple Features and Multistage Classifiers,” Proc. IEEE Int’l Symp. Circuits...ARTICLE (Post Print) 3. DATES COVERED (From - To) SEP 2011 – SEP 2013 4. TITLE AND SUBTITLE A PARALLEL NEUROMORPHIC TEXT RECOGNITION SYSTEM AND ITS...research in computational intelligence has entered a new era. In this paper, we present an HPC-based context-aware intelligent text recognition

Research of aerohydrodynamic and aeroelastic processes on PNRPU HPC system

NASA Astrophysics Data System (ADS)

Modorskii, V. Ya.; Shevelev, N. A.

2016-10-01

Research of aerohydrodynamic and aeroelastic processes with the High Performance Computing Complex in PNIPU is actively conducted within the university priority development direction "Aviation engine and gas turbine technology". Work is carried out in two areas: development and use of domestic software and use of well-known foreign licensed applied software packets. In addition, the third direction associated with the verification of computational experiments - physical modeling, with unique proprietary experimental installations is being developed.

Evaluating Application Resilience with XRay

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Sui; Bronevetsky, Greg; Li, Bin

2015-05-07

The rising count and shrinking feature size of transistors within modern computers is making them increasingly vulnerable to various types of soft faults. This problem is especially acute in high-performance computing (HPC) systems used for scientific computing, because these systems include many thousands of compute cores and nodes, all of which may be utilized in a single large-scale run. The increasing vulnerability of HPC applications to errors induced by soft faults is motivating extensive work on techniques to make these applications more resiilent to such faults, ranging from generic techniques such as replication or checkpoint/restart to algorithmspecific error detection andmore » tolerance techniques. Effective use of such techniques requires a detailed understanding of how a given application is affected by soft faults to ensure that (i) efforts to improve application resilience are spent in the code regions most vulnerable to faults and (ii) the appropriate resilience technique is applied to each code region. This paper presents XRay, a tool to view the application vulnerability to soft errors, and illustrates how XRay can be used in the context of a representative application. In addition to providing actionable insights into application behavior XRay automatically selects the number of fault injection experiments required to provide an informative view of application behavior, ensuring that the information is statistically well-grounded without performing unnecessary experiments.« less

Approach to solution of coupled heat transfer problem on the surface of hypersonic vehicle of arbitrary shape

NASA Astrophysics Data System (ADS)

Bocharov, A. N.; Bityurin, V. A.; Golovin, N. N.; Evstigneev, N. M.; Petrovskiy, V. P.; Ryabkov, O. I.; Teplyakov, I. O.; Shustov, A. A.; Solomonov, Yu S.; Fortov, V. E.

2016-11-01

In this paper, an approach to solve conjugate heat- and mass-transfer problems is considered to be applied to hypersonic vehicle surface of arbitrary shape. The approach under developing should satisfy the following demands. (i) The surface of the body of interest may have arbitrary geometrical shape. (ii) The shape of the body can change during calculation. (iii) The flight characteristics may vary in a wide range, specifically flight altitude, free-stream Mach number, angle-of-attack, etc. (iv) The approach should be realized with using the high-performance-computing (HPC) technologies. The approach is based on coupled solution of 3D unsteady hypersonic flow equations and 3D unsteady heat conductance problem for the thick wall. Iterative process is applied to account for ablation of wall material and, consequently, mass injection from the surface and changes in the surface shape. While iterations, unstructured computational grids both in the flow region and within the wall interior are adapted to the current geometry and flow conditions. The flow computations are done on HPC platform and are most time-consuming part of the whole problem, while heat conductance problem can be solved on many kinds of computers.

Continuous Security and Configuration Monitoring of HPC Clusters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Garcia-Lomeli, H. D.; Bertsch, A. D.; Fox, D. M.

Continuous security and configuration monitoring of information systems has been a time consuming and laborious task for system administrators at the High Performance Computing (HPC) center. Prior to this project, system administrators had to manually check the settings of thousands of nodes, which required a significant number of hours rendering the old process ineffective and inefficient. This paper explains the application of Splunk Enterprise, a software agent, and a reporting tool in the development of a user application interface to track and report on critical system updates and security compliance status of HPC Clusters. In conjunction with other configuration managementmore » systems, the reporting tool is to provide continuous situational awareness to system administrators of the compliance state of information systems. Our approach consisted of the development, testing, and deployment of an agent to collect any arbitrary information across a massively distributed computing center, and organize that information into a human-readable format. Using Splunk Enterprise, this raw data was then gathered into a central repository and indexed for search, analysis, and correlation. Following acquisition and accumulation, the reporting tool generated and presented actionable information by filtering the data according to command line parameters passed at run time. Preliminary data showed results for over six thousand nodes. Further research and expansion of this tool could lead to the development of a series of agents to gather and report critical system parameters. However, in order to make use of the flexibility and resourcefulness of the reporting tool the agent must conform to specifications set forth in this paper. This project has simplified the way system administrators gather, analyze, and report on the configuration and security state of HPC clusters, maintaining ongoing situational awareness. Rather than querying each cluster independently, compliance checking can be managed from one central location.« less

An Optimizing Compiler for Petascale I/O on Leadership-Class Architectures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kandemir, Mahmut Taylan; Choudary, Alok; Thakur, Rajeev

In high-performance computing (HPC), parallel I/O architectures usually have very complex hierarchies with multiple layers that collectively constitute an I/O stack, including high-level I/O libraries such as PnetCDF and HDF5, I/O middleware such as MPI-IO, and parallel file systems such as PVFS and Lustre. Our DOE project explored automated instrumentation and compiler support for I/O intensive applications. Our project made significant progress towards understanding the complex I/O hierarchies of high-performance storage systems (including storage caches, HDDs, and SSDs), and designing and implementing state-of-the-art compiler/runtime system technology that targets I/O intensive HPC applications that target leadership class machine. This final reportmore » summarizes the major achievements of the project and also points out promising future directions Two new sections in this report compared to the previous report are IOGenie and SSD/NVM-specific optimizations.« less

High-Bandwidth Tactical-Network Data Analysis in a High-Performance-Computing (HPC) Environment: Time Tagging the Data

DTIC Science & Technology

2015-09-01

this report made use of posttest processing techniques to provide packet-level time tagging with an accuracy close to 3 µs relative to Coordinated...h set of test records. The process described herein made use of posttest processing techniques to provide packet-level time tagging with an accuracy

Peregrine System User Basics | High-Performance Computing | NREL

Science.gov Websites

peregrine.hpc.nrel.gov or to one of the login nodes. Example commands to access Peregrine from a Linux or Mac OS X system Code Example Create a file called hello.F90 containing the following code: program hello write(6 information by enclosing it in brackets < >. For example: $ ssh -Y

Drug repurposing: translational pharmacology, chemistry, computers and the clinic.

PubMed

Issa, Naiem T; Byers, Stephen W; Dakshanamurthy, Sivanesan

2013-01-01

The process of discovering a pharmacological compound that elicits a desired clinical effect with minimal side effects is a challenge. Prior to the advent of high-performance computing and large-scale screening technologies, drug discovery was largely a serendipitous endeavor, as in the case of thalidomide for erythema nodosum leprosum or cancer drugs in general derived from flora located in far-reaching geographic locations. More recently, de novo drug discovery has become a more rationalized process where drug-target-effect hypotheses are formulated on the basis of already known compounds/protein targets and their structures. Although this approach is hypothesis-driven, the actual success has been very low, contributing to the soaring costs of research and development as well as the diminished pharmaceutical pipeline in the United States. In this review, we discuss the evolution in computational pharmacology as the next generation of successful drug discovery and implementation in the clinic where high-performance computing (HPC) is used to generate and validate drug-target-effect hypotheses completely in silico. The use of HPC would decrease development time and errors while increasing productivity prior to in vitro, animal and human testing. We highlight approaches in chemoinformatics, bioinformatics as well as network biopharmacology to illustrate potential avenues from which to design clinically efficacious drugs. We further discuss the implications of combining these approaches into an integrative methodology for high-accuracy computational predictions within the context of drug repositioning for the efficient streamlining of currently approved drugs back into clinical trials for possible new indications.

I/O load balancing for big data HPC applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Paul, Arnab K.; Goyal, Arpit; Wang, Feiyi

High Performance Computing (HPC) big data problems require efficient distributed storage systems. However, at scale, such storage systems often experience load imbalance and resource contention due to two factors: the bursty nature of scientific application I/O; and the complex I/O path that is without centralized arbitration and control. For example, the extant Lustre parallel file system-that supports many HPC centers-comprises numerous components connected via custom network topologies, and serves varying demands of a large number of users and applications. Consequently, some storage servers can be more loaded than others, which creates bottlenecks and reduces overall application I/O performance. Existing solutionsmore » typically focus on per application load balancing, and thus are not as effective given their lack of a global view of the system. In this paper, we propose a data-driven approach to load balance the I/O servers at scale, targeted at Lustre deployments. To this end, we design a global mapper on Lustre Metadata Server, which gathers runtime statistics from key storage components on the I/O path, and applies Markov chain modeling and a minimum-cost maximum-flow algorithm to decide where data should be placed. Evaluation using a realistic system simulator and a real setup shows that our approach yields better load balancing, which in turn can improve end-to-end performance.« less

A Framework for Adaptable Operating and Runtime Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sterling, Thomas

The emergence of new classes of HPC systems where performance improvement is enabled by Moore’s Law for technology is manifest through multi-core-based architectures including specialized GPU structures. Operating systems were originally designed for control of uniprocessor systems. By the 1980s multiprogramming, virtual memory, and network interconnection were integral services incorporated as part of most modern computers. HPC operating systems were primarily derivatives of the Unix model with Linux dominating the Top-500 list. The use of Linux for commodity clusters was first pioneered by the NASA Beowulf Project. However, the rapid increase in number of cores to achieve performance gain throughmore » technology advances has exposed the limitations of POSIX general-purpose operating systems in scaling and efficiency. This project was undertaken through the leadership of Sandia National Laboratories and in partnership of the University of New Mexico to investigate the alternative of composable lightweight kernels on scalable HPC architectures to achieve superior performance for a wide range of applications. The use of composable operating systems is intended to provide a minimalist set of services specifically required by a given application to preclude overheads and operational uncertainties (“OS noise”) that have been demonstrated to degrade efficiency and operational consistency. This project was undertaken as an exploration to investigate possible strategies and methods for composable lightweight kernel operating systems towards support for extreme scale systems.« less

Collective input/output under memory constraints

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Yin; Chen, Yong; Zhuang, Yu

2014-12-18

Compared with current high-performance computing (HPC) systems, exascale systems are expected to have much less memory per node, which can significantly reduce necessary collective input/output (I/O) performance. In this study, we introduce a memory-conscious collective I/O strategy that takes into account memory capacity and bandwidth constraints. The new strategy restricts aggregation data traffic within disjointed subgroups, coordinates I/O accesses in intranode and internode layers, and determines I/O aggregators at run time considering memory consumption among processes. We have prototyped the design and evaluated it with commonly used benchmarks to verify its potential. The evaluation results demonstrate that this strategy holdsmore » promise in mitigating the memory pressure, alleviating the contention for memory bandwidth, and improving the I/O performance for projected extreme-scale systems. Given the importance of supporting increasingly data-intensive workloads and projected memory constraints on increasingly larger scale HPC systems, this new memory-conscious collective I/O can have a significant positive impact on scientific discovery productivity.« less

Atlas : A library for numerical weather prediction and climate modelling

NASA Astrophysics Data System (ADS)

Deconinck, Willem; Bauer, Peter; Diamantakis, Michail; Hamrud, Mats; Kühnlein, Christian; Maciel, Pedro; Mengaldo, Gianmarco; Quintino, Tiago; Raoult, Baudouin; Smolarkiewicz, Piotr K.; Wedi, Nils P.

2017-11-01

The algorithms underlying numerical weather prediction (NWP) and climate models that have been developed in the past few decades face an increasing challenge caused by the paradigm shift imposed by hardware vendors towards more energy-efficient devices. In order to provide a sustainable path to exascale High Performance Computing (HPC), applications become increasingly restricted by energy consumption. As a result, the emerging diverse and complex hardware solutions have a large impact on the programming models traditionally used in NWP software, triggering a rethink of design choices for future massively parallel software frameworks. In this paper, we present Atlas, a new software library that is currently being developed at the European Centre for Medium-Range Weather Forecasts (ECMWF), with the scope of handling data structures required for NWP applications in a flexible and massively parallel way. Atlas provides a versatile framework for the future development of efficient NWP and climate applications on emerging HPC architectures. The applications range from full Earth system models, to specific tools required for post-processing weather forecast products. The Atlas library thus constitutes a step towards affordable exascale high-performance simulations by providing the necessary abstractions that facilitate the application in heterogeneous HPC environments by promoting the co-design of NWP algorithms with the underlying hardware.

Highly Efficient Parallel Multigrid Solver For Large-Scale Simulation of Grain Growth Using the Structural Phase Field Crystal Model

NASA Astrophysics Data System (ADS)

Guan, Zhen; Pekurovsky, Dmitry; Luce, Jason; Thornton, Katsuyo; Lowengrub, John

The structural phase field crystal (XPFC) model can be used to model grain growth in polycrystalline materials at diffusive time-scales while maintaining atomic scale resolution. However, the governing equation of the XPFC model is an integral-partial-differential-equation (IPDE), which poses challenges in implementation onto high performance computing (HPC) platforms. In collaboration with the XSEDE Extended Collaborative Support Service, we developed a distributed memory HPC solver for the XPFC model, which combines parallel multigrid and P3DFFT. The performance benchmarking on the Stampede supercomputer indicates near linear strong and weak scaling for both multigrid and transfer time between multigrid and FFT modules up to 1024 cores. Scalability of the FFT module begins to decline at 128 cores, but it is sufficient for the type of problem we will be examining. We have demonstrated simulations using 1024 cores, and we expect to achieve 4096 cores and beyond. Ongoing work involves optimization of MPI/OpenMP-based codes for the Intel KNL Many-Core Architecture. This optimizes the code for coming pre-exascale systems, in particular many-core systems such as Stampede 2.0 and Cori 2 at NERSC, without sacrificing efficiency on other general HPC systems.

Biomass Waste Inspired Highly Porous Carbon for High Performance Lithium/Sulfur Batteries

PubMed Central

Zhao, Yan; Ren, Jun; Tan, Taizhe; Babaa, Moulay-Rachid; Bakenov, Zhumabay; Liu, Ning; Zhang, Yongguang

2017-01-01

The synthesis of highly porous carbon (HPC) materials from poplar catkin by KOH chemical activation and hydrothermal carbonization as a conductive additive to a lithium-sulfur cathode is reported. Elemental sulfur was composited with as-prepared HPC through a melt diffusion method to form a S/HPC nanocomposite. Structure and morphology characterization revealed a hierarchically sponge-like structure of HPC with high pore volume (0.62 cm3∙g−1) and large specific surface area (1261.7 m2∙g−1). When tested in Li/S batteries, the resulting compound demonstrated excellent cycling stability, delivering a second-specific capacity of 1154 mAh∙g−1 as well as presenting 74% retention of value after 100 cycles at 0.1 C. Therefore, the porous structure of HPC plays an important role in enhancing electrochemical properties, which provides conditions for effective charge transfer and effective trapping of soluble polysulfide intermediates, and remarkably improves the electrochemical performance of S/HPC composite cathodes. PMID:28878149

Re-Form: FPGA-Powered True Codesign Flow for High-Performance Computing In The Post-Moore Era

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cappello, Franck; Yoshii, Kazutomo; Finkel, Hal

Multicore scaling will end soon because of practical power limits. Dark silicon is becoming a major issue even more than the end of Moore’s law. In the post-Moore era, the energy efficiency of computing will be a major concern. FPGAs could be a key to maximizing the energy efficiency. In this paper we address severe challenges in the adoption of FPGA in HPC and describe “Re-form,” an FPGA-powered codesign flow.

Sandia QIS Capabilities.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Muller, Richard P.

2017-07-01

Sandia National Laboratories has developed a broad set of capabilities in quantum information science (QIS), including elements of quantum computing, quantum communications, and quantum sensing. The Sandia QIS program is built atop unique DOE investments at the laboratories, including the MESA microelectronics fabrication facility, the Center for Integrated Nanotechnologies (CINT) facilities (joint with LANL), the Ion Beam Laboratory, and ASC High Performance Computing (HPC) facilities. Sandia has invested $75 M of LDRD funding over 12 years to develop unique, differentiating capabilities that leverage these DOE infrastructure investments.

An Application-Based Performance Characterization of the Columbia Supercluster

NASA Technical Reports Server (NTRS)

Biswas, Rupak; Djomehri, Jahed M.; Hood, Robert; Jin, Hoaqiang; Kiris, Cetin; Saini, Subhash

2005-01-01

Columbia is a 10,240-processor supercluster consisting of 20 Altix nodes with 512 processors each, and currently ranked as the second-fastest computer in the world. In this paper, we present the performance characteristics of Columbia obtained on up to four computing nodes interconnected via the InfiniBand and/or NUMAlink4 communication fabrics. We evaluate floating-point performance, memory bandwidth, message passing communication speeds, and compilers using a subset of the HPC Challenge benchmarks, and some of the NAS Parallel Benchmarks including the multi-zone versions. We present detailed performance results for three scientific applications of interest to NASA, one from molecular dynamics, and two from computational fluid dynamics. Our results show that both the NUMAlink4 and the InfiniBand hold promise for application scaling to a large number of processors.

MemAxes: Visualization and Analytics for Characterizing Complex Memory Performance Behaviors.

PubMed

Gimenez, Alfredo; Gamblin, Todd; Jusufi, Ilir; Bhatele, Abhinav; Schulz, Martin; Bremer, Peer-Timo; Hamann, Bernd

2018-07-01

Memory performance is often a major bottleneck for high-performance computing (HPC) applications. Deepening memory hierarchies, complex memory management, and non-uniform access times have made memory performance behavior difficult to characterize, and users require novel, sophisticated tools to analyze and optimize this aspect of their codes. Existing tools target only specific factors of memory performance, such as hardware layout, allocations, or access instructions. However, today's tools do not suffice to characterize the complex relationships between these factors. Further, they require advanced expertise to be used effectively. We present MemAxes, a tool based on a novel approach for analytic-driven visualization of memory performance data. MemAxes uniquely allows users to analyze the different aspects related to memory performance by providing multiple visual contexts for a centralized dataset. We define mappings of sampled memory access data to new and existing visual metaphors, each of which enabling a user to perform different analysis tasks. We present methods to guide user interaction by scoring subsets of the data based on known performance problems. This scoring is used to provide visual cues and automatically extract clusters of interest. We designed MemAxes in collaboration with experts in HPC and demonstrate its effectiveness in case studies.

When to Renew Software Licences at HPC Centres? A Mathematical Analysis

NASA Astrophysics Data System (ADS)

Baolai, Ge; MacIsaac, Allan B.

2010-11-01

In this paper we study a common problem faced by many high performance computing (HPC) centres: When and how to renew commercial software licences. Software vendors often sell perpetual licences along with forward update and support contracts at an additional, annual cost. Every year or so, software support personnel and the budget units of HPC centres are required to make the decision of whether or not to renew such support, and usually such decisions are made intuitively. The total cost for a continuing support contract can, however, be costly. One might therefore want a rational answer to the question of whether the option for a renewal should be exercised and when. In an attempt to study this problem within a market framework, we present the mathematical problem derived for the day to day operation of a hypothetical HPC centre that charges for the use of software packages. In the mathematical model, we assume that the uncertainty comes from the demand, number of users using the packages, as well as the price. Further we assume the availability of up to date software versions may also affect the demand. We develop a renewal strategy that aims to maximize the expected profit from the use the software under consideration. The derived problem involves a decision tree, which constitutes a numerical procedure that can be processed in parallel.

HPC Insights, Fall 2011

DTIC Science & Technology

2011-01-01

Simulating Satellite Tracking Using Parallel Computing By Andrew Lindstrom ,University of Hawaii at Hilo — Mentors: Carl Holmberg, Maui High Performance...RDECOM) and his management team, RDECOM Deputy Director Gary Martin ; ARL Director John Miller; Communications- Electronics Research, Development...Saves Resources By Mike Knowles, ARL DSRC Site Lead, Lockheed Martin mode instead of full power down. The first phase of the EAS effort is an attempt

AHPCRC - Army High Performance Computing Research Center

DTIC Science & Technology

2008-01-01

University) Birds and insects use complex flapping and twisting wing motions to maneuver, hover, avoid obstacles, and maintain or regain their...vehicles for use in sensing, surveillance, and wireless communications. HPC simulations examine plunging, pitching, and twisting motions of aeroelastic...wings, to optimize the amplitudes and frequencies of flapping and twisting motions for the maximum amount of thrust. Several methods of calculation

Using ANSYS Fluent on the Peregrine System | High-Performance Computing |

Science.gov Websites

two ways to run ANSYS CFD interactively on NREL HPC systems. When graphics rendering is not a critical when used as above is quite low (e.g., windows take a long time to come up). For small tasks, it may be , go to Category/Connection/SSH, and check off the box "enable compression". When graphics

Peregrine Transition from CentOS6 to CentOS7 | High-Performance Computing |

Science.gov Websites

). Users should consider them primarily as examples, which they can copy and modify for their own use with HPC environments. This can permit one-step access to pre-existing complex software stacks, or /projects. This is not a highly suggested mechanism, but might serve for one-time needs. In the unlikely

RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models.

PubMed

Stamatakis, Alexandros

2006-11-01

RAxML-VI-HPC (randomized axelerated maximum likelihood for high performance computing) is a sequential and parallel program for inference of large phylogenies with maximum likelihood (ML). Low-level technical optimizations, a modification of the search algorithm, and the use of the GTR+CAT approximation as replacement for GTR+Gamma yield a program that is between 2.7 and 52 times faster than the previous version of RAxML. A large-scale performance comparison with GARLI, PHYML, IQPNNI and MrBayes on real data containing 1000 up to 6722 taxa shows that RAxML requires at least 5.6 times less main memory and yields better trees in similar times than the best competing program (GARLI) on datasets up to 2500 taxa. On datasets > or =4000 taxa it also runs 2-3 times faster than GARLI. RAxML has been parallelized with MPI to conduct parallel multiple bootstraps and inferences on distinct starting trees. The program has been used to compute ML trees on two of the largest alignments to date containing 25,057 (1463 bp) and 2182 (51,089 bp) taxa, respectively. icwww.epfl.ch/~stamatak

Lightweight Provenance Service for High-Performance Computing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dai, Dong; Chen, Yong; Carns, Philip

Provenance describes detailed information about the history of a piece of data, containing the relationships among elements such as users, processes, jobs, and workflows that contribute to the existence of data. Provenance is key to supporting many data management functionalities that are increasingly important in operations such as identifying data sources, parameters, or assumptions behind a given result; auditing data usage; or understanding details about how inputs are transformed into outputs. Despite its importance, however, provenance support is largely underdeveloped in highly parallel architectures and systems. One major challenge is the demanding requirements of providing provenance service in situ. Themore » need to remain lightweight and to be always on often conflicts with the need to be transparent and offer an accurate catalog of details regarding the applications and systems. To tackle this challenge, we introduce a lightweight provenance service, called LPS, for high-performance computing (HPC) systems. LPS leverages a kernel instrument mechanism to achieve transparency and introduces representative execution and flexible granularity to capture comprehensive provenance with controllable overhead. Extensive evaluations and use cases have confirmed its efficiency and usability. We believe that LPS can be integrated into current and future HPC systems to support a variety of data management needs.« less

Large Scale GW Calculations on the Cori System

NASA Astrophysics Data System (ADS)

Deslippe, Jack; Del Ben, Mauro; da Jornada, Felipe; Canning, Andrew; Louie, Steven

The NERSC Cori system, powered by 9000+ Intel Xeon-Phi processors, represents one of the largest HPC systems for open-science in the United States and the world. We discuss the optimization of the GW methodology for this system, including both node level and system-scale optimizations. We highlight multiple large scale (thousands of atoms) case studies and discuss both absolute application performance and comparison to calculations on more traditional HPC architectures. We find that the GW method is particularly well suited for many-core architectures due to the ability to exploit a large amount of parallelism across many layers of the system. This work was supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division, as part of the Computational Materials Sciences Program.

Self curing admixture performance report.

DOT National Transportation Integrated Search

2012-02-01

The Oregon Department of Transportation (ODOT) has experienced early age cracking of newly placed high performance : concrete (HPC) bridge decks. The silica fume contained in the HPC requires immediate and proper curing application after : placement ...

CT imaging of malignant metastatic hemangiopericytoma of the parotid gland with histopathological correlation

PubMed Central

Khoo, James B.; Sittampalam, Kesavan; Chee, Soo K.

2008-01-01

Abstract We report an extremely rare case of malignant hemangiopericytoma (HPC) of the parotid gland and its metastatic spread to lung, liver, and skeletal muscle. Computed tomography (CT) imaging, histopathological and immunohistochemical methods were employed to study the features of malignant HPC and its metastases. CT imaging was helpful to determine the exact location, involvement of adjacent structures and vascularity, as well as evaluating pulmonary, hepatic, peritoneal, and muscular metastases. Immunohistochemical and histopatholgical features of the primary tumor as well as the metastases were consistent with the diagnosis of malignant HPC. PMID:18940737

Frequency-specific hippocampal-prefrontal interactions during associative learning

PubMed Central

Brincat, Scott L.; Miller, Earl K.

2015-01-01

Much of our knowledge of the world depends on learning associations (e.g., face-name), for which the hippocampus (HPC) and prefrontal cortex (PFC) are critical. HPC-PFC interactions have rarely been studied in monkeys, whose cognitive/mnemonic abilities are akin to humans. Here, we show functional differences and frequency-specific interactions between HPC and PFC of monkeys learning object-pair associations, an animal model of human explicit memory. PFC spiking activity reflected learning in parallel with behavioral performance, while HPC neurons reflected feedback about whether trial-and-error guesses were correct or incorrect. Theta-band HPC-PFC synchrony was stronger after errors, was driven primarily by PFC to HPC directional influences, and decreased with learning. In contrast, alpha/beta-band synchrony was stronger after correct trials, was driven more by HPC, and increased with learning. Rapid object associative learning may occur in PFC, while HPC may guide neocortical plasticity by signaling success or failure via oscillatory synchrony in different frequency bands. PMID:25706471

Improving User Notification on Frequently Changing HPC Environments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fuson, Christopher B; Renaud, William A

2016-01-01

Today s HPC centers user environments can be very complex. Centers often contain multiple large complicated computational systems each with their own user environment. Changes to a system s environment can be very impactful; however, a center s user environment is, in one-way or another, frequently changing. Because of this, it is vital for centers to notify users of change. For users, untracked changes can be costly, resulting in unnecessary debug time as well as wasting valuable compute allocations and research time. Communicating frequent change to diverse user communities is a common and ongoing task for HPC centers. This papermore » will cover the OLCF s current processes and methods used to communicate change to users of the center s large Cray systems and supporting resources. The paper will share lessons learned and goals as well as practices, tools, and methods used to continually improve and reach members of the OLCF user community.« less

Gyrokinetic particle-in-cell optimization on emerging multi- and manycore platforms

DOE PAGES

Madduri, Kamesh; Im, Eun-Jin; Ibrahim, Khaled Z.; ...

2011-03-02

The next decade of high-performance computing (HPC) systems will see a rapid evolution and divergence of multi- and manycore architectures as power and cooling constraints limit increases in microprocessor clock speeds. Understanding efficient optimization methodologies on diverse multicore designs in the context of demanding numerical methods is one of the greatest challenges faced today by the HPC community. In this paper, we examine the efficient multicore optimization of GTC, a petascale gyrokinetic toroidal fusion code for studying plasma microturbulence in tokamak devices. For GTC’s key computational components (charge deposition and particle push), we explore efficient parallelization strategies across a broadmore » range of emerging multicore designs, including the recently-released Intel Nehalem-EX, the AMD Opteron Istanbul, and the highly multithreaded Sun UltraSparc T2+. We also present the first study on tuning gyrokinetic particle-in-cell (PIC) algorithms for graphics processors, using the NVIDIA C2050 (Fermi). Our work discusses several novel optimization approaches for gyrokinetic PIC, including mixed-precision computation, particle binning and decomposition strategies, grid replication, SIMDized atomic floating-point operations, and effective GPU texture memory utilization. Overall, we achieve significant performance improvements of 1.3–4.7× on these complex PIC kernels, despite the inherent challenges of data dependency and locality. Finally, our work also points to several architectural and programming features that could significantly enhance PIC performance and productivity on next-generation architectures.« less

Pulmonary metastases of recurrent intracranial hemangiopericytoma diagnosed on fine needle aspiration cytology: a case report.

PubMed

Goel, Deepa; Babu, Sasidhara; Prayaga, Aruna K; Sundaram, Challa

2008-01-01

Meningeal hemangiopericytoma (HPC) is a rare neoplasm. It is closely related to hemangiopericytomas in systemic tissues, with a tendency to recur and metastasize outside the CNS. Only a few case reports describe the cytomorphologic appearance of these metastasizing lesions, most having primary tumor in deep soft tissues. We report a case of recurrent meningeal HPC metastasizing to lungs. A 48-year-old woman presented with a history of headache. She underwent primary surgery 10 years previously for left parietal tumor. Histopathologic diagnosis was HPC. Radiotherapy was given postoperatively. Brain magnetic resonance imaging (MRI) at admission suggested local recurrence. She also complained of dry cough and shortness of breath. On evaluation, computed tomography (CT) scan lung showed multiple, bilateral, small nodules. Fine needle aspiration cytology (FNAC) of a larger nodule revealed spindle-shaped cells arranged around blood vessels. Immunohistochemistry with CD34 on cell block confirmed metastatic HPC. FNAC is an easy, accurate, relatively noninvasive procedure for diagnosing metastases, especially in patients with a history of recurrent intracranial HPC. Immunohistochemistry on cell block material collected at the time of FNAC may aid in distinguishing HPC from other tumors that are close mimics cytologically.

High throughput computing: a solution for scientific analysis

USGS Publications Warehouse

O'Donnell, M.

2011-01-01

handle job failures due to hardware, software, or network interruptions (obviating the need to manually resubmit the job after each stoppage); be affordable; and most importantly, allow us to complete very large, complex analyses that otherwise would not even be possible. In short, we envisioned a job-management system that would take advantage of unused FORT CPUs within a local area network (LAN) to effectively distribute and run highly complex analytical processes. What we found was a solution that uses High Throughput Computing (HTC) and High Performance Computing (HPC) systems to do exactly that (Figure 1).

PATHA: Performance Analysis Tool for HPC Applications

DOE PAGES

Yoo, Wucherl; Koo, Michelle; Cao, Yi; ...

2016-02-18

Large science projects rely on complex workflows to analyze terabytes or petabytes of data. These jobs are often running over thousands of CPU cores and simultaneously performing data accesses, data movements, and computation. It is difficult to identify bottlenecks or to debug the performance issues in these large workflows. In order to address these challenges, we have developed Performance Analysis Tool for HPC Applications (PATHA) using the state-of-art open source big data processing tools. Our framework can ingest system logs to extract key performance measures, and apply the most sophisticated statistical tools and data mining methods on the performance data.more » Furthermore, it utilizes an efficient data processing engine to allow users to interactively analyze a large amount of different types of logs and measurements. To illustrate the functionality of PATHA, we conduct a case study on the workflows from an astronomy project known as the Palomar Transient Factory (PTF). This study processed 1.6 TB of system logs collected on the NERSC supercomputer Edison. Using PATHA, we were able to identify performance bottlenecks, which reside in three tasks of PTF workflow with the dependency on the density of celestial objects.« less

Performance testing of HPC on Sunshine Bridge.

DOT National Transportation Integrated Search

2009-09-01

The deck of the Sunshine Bridge overpass, located westbound on Interstate 40 (I-40) near Winslow, Arizona, was : replaced on August 24, 2005. The original deteriorated concrete deck was replaced using high performance : concrete (HPC), reinforced wit...

CDAC Student Report: Summary of LLNL Internship

DOE Office of Scientific and Technical Information (OSTI.GOV)

Herriman, Jane E.

Multiple objectives motivated me to apply for an internship at LLNL: I wanted to experience the work environment at a national lab, to learn about research and job opportunities at LLNL in particular, and to gain greater experience with code development, particularly within the realm of high performance computing (HPC). This summer I was selected to participate in LLNL's Computational Chemistry and Material Science Summer Institute (CCMS). CCMS is a 10 week program hosted by the Quantum Simulations group leader, Dr. Eric Schwegler. CCMS connects graduate students to mentors at LLNL involved in similar re- search and provides weekly seminarsmore » on a broad array of topics from within chemistry and materials science. Dr. Xavier Andrade and Dr. Erik Draeger served as my co-mentors over the summer, and Dr. Andrade continues to mentor me now that CCMS has concluded. Dr. Andrade is a member of the Quantum Simulations group within the Physical and Life Sciences at LLNL, and Dr. Draeger leads the HPC group within the Center for Applied Scientific Computing (CASC). The two have worked together to develop Qb@ll, an open-source first principles molecular dynamics code that was the platform for my summer research project.« less

Who watches the watchers?: preventing fault in a fault tolerance library

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stanavige, C. D.

The Scalable Checkpoint/Restart library (SCR) was developed and is used by researchers at Lawrence Livermore National Laboratory to provide a fast and efficient method of saving and recovering large applications during runtime on high-performance computing (HPC) systems. Though SCR protects other programs, up until June 2017, nothing was actively protecting SCR. The goal of this project was to automate the building and testing of this library on the varying HPC architectures on which it is used. Our methods centered around the use of a continuous integration tool called Bamboo that allowed for automation agents to be installed on the HPCmore » systems themselves. These agents provided a way for us to establish a new and unique way to automate and customize the allocation of resources and running of tests with CMake’s unit testing framework, CTest, as well as integration testing scripts though an HPC package manager called Spack. These methods provided a parallel environment in which to test the more complex features of SCR. As a result, SCR is now automatically built and tested on several HPC architectures any time changes are made by developers to the library’s source code. The results of these tests are then communicated back to the developers for immediate feedback, allowing them to fix functionality of SCR that may have broken. Hours of developers’ time are now being saved from the tedious process of manually testing and debugging, which saves money and allows the SCR project team to focus their efforts towards development. Thus, HPC system users can use SCR in conjunction with their own applications to efficiently and effectively checkpoint and restart as needed with the assurance that SCR itself is functioning properly.« less

1001 Ways to run AutoDock Vina for virtual screening

NASA Astrophysics Data System (ADS)

Jaghoori, Mohammad Mahdi; Bleijlevens, Boris; Olabarriaga, Silvia D.

2016-03-01

Large-scale computing technologies have enabled high-throughput virtual screening involving thousands to millions of drug candidates. It is not trivial, however, for biochemical scientists to evaluate the technical alternatives and their implications for running such large experiments. Besides experience with the molecular docking tool itself, the scientist needs to learn how to run it on high-performance computing (HPC) infrastructures, and understand the impact of the choices made. Here, we review such considerations for a specific tool, AutoDock Vina, and use experimental data to illustrate the following points: (1) an additional level of parallelization increases virtual screening throughput on a multi-core machine; (2) capturing of the random seed is not enough (though necessary) for reproducibility on heterogeneous distributed computing systems; (3) the overall time spent on the screening of a ligand library can be improved by analysis of factors affecting execution time per ligand, including number of active torsions, heavy atoms and exhaustiveness. We also illustrate differences among four common HPC infrastructures: grid, Hadoop, small cluster and multi-core (virtual machine on the cloud). Our analysis shows that these platforms are suitable for screening experiments of different sizes. These considerations can guide scientists when choosing the best computing platform and set-up for their future large virtual screening experiments.

1001 Ways to run AutoDock Vina for virtual screening.

PubMed

Jaghoori, Mohammad Mahdi; Bleijlevens, Boris; Olabarriaga, Silvia D

2016-03-01

Large-scale computing technologies have enabled high-throughput virtual screening involving thousands to millions of drug candidates. It is not trivial, however, for biochemical scientists to evaluate the technical alternatives and their implications for running such large experiments. Besides experience with the molecular docking tool itself, the scientist needs to learn how to run it on high-performance computing (HPC) infrastructures, and understand the impact of the choices made. Here, we review such considerations for a specific tool, AutoDock Vina, and use experimental data to illustrate the following points: (1) an additional level of parallelization increases virtual screening throughput on a multi-core machine; (2) capturing of the random seed is not enough (though necessary) for reproducibility on heterogeneous distributed computing systems; (3) the overall time spent on the screening of a ligand library can be improved by analysis of factors affecting execution time per ligand, including number of active torsions, heavy atoms and exhaustiveness. We also illustrate differences among four common HPC infrastructures: grid, Hadoop, small cluster and multi-core (virtual machine on the cloud). Our analysis shows that these platforms are suitable for screening experiments of different sizes. These considerations can guide scientists when choosing the best computing platform and set-up for their future large virtual screening experiments.

A parallel calibration utility for WRF-Hydro on high performance computers

NASA Astrophysics Data System (ADS)

Wang, J.; Wang, C.; Kotamarthi, V. R.

2017-12-01

A successful modeling of complex hydrological processes comprises establishing an integrated hydrological model which simulates the hydrological processes in each water regime, calibrates and validates the model performance based on observation data, and estimates the uncertainties from different sources especially those associated with parameters. Such a model system requires large computing resources and often have to be run on High Performance Computers (HPC). The recently developed WRF-Hydro modeling system provides a significant advancement in the capability to simulate regional water cycles more completely. The WRF-Hydro model has a large range of parameters such as those in the input table files — GENPARM.TBL, SOILPARM.TBL and CHANPARM.TBL — and several distributed scaling factors such as OVROUGHRTFAC. These parameters affect the behavior and outputs of the model and thus may need to be calibrated against the observations in order to obtain a good modeling performance. Having a parameter calibration tool specifically for automate calibration and uncertainty estimates of WRF-Hydro model can provide significant convenience for the modeling community. In this study, we developed a customized tool using the parallel version of the model-independent parameter estimation and uncertainty analysis tool, PEST, to enabled it to run on HPC with PBS and SLURM workload manager and job scheduler. We also developed a series of PEST input file templates that are specifically for WRF-Hydro model calibration and uncertainty analysis. Here we will present a flood case study occurred in April 2013 over Midwest. The sensitivity and uncertainties are analyzed using the customized PEST tool we developed.

Scheduling Operations for Massive Heterogeneous Clusters

NASA Technical Reports Server (NTRS)

Humphrey, John; Spagnoli, Kyle

2013-01-01

High-performance computing (HPC) programming has become increasingly difficult with the advent of hybrid supercomputers consisting of multicore CPUs and accelerator boards such as the GPU. Manual tuning of software to achieve high performance on this type of machine has been performed by programmers. This is needlessly difficult and prone to being invalidated by new hardware, new software, or changes in the underlying code. A system was developed for task-based representation of programs, which when coupled with a scheduler and runtime system, allows for many benefits, including higher performance and utilization of computational resources, easier programming and porting, and adaptations of code during runtime. The system consists of a method of representing computer algorithms as a series of data-dependent tasks. The series forms a graph, which can be scheduled for execution on many nodes of a supercomputer efficiently by a computer algorithm. The schedule is executed by a dispatch component, which is tailored to understand all of the hardware types that may be available within the system. The scheduler is informed by a cluster mapping tool, which generates a topology of available resources and their strengths and communication costs. Software is decoupled from its hardware, which aids in porting to future architectures. A computer algorithm schedules all operations, which for systems of high complexity (i.e., most NASA codes), cannot be performed optimally by a human. The system aids in reducing repetitive code, such as communication code, and aids in the reduction of redundant code across projects. It adds new features to code automatically, such as recovering from a lost node or the ability to modify the code while running. In this project, the innovators at the time of this reporting intend to develop two distinct technologies that build upon each other and both of which serve as building blocks for more efficient HPC usage. First is the scheduling and dynamic execution framework, and the second is scalable linear algebra libraries that are built directly on the former.

Performance of high performance concrete (HPC) in low pH and sulfate environment.

DOT National Transportation Integrated Search

2013-05-01

The goal of this research is to determine the impact of low pH and sulfate environment on high-performance concrete (HPC) and if the current structural and materials specifications provide adequate protections for concrete structures to meet the 75-y...

Establishing Linux Clusters for High-Performance Computing (HPC) at NPS

DTIC Science & Technology

2004-09-01

52 e. Intel Roll..................................................................................53 f. Area51 Roll...results of generating md5summ for Area51 roll. All the file information is available. This number can be used to be checked against the number that the...vendor provides fro the particular piece of software. ......51 Figure 22 The given md5summ for Area51 roll form the download site. This number can

AHPCRC (Army High Performance Computing Research Center) Bulletin. Volume 3, Issue 1

DTIC Science & Technology

2011-01-01

release; distribution is unlimited. Multiscale Modeling of Materials The rotating reflector antenna associated with airport traffic control systems is...batteries and phased-array antennas . Power and efficiency studies evaluate on-board HPC systems and advanced image processing applications. 2010 marked...giving way in some applications to a newer technology called the phased array antenna system (sometimes called a beamformer, example shown at right

Selecting a Benchmark Suite to Profile High-Performance Computing (HPC) Machines

DTIC Science & Technology

2014-11-01

architectures. Machines now contain central processing units (CPUs), graphics processing units (GPUs), and many integrated core ( MIC ) architecture all...evaluate the feasibility and applicability of a new architecture just released to the market . Researchers are often unsure how available resources will...architectures. Having a suite of programs running on different architectures, such as GPUs, MICs , and CPUs, adds complexity and technical challenges

High Performance Computing Modeling Advances Accelerator Science for High-Energy Physics

DOE PAGES

Amundson, James; Macridin, Alexandru; Spentzouris, Panagiotis

2014-07-28

The development and optimization of particle accelerators are essential for advancing our understanding of the properties of matter, energy, space, and time. Particle accelerators are complex devices whose behavior involves many physical effects on multiple scales. Therefore, advanced computational tools utilizing high-performance computing are essential for accurately modeling them. In the past decade, the US Department of Energy's SciDAC program has produced accelerator-modeling tools that have been employed to tackle some of the most difficult accelerator science problems. The authors discuss the Synergia framework and its applications to high-intensity particle accelerator physics. Synergia is an accelerator simulation package capable ofmore » handling the entire spectrum of beam dynamics simulations. Our authors present Synergia's design principles and its performance on HPC platforms.« less

Construction of crack-free bridge decks : technical summary.

DOT National Transportation Integrated Search

2017-04-01

The report documents the performance of the decks based on crack surveys performed on the LC-HPC decks and : matching control bridge decks. The specifications for LC-HPC bridge decks, which cover aggregates, concrete, : and construction procedures, a...

An Efficient Silent Data Corruption Detection Method with Error-Feedback Control and Even Sampling for HPC Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Di, Sheng; Berrocal, Eduardo; Cappello, Franck

The silent data corruption (SDC) problem is attracting more and more attentions because it is expected to have a great impact on exascale HPC applications. SDC faults are hazardous in that they pass unnoticed by hardware and can lead to wrong computation results. In this work, we formulate SDC detection as a runtime one-step-ahead prediction method, leveraging multiple linear prediction methods in order to improve the detection results. The contributions are twofold: (1) we propose an error feedback control model that can reduce the prediction errors for different linear prediction methods, and (2) we propose a spatial-data-based even-sampling method tomore » minimize the detection overheads (including memory and computation cost). We implement our algorithms in the fault tolerance interface, a fault tolerance library with multiple checkpoint levels, such that users can conveniently protect their HPC applications against both SDC errors and fail-stop errors. We evaluate our approach by using large-scale traces from well-known, large-scale HPC applications, as well as by running those HPC applications on a real cluster environment. Experiments show that our error feedback control model can improve detection sensitivity by 34-189% for bit-flip memory errors injected with the bit positions in the range [20,30], without any degradation on detection accuracy. Furthermore, memory size can be reduced by 33% with our spatial-data even-sampling method, with only a slight and graceful degradation in the detection sensitivity.« less

3D streamers simulation in a pin to plane configuration using massively parallel computing

NASA Astrophysics Data System (ADS)

Plewa, J.-M.; Eichwald, O.; Ducasse, O.; Dessante, P.; Jacobs, C.; Renon, N.; Yousfi, M.

2018-03-01

This paper concerns the 3D simulation of corona discharge using high performance computing (HPC) managed with the message passing interface (MPI) library. In the field of finite volume methods applied on non-adaptive mesh grids and in the case of a specific 3D dynamic benchmark test devoted to streamer studies, the great efficiency of the iterative R&B SOR and BiCGSTAB methods versus the direct MUMPS method was clearly demonstrated in solving the Poisson equation using HPC resources. The optimization of the parallelization and the resulting scalability was undertaken as a function of the HPC architecture for a number of mesh cells ranging from 8 to 512 million and a number of cores ranging from 20 to 1600. The R&B SOR method remains at least about four times faster than the BiCGSTAB method and requires significantly less memory for all tested situations. The R&B SOR method was then implemented in a 3D MPI parallelized code that solves the classical first order model of an atmospheric pressure corona discharge in air. The 3D code capabilities were tested by following the development of one, two and four coplanar streamers generated by initial plasma spots for 6 ns. The preliminary results obtained allowed us to follow in detail the formation of the tree structure of a corona discharge and the effects of the mutual interactions between the streamers in terms of streamer velocity, trajectory and diameter. The computing time for 64 million of mesh cells distributed over 1000 cores using the MPI procedures is about 30 min ns-1, regardless of the number of streamers.

A study of the viability of exploiting memory content similarity to improve resilience to memory errors

DOE PAGES

Levy, Scott; Ferreira, Kurt B.; Bridges, Patrick G.; ...

2014-12-09

Building the next-generation of extreme-scale distributed systems will require overcoming several challenges related to system resilience. As the number of processors in these systems grow, the failure rate increases proportionally. One of the most common sources of failure in large-scale systems is memory. In this paper, we propose a novel runtime for transparently exploiting memory content similarity to improve system resilience by reducing the rate at which memory errors lead to node failure. We evaluate the viability of this approach by examining memory snapshots collected from eight high-performance computing (HPC) applications and two important HPC operating systems. Based on themore » characteristics of the similarity uncovered, we conclude that our proposed approach shows promise for addressing system resilience in large-scale systems.« less

The Practical Obstacles of Data Transfer: Why researchers still love scp

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nam, Hai Ah; Hill, Jason J; Parete-Koon, Suzanne T

The importance of computing facilities is heralded every six months with the announcement of the new Top500 list, showcasing the world s fastest supercomputers. Unfortu- nately, with great computing capability does not come great long-term data storage capacity, which often means users must move their data to their local site archive, to remote sites where they may be doing future computation or anal- ysis, or back to their home institution, else face the dreaded data purge that most HPC centers employ to keep utiliza- tion of large parallel filesystems low to manage performance and capacity. At HPC centers, data transfermore » is crucial to the scientific workflow and will increase in importance as computing systems grow in size. The Energy Sciences Net- work (ESnet) recently launched its fifth generation network, a 100 Gbps high-performance, unclassified national network connecting more than 40 DOE research sites to support scientific research and collaboration. Despite the tenfold increase in bandwidth to DOE research sites amenable to multiple data transfer streams and high throughput, in prac- tice, researchers often under-utilize the network and resort to painfully-slow single stream transfer methods such as scp to avoid the complexity of using multiple stream tools such as GridFTP and bbcp, and contend with frustration from the lack of consistency of available tools between sites. In this study we survey and assess the data transfer methods pro- vided at several DOE supported computing facilities, includ- ing both leadership-computing facilities, connected through ESnet. We present observed transfer rates, suggested opti- mizations, and discuss the obstacles the tools must overcome to receive wide-spread adoption over scp.« less

HPC AND GRID COMPUTING FOR INTEGRATIVE BIOMEDICAL RESEARCH

PubMed Central

Kurc, Tahsin; Hastings, Shannon; Kumar, Vijay; Langella, Stephen; Sharma, Ashish; Pan, Tony; Oster, Scott; Ervin, David; Permar, Justin; Narayanan, Sivaramakrishnan; Gil, Yolanda; Deelman, Ewa; Hall, Mary; Saltz, Joel

2010-01-01

Integrative biomedical research projects query, analyze, and integrate many different data types and make use of datasets obtained from measurements or simulations of structure and function at multiple biological scales. With the increasing availability of high-throughput and high-resolution instruments, the integrative biomedical research imposes many challenging requirements on software middleware systems. In this paper, we look at some of these requirements using example research pattern templates. We then discuss how middleware systems, which incorporate Grid and high-performance computing, could be employed to address the requirements. PMID:20107625

AutoCNet: A Python library for sparse multi-image correspondence identification for planetary data

NASA Astrophysics Data System (ADS)

Laura, Jason; Rodriguez, Kelvin; Paquette, Adam C.; Dunn, Evin

2018-01-01

In this work we describe the AutoCNet library, written in Python, to support the application of computer vision techniques for n-image correspondence identification in remotely sensed planetary images and subsequent bundle adjustment. The library is designed to support exploratory data analysis, algorithm and processing pipeline development, and application at scale in High Performance Computing (HPC) environments for processing large data sets and generating foundational data products. We also present a brief case study illustrating high level usage for the Apollo 15 Metric camera.

Applied Computational Electromagnetics Society Journal (ACES); Special Issue on Electromagnetics and High Performance Computing. Vol. 13, No. 2

DTIC Science & Technology

1998-07-01

author’s responsibility to obtain written permission to reproduce such material. 1 " vssmwmato srÄmaöNfTT fWi««-ii|<.1iw »■■«. i-i...interesting to compare papers in the issue with previous special issues of other jour- nals and monographs, for example [ 1 , 2]. HPC issues first attracted...environment, in particular the Kendall Square Research KSR- 1 . Fast algorithms have attracted considerable atten- tion in the CEM community, since they

Scalability Test of Multiscale Fluid-Platelet Model for Three Top Supercomputers

PubMed Central

Zhang, Peng; Zhang, Na; Gao, Chao; Zhang, Li; Gao, Yuxiang; Deng, Yuefan; Bluestein, Danny

2016-01-01

We have tested the scalability of three supercomputers: the Tianhe-2, Stampede and CS-Storm with multiscale fluid-platelet simulations, in which a highly-resolved and efficient numerical model for nanoscale biophysics of platelets in microscale viscous biofluids is considered. Three experiments involving varying problem sizes were performed: Exp-S: 680,718-particle single-platelet; Exp-M: 2,722,872-particle 4-platelet; and Exp-L: 10,891,488-particle 16-platelet. Our implementations of multiple time-stepping (MTS) algorithm improved the performance of single time-stepping (STS) in all experiments. Using MTS, our model achieved the following simulation rates: 12.5, 25.0, 35.5 μs/day for Exp-S and 9.09, 6.25, 14.29 μs/day for Exp-M on Tianhe-2, CS-Storm 16-K80 and Stampede K20. The best rate for Exp-L was 6.25 μs/day for Stampede. Utilizing current advanced HPC resources, the simulation rates achieved by our algorithms bring within reach performing complex multiscale simulations for solving vexing problems at the interface of biology and engineering, such as thrombosis in blood flow which combines millisecond-scale hematology with microscale blood flow at resolutions of micro-to-nanoscale cellular components of platelets. This study of testing the performance characteristics of supercomputers with advanced computational algorithms that offer optimal trade-off to achieve enhanced computational performance serves to demonstrate that such simulations are feasible with currently available HPC resources. PMID:27570250

Global Simulation of Bioenergy Crop Productivity: Analytical Framework and Case Study for Switchgrass

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kang, Shujiang; Kline, Keith L; Nair, S. Surendran

A global energy crop productivity model that provides geospatially explicit quantitative details on biomass potential and factors affecting sustainability would be useful, but does not exist now. This study describes a modeling platform capable of meeting many challenges associated with global-scale agro-ecosystem modeling. We designed an analytical framework for bioenergy crops consisting of six major components: (i) standardized natural resources datasets, (ii) global field-trial data and crop management practices, (iii) simulation units and management scenarios, (iv) model calibration and validation, (v) high-performance computing (HPC) simulation, and (vi) simulation output processing and analysis. The HPC-Environmental Policy Integrated Climate (HPC-EPIC) model simulatedmore » a perennial bioenergy crop, switchgrass (Panicum virgatum L.), estimating feedstock production potentials and effects across the globe. This modeling platform can assess soil C sequestration, net greenhouse gas (GHG) emissions, nonpoint source pollution (e.g., nutrient and pesticide loss), and energy exchange with the atmosphere. It can be expanded to include additional bioenergy crops (e.g., miscanthus, energy cane, and agave) and food crops under different management scenarios. The platform and switchgrass field-trial dataset are available to support global analysis of biomass feedstock production potential and corresponding metrics of sustainability.« less

High-Bandwidth Tactical-Network Data Analysis in a High-Performance-Computing (HPC) Environment: Packet-Level Analysis

DTIC Science & Technology

2015-09-01

with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS . 1...UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS (ES) Technical and Project Engineering, LLC QED Systems, LLC Alexandria, VA...AND ADDRESS (ES) US Army Research Laboratory ATTN: RDRL-CIH-C Aberdeen Proving Ground, MD 21005 10. SPONSOR/MONITOR’S ACRONYM(S) 11. SPONSOR

76 FR 45786 - Advanced Scientific Computing Advisory Committee; Meeting

Federal Register 2010, 2011, 2012, 2013, 2014

2011-08-01

... updates. EU Data Initiative. HPC & EERE Wind Program. Early Career Research on Energy Efficient Interconnect for Exascale Computing. Separating Algorithm and Implentation. Update on ASCR exascale planning...

Primary osseous hemangiopericytoma in the thoracic spine.

PubMed

Ren, Ke; Zhou, Xing; Wu, SuJia; Sun, Xiaoliang

2014-01-01

Hemangiopericytoma (HPC) is a rare tumor of the central nervous system, most commonly found in the cranial cavity. HPCs in the spine are rare, and very few of them are primary osseous HPC. The aims of this study were to describe a rare case of primary osseous HPC in the thoracic spine and review the literature. A 54-year-old man presented with a 3-month history of back pain. Aneuro logical examination revealed no motor or sensory deficits. Magnetic resonance imaging (MRI) and computed tomography (CT) scan showed a tumor originating from the bone structure of the T10 vertebra with paravertebral extension, and chest CT revealed pulmonary metastases. A laminectomy, face-totomy,and subtotal resection of the tumor was performed with posterior pedicle screw system fixation followed by radiotherapy. The post-operative course was uneventful. His back pain was resolved completely after surgery. The patient survived with tumor during the 18-month follow-up period. Histopathology and immunohistologic findings were consistent with HPC. On immunohistochemistry, the tumor was positive for vimentin and CD34, partially positive for S-100, but negative for EMA, desmin, CD117, and CD1a. A literature review identified eight such cases reported between 1942 and 2013. As a conclusion, clinical manifestations of primary osseous spinal HPCs are different from intraspinal meningeal HPCs. Although showing certain variability, histopathology and immunohistochemical examinations are essential to establish the diagnosis. Surgical resection and radiotherapy are the treatment of choice. *These authors contributed equally to this work.

Accelerating Science with the NERSC Burst Buffer Early User Program

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bhimji, Wahid; Bard, Debbie; Romanus, Melissa

NVRAM-based Burst Buffers are an important part of the emerging HPC storage landscape. The National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory recently installed one of the first Burst Buffer systems as part of its new Cori supercomputer, collaborating with Cray on the development of the DataWarp software. NERSC has a diverse user base comprised of over 6500 users in 700 different projects spanning a wide variety of scientific computing applications. The use-cases of the Burst Buffer at NERSC are therefore also considerable and diverse. We describe here performance measurements and lessons learned from the Burstmore » Buffer Early User Program at NERSC, which selected a number of research projects to gain early access to the Burst Buffer and exercise its capability to enable new scientific advancements. To the best of our knowledge this is the first time a Burst Buffer has been stressed at scale by diverse, real user workloads and therefore these lessons will be of considerable benefit to shaping the developing use of Burst Buffers at HPC centers.« less

Fine grained event processing on HPCs with the ATLAS Yoda system

NASA Astrophysics Data System (ADS)

Calafiura, Paolo; De, Kaushik; Guan, Wen; Maeno, Tadashi; Nilsson, Paul; Oleynik, Danila; Panitkin, Sergey; Tsulaia, Vakhtang; Van Gemmeren, Peter; Wenaus, Torre

2015-12-01

High performance computing facilities present unique challenges and opportunities for HEP event processing. The massive scale of many HPC systems means that fractionally small utilization can yield large returns in processing throughput. Parallel applications which can dynamically and efficiently fill any scheduling opportunities the resource presents benefit both the facility (maximal utilization) and the (compute-limited) science. The ATLAS Yoda system provides this capability to HEP-like event processing applications by implementing event-level processing in an MPI-based master-client model that integrates seamlessly with the more broadly scoped ATLAS Event Service. Fine grained, event level work assignments are intelligently dispatched to parallel workers to sustain full utilization on all cores, with outputs streamed off to destination object stores in near real time with similarly fine granularity, such that processing can proceed until termination with full utilization. The system offers the efficiency and scheduling flexibility of preemption without requiring the application actually support or employ check-pointing. We will present the new Yoda system, its motivations, architecture, implementation, and applications in ATLAS data processing at several US HPC centers.

On Parallelizing Single Dynamic Simulation Using HPC Techniques and APIs of Commercial Software

DOE Office of Scientific and Technical Information (OSTI.GOV)

Diao, Ruisheng; Jin, Shuangshuang; Howell, Frederic

Time-domain simulations are heavily used in today’s planning and operation practices to assess power system transient stability and post-transient voltage/frequency profiles following severe contingencies to comply with industry standards. Because of the increased modeling complexity, it is several times slower than real time for state-of-the-art commercial packages to complete a dynamic simulation for a large-scale model. With the growing stochastic behavior introduced by emerging technologies, power industry has seen a growing need for performing security assessment in real time. This paper presents a parallel implementation framework to speed up a single dynamic simulation by leveraging the existing stability model librarymore » in commercial tools through their application programming interfaces (APIs). Several high performance computing (HPC) techniques are explored such as parallelizing the calculation of generator current injection, identifying fast linear solvers for network solution, and parallelizing data outputs when interacting with APIs in the commercial package, TSAT. The proposed method has been tested on a WECC planning base case with detailed synchronous generator models and exhibits outstanding scalable performance with sufficient accuracy.« less

Test plan : Branson TRIP travel time/data accuracy

DOT National Transportation Integrated Search

2000-04-01

In the mid 1990's the FHWA established a High Performance Concrete (HPC) program aimed at demonstrating the positive effects of utilizing HPC in bridges. Research on the benefits of using high performance concrete for bridges has shown a number of be...

Design and performance of crack-free environmentally friendly concrete "crack-free eco-crete".

DOT National Transportation Integrated Search

2014-08-01

High-performance concrete (HPC) is characterized by high content of cement and supplementary cementitious materials (SCMs). : Using high binder content, low water-to-cementitious material ratio (w/cm), and various chemical admixtures in the HPC can r...

Direct numerical simulation of reactor two-phase flows enabled by high-performance computing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fang, Jun; Cambareri, Joseph J.; Brown, Cameron S.

Nuclear reactor two-phase flows remain a great engineering challenge, where the high-resolution two-phase flow database which can inform practical model development is still sparse due to the extreme reactor operation conditions and measurement difficulties. Owing to the rapid growth of computing power, the direct numerical simulation (DNS) is enjoying a renewed interest in investigating the related flow problems. A combination between DNS and an interface tracking method can provide a unique opportunity to study two-phase flows based on first principles calculations. More importantly, state-of-the-art high-performance computing (HPC) facilities are helping unlock this great potential. This paper reviews the recent researchmore » progress of two-phase flow DNS related to reactor applications. The progress in large-scale bubbly flow DNS has been focused not only on the sheer size of those simulations in terms of resolved Reynolds number, but also on the associated advanced modeling and analysis techniques. Specifically, the current areas of active research include modeling of sub-cooled boiling, bubble coalescence, as well as the advanced post-processing toolkit for bubbly flow simulations in reactor geometries. A novel bubble tracking method has been developed to track the evolution of bubbles in two-phase bubbly flow. Also, spectral analysis of DNS database in different geometries has been performed to investigate the modulation of the energy spectrum slope due to bubble-induced turbulence. In addition, the single-and two-phase analysis results are presented for turbulent flows within the pressurized water reactor (PWR) core geometries. The related simulations are possible to carry out only with the world leading HPC platforms. These simulations are allowing more complex turbulence model development and validation for use in 3D multiphase computational fluid dynamics (M-CFD) codes.« less

Performance Refactoring of Instrumentation, Measurement, and Analysis Technologies for Petascale Computing. The PRIMA Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Malony, Allen D.; Wolf, Felix G.

2014-01-31

The growing number of cores provided by today’s high-­end computing systems present substantial challenges to application developers in their pursuit of parallel efficiency. To find the most effective optimization strategy, application developers need insight into the runtime behavior of their code. The University of Oregon (UO) and the Juelich Supercomputing Centre of Forschungszentrum Juelich (FZJ) develop the performance analysis tools TAU and Scalasca, respectively, which allow high-­performance computing (HPC) users to collect and analyze relevant performance data – even at very large scales. TAU and Scalasca are considered among the most advanced parallel performance systems available, and are used extensivelymore » across HPC centers in the U.S., Germany, and around the world. The TAU and Scalasca groups share a heritage of parallel performance tool research and partnership throughout the past fifteen years. Indeed, the close interactions of the two groups resulted in a cross-­fertilization of tool ideas and technologies that pushed TAU and Scalasca to what they are today. It also produced two performance systems with an increasing degree of functional overlap. While each tool has its specific analysis focus, the tools were implementing measurement infrastructures that were substantially similar. Because each tool provides complementary performance analysis, sharing of measurement results is valuable to provide the user with more facets to understand performance behavior. However, each measurement system was producing performance data in different formats, requiring data interoperability tools to be created. A common measurement and instrumentation system was needed to more closely integrate TAU and Scalasca and to avoid the duplication of development and maintenance effort. The PRIMA (Performance Refactoring of Instrumentation, Measurement, and Analysis) project was proposed over three years ago as a joint international effort between UO and FZJ to accomplish these objectives: (1) refactor TAU and Scalasca performance system components for core code sharing and (2) integrate TAU and Scalasca functionality through data interfaces, formats, and utilities. As presented in this report, the project has completed these goals. In addition to shared technical advances, the groups have worked to engage with users through application performance engineering and tools training. In this regard, the project benefits from the close interactions the teams have with national laboratories in the United States and Germany. We have also sought to enhance our interactions through joint tutorials and outreach. UO has become a member of the Virtual Institute of High-­Productivity Supercomputing (VI-­HPS) established by the Helmholtz Association of German Research Centres as a center of excellence, focusing on HPC tools for diagnosing programming errors and optimizing performance. UO and FZJ have conducted several VI-­HPS training activities together within the past three years.« less

Performance Refactoring of Instrumentation, Measurement, and Analysis Technologies for Petascale Computing: the PRIMA Project

DOE Office of Scientific and Technical Information (OSTI.GOV)

Malony, Allen D.; Wolf, Felix G.

2014-01-31

The growing number of cores provided by today’s high-end computing systems present substantial challenges to application developers in their pursuit of parallel efficiency. To find the most effective optimization strategy, application developers need insight into the runtime behavior of their code. The University of Oregon (UO) and the Juelich Supercomputing Centre of Forschungszentrum Juelich (FZJ) develop the performance analysis tools TAU and Scalasca, respectively, which allow high-performance computing (HPC) users to collect and analyze relevant performance data – even at very large scales. TAU and Scalasca are considered among the most advanced parallel performance systems available, and are used extensivelymore » across HPC centers in the U.S., Germany, and around the world. The TAU and Scalasca groups share a heritage of parallel performance tool research and partnership throughout the past fifteen years. Indeed, the close interactions of the two groups resulted in a cross-fertilization of tool ideas and technologies that pushed TAU and Scalasca to what they are today. It also produced two performance systems with an increasing degree of functional overlap. While each tool has its specific analysis focus, the tools were implementing measurement infrastructures that were substantially similar. Because each tool provides complementary performance analysis, sharing of measurement results is valuable to provide the user with more facets to understand performance behavior. However, each measurement system was producing performance data in different formats, requiring data interoperability tools to be created. A common measurement and instrumentation system was needed to more closely integrate TAU and Scalasca and to avoid the duplication of development and maintenance effort. The PRIMA (Performance Refactoring of Instrumentation, Measurement, and Analysis) project was proposed over three years ago as a joint international effort between UO and FZJ to accomplish these objectives: (1) refactor TAU and Scalasca performance system components for core code sharing and (2) integrate TAU and Scalasca functionality through data interfaces, formats, and utilities. As presented in this report, the project has completed these goals. In addition to shared technical advances, the groups have worked to engage with users through application performance engineering and tools training. In this regard, the project benefits from the close interactions the teams have with national laboratories in the United States and Germany. We have also sought to enhance our interactions through joint tutorials and outreach. UO has become a member of the Virtual Institute of High-Productivity Supercomputing (VI-HPS) established by the Helmholtz Association of German Research Centres as a center of excellence, focusing on HPC tools for diagnosing programming errors and optimizing performance. UO and FZJ have conducted several VI-HPS training activities together within the past three years.« less

Could Blobs Fuel Storage-Based Convergence between HPC and Big Data?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Matri, Pierre; Alforov, Yevhen; Brandon, Alvaro

The increasingly growing data sets processed on HPC platforms raise major challenges for the underlying storage layer. A promising alternative to POSIX-IO- compliant file systems are simpler blobs (binary large objects), or object storage systems. Such systems offer lower overhead and better performance at the cost of largely unused features such as file hierarchies or permissions. Similarly, blobs are increasingly considered for replacing distributed file systems for big data analytics or as a base for storage abstractions such as key-value stores or time-series databases. This growing interest in such object storage on HPC and big data platforms raises the question:more » Are blobs the right level of abstraction to enable storage-based convergence between HPC and Big Data? In this paper we study the impact of blob-based storage for real-world applications on HPC and cloud environments. The results show that blobbased storage convergence is possible, leading to a significant performance improvement on both platforms« less

WMT: The CSDMS Web Modeling Tool

NASA Astrophysics Data System (ADS)

Piper, M.; Hutton, E. W. H.; Overeem, I.; Syvitski, J. P.

2015-12-01

The Community Surface Dynamics Modeling System (CSDMS) has a mission to enable model use and development for research in earth surface processes. CSDMS strives to expand the use of quantitative modeling techniques, promotes best practices in coding, and advocates for the use of open-source software. To streamline and standardize access to models, CSDMS has developed the Web Modeling Tool (WMT), a RESTful web application with a client-side graphical interface and a server-side database and API that allows users to build coupled surface dynamics models in a web browser on a personal computer or a mobile device, and run them in a high-performance computing (HPC) environment. With WMT, users can: Design a model from a set of components Edit component parameters Save models to a web-accessible server Share saved models with the community Submit runs to an HPC system Download simulation results The WMT client is an Ajax application written in Java with GWT, which allows developers to employ object-oriented design principles and development tools such as Ant, Eclipse and JUnit. For deployment on the web, the GWT compiler translates Java code to optimized and obfuscated JavaScript. The WMT client is supported on Firefox, Chrome, Safari, and Internet Explorer. The WMT server, written in Python and SQLite, is a layered system, with each layer exposing a web service API: wmt-db: database of component, model, and simulation metadata and output wmt-api: configure and connect components wmt-exe: launch simulations on remote execution servers The database server provides, as JSON-encoded messages, the metadata for users to couple model components, including descriptions of component exchange items, uses and provides ports, and input parameters. Execution servers are network-accessible computational resources, ranging from HPC systems to desktop computers, containing the CSDMS software stack for running a simulation. Once a simulation completes, its output, in NetCDF, is packaged and uploaded to a data server where it is stored and from which a user can download it as a single compressed archive file.

DOD HPC Insights. Spring 2012

DTIC Science & Technology

2012-04-01

spr2012-Final.indd 1 5/9/2012 10:54:32 AM DOD Spring 2012 INSIGHTS A publication of the Department of Defense High Performance Computing...0188 Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing...collection of information if it does not display a currently valid OMB control number. 1 . REPORT DATE 2012 2. REPORT TYPE 3. DATES COVERED 4. TITLE

Rebuilding the NAVSEA Early Stage Ship Design Environment

DTIC Science & Technology

2010-04-01

rules -of- thumb to base these crucial decisions upon. With High Performance Computing (HPC) as an enabler, the vision is to explore all downstream...the results of the analysis back into LEAPS. Another software development worthy of discussion here is Intelligent Ship Arrangements ( ISA ), which...constraints and rules set by the users ahead of time. When used in a systematic and stochastic way, and when integrated using LEAPS, having this

Visualization Development of the Ballistic Threat Geospatial Optimization

DTIC Science & Technology

2015-07-01

topographic globes, Keyhole Markup Language (KML), and Collada files. World Wind gives the user the ability to import 3-D models and navigate...present. After the first person view window is closed , the images stored in memory are then converted to a QuickTime movie (.MOV). The video will be...processing unit HPC high-performance computing JOGL Java implementation of OpenGL KML Keyhole Markup Language NASA National Aeronautics and Space

Investigation into shrinkage of high-performance concrete used for Iowa bridge decks and overlays.

DOT National Transportation Integrated Search

2013-09-01

High-performance concrete (HPC) overlays have been used increasingly as an effective and economical method for bridge decks in Iowa and other states. However, due to its high cementitious material content, HPC often displays high shrinkage cracking p...

Economical and crack-free high-performance concrete for pavement and transportation infrastructure construction.

DOT National Transportation Integrated Search

2017-05-01

The main objective of this research is to develop and validate the behavior of a new class of environmentally friendly and costeffective : high-performance concrete (HPC) referred to herein as Eco-HPC. The proposed project aimed at developing two cla...

UPLC-QTOFMS-based metabolomic analysis of the serum of hypoxic preconditioning mice

PubMed Central

Liu, Jie; Zhang, Gang; Chen, Dewei; Chen, Jian; Yuan, Zhi-Bin; Zhang, Er-Long; Gao, Yi-Xing; Xu, Gang; Sun, Bing-Da; Liao, Wenting; Gao, Yu-Qi

2017-01-01

Hypoxic preconditioning (HPC) is well-known to exert a protective effect against hypoxic injury; however, the underlying molecular mechanism remains unclear. The present study utilized a serum metabolomics approach to detect the alterations associated with HPC. In the present study, an animal model of HPC was established by exposing adult BALB/c mice to acute repetitive hypoxia four times. The serum samples were collected by orbital blood sampling. Metabolite profiling was performed using ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOFMS), in conjunction with univariate and multivariate statistical analyses. The results of the present study confirmed that the HPC mouse model was established and refined, suggesting significant differences between the control and HPC groups at the molecular levels. HPC caused significant metabolic alterations, as represented by the significant upregulation of valine, methionine, tyrosine, isoleucine, phenylalanine, lysophosphatidylcholine (LysoPC; 16:1), LysoPC (22:6), linoelaidylcarnitine, palmitoylcarnitine, octadecenoylcarnitine, taurine, arachidonic acid, linoleic acid, oleic acid and palmitic acid, and the downregulation of acetylcarnitine, malate, citrate and succinate. Using MetaboAnalyst 3.0, a number of key metabolic pathways were observed to be acutely perturbed, including valine, leucine and isoleucine biosynthesis, in addition to taurine, hypotaurine, phenylalanine, linoleic acid and arachidonic acid metabolism. The results of the present study provided novel insights into the mechanisms involved in the acclimatization of organisms to hypoxia, and demonstrated the protective mechanism of HPC. PMID:28901489

PHoToNs–A parallel heterogeneous and threads oriented code for cosmological N-body simulation

NASA Astrophysics Data System (ADS)

Wang, Qiao; Cao, Zong-Yan; Gao, Liang; Chi, Xue-Bin; Meng, Chen; Wang, Jie; Wang, Long

2018-06-01

We introduce a new code for cosmological simulations, PHoToNs, which incorporates features for performing massive cosmological simulations on heterogeneous high performance computer (HPC) systems and threads oriented programming. PHoToNs adopts a hybrid scheme to compute gravitational force, with the conventional Particle-Mesh (PM) algorithm to compute the long-range force, the Tree algorithm to compute the short range force and the direct summation Particle-Particle (PP) algorithm to compute gravity from very close particles. A self-similar space filling a Peano-Hilbert curve is used to decompose the computing domain. Threads programming is advantageously used to more flexibly manage the domain communication, PM calculation and synchronization, as well as Dual Tree Traversal on the CPU+MIC platform. PHoToNs scales well and efficiency of the PP kernel achieves 68.6% of peak performance on MIC and 74.4% on CPU platforms. We also test the accuracy of the code against the much used Gadget-2 in the community and found excellent agreement.

An Assessment of the State-of-the-art in Multidisciplinary Aeromechanical Analyses

NASA Technical Reports Server (NTRS)

Datta, Anubhav; Johnson, Wayne

2008-01-01

This paper presents a survey of the current state-of-the-art in multidisciplinary aeromechanical analyses which integrate advanced Computational Structural Dynamics (CSD) and Computational Fluid Dynamics (CFD) methods. The application areas to be surveyed include fixed wing aircraft, turbomachinery, and rotary wing aircraft. The objective of the authors in the present paper, together with a companion paper on requirements, is to lay out a path for a High Performance Computing (HPC) based next generation comprehensive rotorcraft analysis. From this survey of the key technologies in other application areas it is possible to identify the critical technology gaps that stem from unique rotorcraft requirements.

Dust in the wind: challenges for urban aerodynamics

NASA Astrophysics Data System (ADS)

Boris, Jay P.

2007-04-01

The fluid dynamics of airflow through a city controls the transport and dispersion of airborne contaminants. This is urban aerodynamics, not meteorology. The average flow, large-scale fluctuations and turbulence are closely coupled to the building geometry. Buildings create large "rooster-tail" wakes; there are systematic fountain flows up the backs of tall buildings; and dust in the wind can move perpendicular to or even against the locally prevailing wind. Requirements for better prediction accuracy demand time-dependent, three-dimensional CFD computations that include solar heating and buoyancy, complete landscape and building geometry specification including foliage and, realistic wind fluctuations. This fundamental prediction capability is necessary to assess urban visibility and line-of-sight sensor performance in street canyons and rugged terrain. Computing urban aerodynamics accurately is clearly a time-dependent High Performance Computing (HPC) problem. In an emergency, on the other hand, prediction technology to assess crisis information, sensor performance, and obscured line-of-sight propagation in the face of industrial spills, transportation accidents, or terrorist attacks has very tight time requirements that suggest simple approximations which tend to produce inaccurate results. In the past we have had to choose one or the other: a fast, inaccurate model or a slow accurate model. Using new fluid-dynamic principles, an urban-oriented emergency assessment system called CT-Analyst® was invented that solves this dilemma. It produces HPC-quality results for airborne contaminant scenarios nearly instantly and has unique new capabilities suited to sensor optimization. This presentation treats the design and use of CT-Analyst and discusses the developments needed for widespread use with advanced sensor and communication systems.

Freestanding hierarchically porous carbon framework decorated by polyaniline as binder-free electrodes for high performance supercapacitors

NASA Astrophysics Data System (ADS)

Miao, Fujun; Shao, Changlu; Li, Xinghua; Wang, Kexin; Lu, Na; Liu, Yichun

2016-10-01

Freestanding hierarchically porous carbon electrode materials with favorable features of large surface areas, hierarchical porosity and continuous conducting pathways are very attractive for practical applications in electrochemical devices. Herein, three-dimensional freestanding hierarchically porous carbon (HPC) materials have been fabricated successfully mainly by the facile phase separation method. In order to further improve the energy storage ability, polyaniline (PANI) with high pseudocapacitance has been decorated on HPC through in situ chemical polymerization of aniline monomers. Benefiting from the synergistic effects between HPC and PANI, the resulting HPC/PANI composites as electrode materials present dramatic electrochemical performance with high specific capacitance up to 290 F g-1 at 0.5 A g-1 and good rate capability with ∼86% (248 F g-1) capacitance retention at 64 A g-1 of initial capacitance in three-electrode configuration. Moreover, the as-assembled symmetric supercapacitor based on HPC/PANI composites also demonstrates good capacitive properties with high energy density of 9.6 Wh kg-1 at 223 W kg-1 and long-term cycling stability with 78% capacitance retention after 10 000 cycles. Therefore, this work provides a new approach for designing high-performance electrodes with exceptional electrochemical performance, which are very promising for practical application in the energy storage field.

Scalable Analysis Methods and In Situ Infrastructure for Extreme Scale Knowledge Discovery

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bethel, Wes

2016-07-24

The primary challenge motivating this team’s work is the widening gap between the ability to compute information and to store it for subsequent analysis. This gap adversely impacts science code teams, who are able to perform analysis only on a small fraction of the data they compute, resulting in the very real likelihood of lost or missed science, when results are computed but not analyzed. Our approach is to perform as much analysis or visualization processing on data while it is still resident in memory, an approach that is known as in situ processing. The idea in situ processing wasmore » not new at the time of the start of this effort in 2014, but efforts in that space were largely ad hoc, and there was no concerted effort within the research community that aimed to foster production-quality software tools suitable for use by DOE science projects. In large, our objective was produce and enable use of production-quality in situ methods and infrastructure, at scale, on DOE HPC facilities, though we expected to have impact beyond DOE due to the widespread nature of the challenges, which affect virtually all large-scale computational science efforts. To achieve that objective, we assembled a unique team of researchers consisting of representatives from DOE national laboratories, academia, and industry, and engaged in software technology R&D, as well as engaged in close partnerships with DOE science code teams, to produce software technologies that were shown to run effectively at scale on DOE HPC platforms.« less

Parallel Application Performance on Two Generations of Intel Xeon HPC Platforms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chang, Christopher H.; Long, Hai; Sides, Scott

2015-10-15

Two next-generation node configurations hosting the Haswell microarchitecture were tested with a suite of microbenchmarks and application examples, and compared with a current Ivy Bridge production node on NREL" tm s Peregrine high-performance computing cluster. A primary conclusion from this study is that the additional cores are of little value to individual task performance--limitations to application parallelism, or resource contention among concurrently running but independent tasks, limits effective utilization of these added cores. Hyperthreading generally impacts throughput negatively, but can improve performance in the absence of detailed attention to runtime workflow configuration. The observations offer some guidance to procurement ofmore » future HPC systems at NREL. First, raw core count must be balanced with available resources, particularly memory bandwidth. Balance-of-system will determine value more than processor capability alone. Second, hyperthreading continues to be largely irrelevant to the workloads that are commonly seen, and were tested here, at NREL. Finally, perhaps the most impactful enhancement to productivity might occur through enabling multiple concurrent jobs per node. Given the right type and size of workload, more may be achieved by doing many slow things at once, than fast things in order.« less

Constructive Engineering of Simulations

NASA Technical Reports Server (NTRS)

Snyder, Daniel R.; Barsness, Brendan

2011-01-01

Joint experimentation that investigates sensor optimization, re-tasking and management has far reaching implications for Department of Defense, Interagency and multinational partners. An adaption of traditional human in the loop (HITL) Modeling and Simulation (M&S) was one approach used to generate the findings necessary to derive and support these implications. Here an entity-based simulation was re-engineered to run on USJFCOM's High Performance Computer (HPC). The HPC was used to support the vast number of constructive runs necessary to produce statistically significant data in a timely manner. Then from the resulting sensitivity analysis, event designers blended the necessary visualization and decision making components into a synthetic environment for the HITL simulations trials. These trials focused on areas where human decision making had the greatest impact on the sensor investigations. Thus, this paper discusses how re-engineering existing M&S for constructive applications can positively influence the design of an associated HITL experiment.

A web portal for hydrodynamical, cosmological simulations

NASA Astrophysics Data System (ADS)

Ragagnin, A.; Dolag, K.; Biffi, V.; Cadolle Bel, M.; Hammer, N. J.; Krukau, A.; Petkova, M.; Steinborn, D.

2017-07-01

This article describes a data centre hosting a web portal for accessing and sharing the output of large, cosmological, hydro-dynamical simulations with a broad scientific community. It also allows users to receive related scientific data products by directly processing the raw simulation data on a remote computing cluster. The data centre has a multi-layer structure: a web portal, a job control layer, a computing cluster and a HPC storage system. The outer layer enables users to choose an object from the simulations. Objects can be selected by visually inspecting 2D maps of the simulation data, by performing highly compounded and elaborated queries or graphically by plotting arbitrary combinations of properties. The user can run analysis tools on a chosen object. These services allow users to run analysis tools on the raw simulation data. The job control layer is responsible for handling and performing the analysis jobs, which are executed on a computing cluster. The innermost layer is formed by a HPC storage system which hosts the large, raw simulation data. The following services are available for the users: (I) CLUSTERINSPECT visualizes properties of member galaxies of a selected galaxy cluster; (II) SIMCUT returns the raw data of a sub-volume around a selected object from a simulation, containing all the original, hydro-dynamical quantities; (III) SMAC creates idealized 2D maps of various, physical quantities and observables of a selected object; (IV) PHOX generates virtual X-ray observations with specifications of various current and upcoming instruments.

Guest Editorial High Performance Computing (HPC) Applications for a More Resilient and Efficient Power Grid

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, Zhenyu Henry; Tate, Zeb; Abhyankar, Shrirang

The power grid has been evolving over the last 120 years, but it is seeing more changes in this decade and next than it has seen over the past century. In particular, the widespread deployment of intermittent renewable generation, smart loads and devices, hierarchical and distributed control technologies, phasor measurement units, energy storage, and widespread usage of electric vehicles will require fundamental changes in methods and tools for the operation and planning of the power grid. The resulting new dynamic and stochastic behaviors will demand the inclusion of more complexity in modeling the power grid. Solving such complex models inmore » the traditional computing environment will be a major challenge. Along with the increasing complexity of power system models, the increasing complexity of smart grid data further adds to the prevailing challenges. In this environment, the myriad of smart sensors and meters in the power grid increase by multiple orders of magnitude, so do the volume and speed of the data. The information infrastructure will need to drastically change to support the exchange of enormous amounts of data as smart grid applications will need the capability to collect, assimilate, analyze and process the data, to meet real-time grid functions. High performance computing (HPC) holds the promise to enhance these functions, but it is a great resource that has not been fully explored and adopted for the power grid domain.« less

Efficient Machine Learning Approach for Optimizing Scientific Computing Applications on Emerging HPC Architectures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Arumugam, Kamesh

Efficient parallel implementations of scientific applications on multi-core CPUs with accelerators such as GPUs and Xeon Phis is challenging. This requires - exploiting the data parallel architecture of the accelerator along with the vector pipelines of modern x86 CPU architectures, load balancing, and efficient memory transfer between different devices. It is relatively easy to meet these requirements for highly structured scientific applications. In contrast, a number of scientific and engineering applications are unstructured. Getting performance on accelerators for these applications is extremely challenging because many of these applications employ irregular algorithms which exhibit data-dependent control-ow and irregular memory accesses. Furthermore,more » these applications are often iterative with dependency between steps, and thus making it hard to parallelize across steps. As a result, parallelism in these applications is often limited to a single step. Numerical simulation of charged particles beam dynamics is one such application where the distribution of work and memory access pattern at each time step is irregular. Applications with these properties tend to present significant branch and memory divergence, load imbalance between different processor cores, and poor compute and memory utilization. Prior research on parallelizing such irregular applications have been focused around optimizing the irregular, data-dependent memory accesses and control-ow during a single step of the application independent of the other steps, with the assumption that these patterns are completely unpredictable. We observed that the structure of computation leading to control-ow divergence and irregular memory accesses in one step is similar to that in the next step. It is possible to predict this structure in the current step by observing the computation structure of previous steps. In this dissertation, we present novel machine learning based optimization techniques to address the parallel implementation challenges of such irregular applications on different HPC architectures. In particular, we use supervised learning to predict the computation structure and use it to address the control-ow and memory access irregularities in the parallel implementation of such applications on GPUs, Xeon Phis, and heterogeneous architectures composed of multi-core CPUs with GPUs or Xeon Phis. We use numerical simulation of charged particles beam dynamics simulation as a motivating example throughout the dissertation to present our new approach, though they should be equally applicable to a wide range of irregular applications. The machine learning approach presented here use predictive analytics and forecasting techniques to adaptively model and track the irregular memory access pattern at each time step of the simulation to anticipate the future memory access pattern. Access pattern forecasts can then be used to formulate optimization decisions during application execution which improves the performance of the application at a future time step based on the observations from earlier time steps. In heterogeneous architectures, forecasts can also be used to improve the memory performance and resource utilization of all the processing units to deliver a good aggregate performance. We used these optimization techniques and anticipation strategy to design a cache-aware, memory efficient parallel algorithm to address the irregularities in the parallel implementation of charged particles beam dynamics simulation on different HPC architectures. Experimental result using a diverse mix of HPC architectures shows that our approach in using anticipation strategy is effective in maximizing data reuse, ensuring workload balance, minimizing branch and memory divergence, and in improving resource utilization.« less

Power-Time Curve Comparison between Weightlifting Derivatives

PubMed Central

Suchomel, Timothy J.; Sole, Christopher J.

2017-01-01

This study examined the power production differences between weightlifting derivatives through a comparison of power-time (P-t) curves. Thirteen resistance-trained males performed hang power clean (HPC), jump shrug (JS), and hang high pull (HHP) repetitions at relative loads of 30%, 45%, 65%, and 80% of their one repetition maximum (1RM) HPC. Relative peak power (PPRel), work (WRel), and P-t curves were compared. The JS produced greater PPRel than the HPC (p < 0.001, d = 2.53) and the HHP (p < 0.001, d = 2.14). In addition, the HHP PPRel was statistically greater than the HPC (p = 0.008, d = 0.80). Similarly, the JS produced greater WRel compared to the HPC (p < 0.001, d = 1.89) and HHP (p < 0.001, d = 1.42). Furthermore, HHP WRel was statistically greater than the HPC (p = 0.003, d = 0.73). The P-t profiles of each exercise were similar during the first 80-85% of the movement; however, during the final 15-20% of the movement the P-t profile of the JS was found to be greater than the HPC and HHP. The JS produced greater PPRel and WRel compared to the HPC and HHP with large effect size differences. The HHP produced greater PPRel and WRel than the HPC with moderate effect size differences. The JS and HHP produced markedly different P-t profiles in the final 15-20% of the movement compared to the HPC. Thus, these exercises may be superior methods of training to enhance PPRel. The greatest differences in PPRel between the JS and HHP and the HPC occurred at lighter loads, suggesting that loads of 30-45% 1RM HPC may provide the best training stimulus when using the JS and HHP. In contrast, loads ranging 65-80% 1RM HPC may provide an optimal stimulus for power production during the HPC. Key points The JS and HHP exercises produced greater relative peak power and relative work compared to the HPC. Although the power-time curves were similar during the first 80-85% of the movement, the JS and HHP possessed unique power-time characteristics during the final 15-20% of the movement compared to the HPC. The JS and HHP may be effectively implemented to train peak power characteristics, especially using loads ranging from 30-45% of an individual’s 1RM HPC. The HPC may be best implemented using loads ranging from 65-80% of an individual’s 1RM HPC. PMID:28912659

Using Rollback Avoidance to Mitigate Failures in Next-Generation Extreme-Scale Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Levy, Scott N.

2016-05-01

High-performance computing (HPC) systems enable scientists to numerically model complex phenomena in many important physical systems. The next major milestone in the development of HPC systems is the construction of the rst supercomputer capable executing more than an exa op, 10 18 oating point operations per second. On systems of this scale, failures will occur much more frequently than on current systems. As a result, resilience is a key obstacle to building next-generation extremescale systems. Coordinated checkpointing is currently the most widely-used mechanism for handling failures on HPC systems. Although coordinated checkpointing remains e ective on current systems, increasing themore » scale of today's systems to build next-generation systems will increase the cost of fault tolerance as more and more time is taken away from the application to protect against or recover from failure. Rollback avoidance techniques seek to mitigate the cost of checkpoint/restart by allowing an application to continue its execution rather than rolling back to an earlier checkpoint when failures occur. These techniqes include failure prediction and preventive migration, replicated computation, fault-tolerant algorithms, and softwarebased memory fault correction. In this thesis, we examine how rollback avoidance techniques can be used to address failures on extreme-scale systems. Using a combination of analytic modeling and simulation, we evaluate the potential impact of rollback avoidance on these systems. We then present a novel rollback avoidance technique that exploits similarities in application memory. Finally, we examine the feasibility of using this technique to protect against memory faults in kernel memory.« less

Modeling Cardiac Electrophysiology at the Organ Level in the Peta FLOPS Computing Age

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mitchell, Lawrence; Bishop, Martin; Hoetzl, Elena

2010-09-30

Despite a steep increase in available compute power, in-silico experimentation with highly detailed models of the heart remains to be challenging due to the high computational cost involved. It is hoped that next generation high performance computing (HPC) resources lead to significant reductions in execution times to leverage a new class of in-silico applications. However, performance gains with these new platforms can only be achieved by engaging a much larger number of compute cores, necessitating strongly scalable numerical techniques. So far strong scalability has been demonstrated only for a moderate number of cores, orders of magnitude below the range requiredmore » to achieve the desired performance boost.In this study, strong scalability of currently used techniques to solve the bidomain equations is investigated. Benchmark results suggest that scalability is limited to 512-4096 cores within the range of relevant problem sizes even when systems are carefully load-balanced and advanced IO strategies are employed.« less

Integrating Reconfigurable Hardware-Based Grid for High Performance Computing

PubMed Central

Dondo Gazzano, Julio; Sanchez Molina, Francisco; Rincon, Fernando; López, Juan Carlos

2015-01-01

FPGAs have shown several characteristics that make them very attractive for high performance computing (HPC). The impressive speed-up factors that they are able to achieve, the reduced power consumption, and the easiness and flexibility of the design process with fast iterations between consecutive versions are examples of benefits obtained with their use. However, there are still some difficulties when using reconfigurable platforms as accelerator that need to be addressed: the need of an in-depth application study to identify potential acceleration, the lack of tools for the deployment of computational problems in distributed hardware platforms, and the low portability of components, among others. This work proposes a complete grid infrastructure for distributed high performance computing based on dynamically reconfigurable FPGAs. Besides, a set of services designed to facilitate the application deployment is described. An example application and a comparison with other hardware and software implementations are shown. Experimental results show that the proposed architecture offers encouraging advantages for deployment of high performance distributed applications simplifying development process. PMID:25874241

Making Cloud Computing Available For Researchers and Innovators (Invited)

NASA Astrophysics Data System (ADS)

Winsor, R.

2010-12-01

High Performance Computing (HPC) facilities exist in most academic institutions but are almost invariably over-subscribed. Access is allocated based on academic merit, the only practical method of assigning valuable finite compute resources. Cloud computing on the other hand, and particularly commercial clouds, draw flexibly on an almost limitless resource as long as the user has sufficient funds to pay the bill. How can the commercial cloud model be applied to scientific computing? Is there a case to be made for a publicly available research cloud and how would it be structured? This talk will explore these themes and describe how Cybera, a not-for-profit non-governmental organization in Alberta Canada, aims to leverage its high speed research and education network to provide cloud computing facilities for a much wider user base.

MRS study of meningeal hemangiopericytoma and edema: a comparison with meningothelial meningioma.

PubMed

Righi, Valeria; Tugnoli, Vitaliano; Mucci, Adele; Bacci, Antonella; Bonora, Sergio; Schenetti, Luisa

2012-10-01

Intracranial hemangiopericytomas (HPCs) are rare tumors and their radiological appearance resembles that of meningiomas, especially meningothelial meningiomas. To increase the knowledge on the biochemical composition of this type of tumor for better diagnosis and prognosis, we performed a molecular study using ex vivo high resolution magic angle spinning (HR-MAS) magnetic resonance spectroscopy (MRS) perfomed on HPC and peritumoral edematous tissues. Moreover, to help in the discrimination between HPC and meningothelial meningioma we compared the ex vivo HR-MAS spectra of samples from one patient with HPC and 5 patients affected by meningothelial meningioma. Magnetic resonance imaging (MRI), in vivo localized single voxel 1H-MRS was also performed on the same patients prior to surgery and the in vivo and ex vivo MRS spectra were compared. We observed the presence of OH-butyrate, together with glucose in HPC and a low amount of N-acetylaspartate in the edema, that may reflect neuronal alteration responsible for associated epilepsy. Many differences between HPC and meningothelial meningioma were identified. The relative ratios of myo-inositol, glucose and gluthatione with respect to glutamate are higher in HPC compared to meningioma; whereas the relative ratios of creatine, glutamine, alanine, glycine and choline-containing compounds with respect to glutamate are lower in HPC compared to meningioma. These data will be useful to improve the interpretation of in vivo MRS spectra resulting in a more accurate diagnosis of these rare tumors.

Continuous whole-system monitoring toward rapid understanding of production HPC applications and systems

DOE PAGES

Agelastos, Anthony; Allan, Benjamin; Brandt, Jim; ...

2016-05-18

A detailed understanding of HPC applications’ resource needs and their complex interactions with each other and HPC platform resources are critical to achieving scalability and performance. Such understanding has been difficult to achieve because typical application profiling tools do not capture the behaviors of codes under the potentially wide spectrum of actual production conditions and because typical monitoring tools do not capture system resource usage information with high enough fidelity to gain sufficient insight into application performance and demands. In this paper we present both system and application profiling results based on data obtained through synchronized system wide monitoring onmore » a production HPC cluster at Sandia National Laboratories (SNL). We demonstrate analytic and visualization techniques that we are using to characterize application and system resource usage under production conditions for better understanding of application resource needs. Furthermore, our goals are to improve application performance (through understanding application-to-resource mapping and system throughput) and to ensure that future system capabilities match their intended workloads.« less

Hierarchically Porous Carbon Materials for CO 2 Capture: The Role of Pore Structure

DOE Office of Scientific and Technical Information (OSTI.GOV)

Estevez, Luis; Barpaga, Dushyant; Zheng, Jian

2018-01-17

With advances in porous carbon synthesis techniques, hierarchically porous carbon (HPC) materials are being utilized as relatively new porous carbon sorbents for CO2 capture applications. These HPC materials were used as a platform to prepare samples with differing textural properties and morphologies to elucidate structure-property relationships. It was found that high microporous content, rather than overall surface area was of primary importance for predicting good CO2 capture performance. Two HPC materials were analyzed, each with near identical high surface area (~2700 m2/g) and colossally high pore volume (~10 cm3/g), but with different microporous content and pore size distributions, which ledmore » to dramatically different CO2 capture performance. Overall, large pore volumes obtained from distinct mesopores were found to significantly impact adsorption performance. From these results, an optimized HPC material was synthesized that achieved a high CO2 capacity of ~3.7 mmol/g at 25°C and 1 bar.« less

Introducing students to ocean modeling via a web-based implementation for the Regional Ocean Modeling System (ROMS) river plume case study

NASA Astrophysics Data System (ADS)

Harris, C. K.; Overeem, I.; Hutton, E.; Moriarty, J.; Wiberg, P.

2016-12-01

Numerical models are increasingly used for both research and applied sciences, and it is important that we train students to run models and analyze model data. This is especially true within oceanographic sciences, many of which use hydrodynamic models to address oceanographic transport problems. These models, however, often require a fair amount of training and computer skills before a student can run the models and analyze the large data sets produced by the models. One example is the Regional Ocean Modeling System (ROMS), an open source, three-dimensional primitive equation hydrodynamic ocean model that uses a structured curvilinear horizontal grid. It currently has thousands of users worldwide, and the full model includes modules for sediment transport and biogeochemistry, and several options for turbulence closures and numerical schemes. Implementing ROMS can be challenging to students, however, in part because the code was designed to provide flexibility for the choice of model parameterizations and processes, and to run on a variety of High Performance Computing (HPC) platforms. To provide a more accessible tool for classroom use, we have modified an existing idealized ROMS implementation to be run on a High Performance Computer (HPC) via the WMT (Web Modeling Toolkit), and developed a series of lesson plans that explore sediment transport within the idealized model domain. This has addressed our goal to provide a relatively easy introduction to the numerical modeling process that can be used within upper level undergraduate and graduate classes to explore sediment transport on continental shelves. The model implementation includes wave forcing, along-shelf currents, a riverine source, and suspended sediment transport. The model calculates suspended transport and deposition of sediment delivered to the continental shelf by a riverine flood. Lesson plans lead the students through running the model on a remote HPC, modifying the standard model. The lesson plans also include instruction for visualizing the model output within Matlab and Panoply. The lesson plans have been used within graduate, undergraduate classrooms, as well as in clinics aimed at educators. Feedback from these exercises has been used to improve the lesson plans and model implementation.

Use of Continuous Integration Tools for Application Performance Monitoring

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vergara Larrea, Veronica G; Joubert, Wayne; Fuson, Christopher B

High performance computing systems are becom- ing increasingly complex, both in node architecture and in the multiple layers of software stack required to compile and run applications. As a consequence, the likelihood is increasing for application performance regressions to occur as a result of routine upgrades of system software components which interact in complex ways. The purpose of this study is to evaluate the effectiveness of continuous integration tools for application performance monitoring on HPC systems. In addition, this paper also describes a prototype system for application perfor- mance monitoring based on Jenkins, a Java-based continuous integration tool. The monitoringmore » system described leverages several features in Jenkins to track application performance results over time. Preliminary results and lessons learned from monitoring applications on Cray systems at the Oak Ridge Leadership Computing Facility are presented.« less

Evaluation of FPGA to PC feedback loop

NASA Astrophysics Data System (ADS)

Linczuk, Pawel; Zabolotny, Wojciech M.; Wojenski, Andrzej; Krawczyk, Rafal D.; Pozniak, Krzysztof T.; Chernyshova, Maryna; Czarski, Tomasz; Gaska, Michal; Kasprowicz, Grzegorz; Kowalska-Strzeciwilk, Ewa; Malinowski, Karol

2017-08-01

The paper presents the evaluation study of the performance of the data transmission subsystem which can be used in High Energy Physics (HEP) and other High-Performance Computing (HPC) systems. The test environment consisted of Xilinx Artix-7 FPGA and server-grade PC connected via the PCIe 4xGen2 bus. The DMA engine was based on the Xilinx DMA for PCI Express Subsystem1 controlled by the modified Xilinx XDMA kernel driver.2 The research is focused on the influence of the system configuration on achievable throughput and latency of data transfer.

Development and construction of low-cracking high-performance concrete (LC-HPC) bridge decks : free shrinkage, moisture optimization and concrete production : final report.

DOT National Transportation Integrated Search

2009-08-01

The development and evaluation of low-cracking high-performance concrete (LC-HPC) for use in bridge decks : is described based on laboratory test results and experience gained during the construction of 14 bridges. This report : emphasizes the materi...

Development and construction of low-cracking high-performance concrete (LC-HPC) bridge decks : free shrinkage, moisture optimization and concrete production : summary report.

DOT National Transportation Integrated Search

2009-08-01

The development and evaluation of low-cracking high-performance concrete (LC-HPC) for use in bridge decks : is described based on laboratory test results and experience gained during the construction of 14 bridges. This report : emphasizes the materi...

Molgenis-impute: imputation pipeline in a box.

PubMed

Kanterakis, Alexandros; Deelen, Patrick; van Dijk, Freerk; Byelas, Heorhiy; Dijkstra, Martijn; Swertz, Morris A

2015-08-19

Genotype imputation is an important procedure in current genomic analysis such as genome-wide association studies, meta-analyses and fine mapping. Although high quality tools are available that perform the steps of this process, considerable effort and expertise is required to set up and run a best practice imputation pipeline, particularly for larger genotype datasets, where imputation has to scale out in parallel on computer clusters. Here we present MOLGENIS-impute, an 'imputation in a box' solution that seamlessly and transparently automates the set up and running of all the steps of the imputation process. These steps include genome build liftover (liftovering), genotype phasing with SHAPEIT2, quality control, sample and chromosomal chunking/merging, and imputation with IMPUTE2. MOLGENIS-impute builds on MOLGENIS-compute, a simple pipeline management platform for submission and monitoring of bioinformatics tasks in High Performance Computing (HPC) environments like local/cloud servers, clusters and grids. All the required tools, data and scripts are downloaded and installed in a single step. Researchers with diverse backgrounds and expertise have tested MOLGENIS-impute on different locations and imputed over 30,000 samples so far using the 1,000 Genomes Project and new Genome of the Netherlands data as the imputation reference. The tests have been performed on PBS/SGE clusters, cloud VMs and in a grid HPC environment. MOLGENIS-impute gives priority to the ease of setting up, configuring and running an imputation. It has minimal dependencies and wraps the pipeline in a simple command line interface, without sacrificing flexibility to adapt or limiting the options of underlying imputation tools. It does not require knowledge of a workflow system or programming, and is targeted at researchers who just want to apply best practices in imputation via simple commands. It is built on the MOLGENIS compute workflow framework to enable customization with additional computational steps or it can be included in other bioinformatics pipelines. It is available as open source from: https://github.com/molgenis/molgenis-imputation.

Adoption of High Performance Computational (HPC) Modeling Software for Widespread Use in the Manufacture of Welded Structures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brust, Frederick W.; Punch, Edward F.; Twombly, Elizabeth Kurth

This report summarizes the final product developed for the US DOE Small Business Innovation Research (SBIR) Phase II grant made to Engineering Mechanics Corporation of Columbus (Emc 2) between April 16, 2014 and August 31, 2016 titled ‘Adoption of High Performance Computational (HPC) Modeling Software for Widespread Use in the Manufacture of Welded Structures’. Many US companies have moved fabrication and production facilities off shore because of cheaper labor costs. A key aspect in bringing these jobs back to the US is the use of technology to render US-made fabrications more cost-efficient overall with higher quality. One significant advantage thatmore » has emerged in the US over the last two decades is the use of virtual design for fabrication of small and large structures in weld fabrication industries. Industries that use virtual design and analysis tools have reduced material part size, developed environmentally-friendly fabrication processes, improved product quality and performance, and reduced manufacturing costs. Indeed, Caterpillar Inc. (CAT), one of the partners in this effort, continues to have a large fabrication presence in the US because of the use of weld fabrication modeling to optimize fabrications by controlling weld residual stresses and distortions and improving fatigue, corrosion, and fracture performance. This report describes Emc 2’s DOE SBIR Phase II final results to extend an existing, state-of-the-art software code, Virtual Fabrication Technology (VFT®), currently used to design and model large welded structures prior to fabrication - to a broader range of products with widespread applications for small and medium-sized enterprises (SMEs). VFT® helps control distortion, can minimize and/or control residual stresses, control welding microstructure, and pre-determine welding parameters such as weld-sequencing, pre-bending, thermal-tensioning, etc. VFT® uses material properties, consumable properties, etc. as inputs. Through VFT®, manufacturing companies can avoid costly design changes after fabrication. This leads to the concept of joint design/fabrication where these important disciplines are intimately linked to minimize fabrication costs. Finally service performance (such as fatigue, corrosion, and fracture/damage) can be improved using this product. Emc 2’s DOE SBIR Phase II effort successfully adapted VFT® to perform efficiently in an HPC environment independent of commercial software on a platform to permit easy and cost effective access to the code. This provides the key for SMEs to access this sophisticated and proven methodology that is quick, accurate, cost effective and available “on-demand” to address weld-simulation and fabrication problems prior to manufacture. In addition, other organizations, such as Government agencies and large companies, may have a need for spot use of such a tool. The open source code, WARP3D, a high performance finite element code used in fracture and damage assessment of structures, was significantly modified so computational weld problems can be solved efficiently on multiple processors and threads with VFT®. The thermal solver for VFT®, based on a series of closed form solution approximations, was extensively enhanced for solution on multiple processors greatly increasing overall speed. In addition, the graphical user interface (GUI) was re-written to permit SMEs access to an HPC environment at the Ohio Super Computer Center (OSC) to integrate these solutions with WARP3D. The GUI is used to define all weld pass descriptions, number of passes, material properties, consumable properties, weld speed, etc. for the structure to be modeled. The GUI was enhanced to make it more user-friendly so that non-experts can perform weld modeling. Finally, an extensive outreach program to market this capability to fabrication companies was performed. This access will permit SMEs to perform weld modeling to improve their competitiveness at a reasonable cost.« less

Spatial data analytics on heterogeneous multi- and many-core parallel architectures using python

USGS Publications Warehouse

Laura, Jason R.; Rey, Sergio J.

2017-01-01

Parallel vector spatial analysis concerns the application of parallel computational methods to facilitate vector-based spatial analysis. The history of parallel computation in spatial analysis is reviewed, and this work is placed into the broader context of high-performance computing (HPC) and parallelization research. The rise of cyber infrastructure and its manifestation in spatial analysis as CyberGIScience is seen as a main driver of renewed interest in parallel computation in the spatial sciences. Key problems in spatial analysis that have been the focus of parallel computing are covered. Chief among these are spatial optimization problems, computational geometric problems including polygonization and spatial contiguity detection, the use of Monte Carlo Markov chain simulation in spatial statistics, and parallel implementations of spatial econometric methods. Future directions for research on parallelization in computational spatial analysis are outlined.

Recurrent extradural hemangiopericytoma of thoracic spine: a case report.

PubMed

Jayashankar, Erukkambattu; Prabhala, Shailaja; Raju, Subodh; Tanikella, Ramamurti

2014-01-01

Hemangiopericytoma (HPC) is a rare tumor that arises from pericapillary cells or pericytes of Zimmerman. In the central nervous system, it accounts for less than 1% of tumors, and spinal involvement is very rare. Meningeal hemangiopericytomas show morphological similarities with meningiomas particularly with angiomatous meningioma, where one needs to take the help of immunohistochemistry (IHC) to delineate HPC from meningioma. Here, we report a case of recurrent extradural HPC in a 16 year-old girl, who 5 years back had a pathological diagnosis of angiomatous meningioma, for D5-D6 lesion. On evaluation, magnetic resonance imaging (MRI) showed a large extradural tumor with a significant cord compression involving D5-D6 body, pedicle and ribs. Excision of the lesion and spinal stabilization was performed. The histopathological examination and immunohistochemistry performed on tumor sections revealed features favoring HPC. To conclude, detailed IHC is helpful in avoiding misdiagnosis and in further management of the patient.

Tigres Workflow Library: Supporting Scientific Pipelines on HPC Systems

DOE PAGES

Hendrix, Valerie; Fox, James; Ghoshal, Devarshi; ...

2016-07-21

The growth in scientific data volumes has resulted in the need for new tools that enable users to operate on and analyze data on large-scale resources. In the last decade, a number of scientific workflow tools have emerged. These tools often target distributed environments, and often need expert help to compose and execute the workflows. Data-intensive workflows are often ad-hoc, they involve an iterative development process that includes users composing and testing their workflows on desktops, and scaling up to larger systems. In this paper, we present the design and implementation of Tigres, a workflow library that supports the iterativemore » workflow development cycle of data-intensive workflows. Tigres provides an application programming interface to a set of programming templates i.e., sequence, parallel, split, merge, that can be used to compose and execute computational and data pipelines. We discuss the results of our evaluation of scientific and synthetic workflows showing Tigres performs with minimal template overheads (mean of 13 seconds over all experiments). We also discuss various factors (e.g., I/O performance, execution mechanisms) that affect the performance of scientific workflows on HPC systems.« less

Tigres Workflow Library: Supporting Scientific Pipelines on HPC Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hendrix, Valerie; Fox, James; Ghoshal, Devarshi

The growth in scientific data volumes has resulted in the need for new tools that enable users to operate on and analyze data on large-scale resources. In the last decade, a number of scientific workflow tools have emerged. These tools often target distributed environments, and often need expert help to compose and execute the workflows. Data-intensive workflows are often ad-hoc, they involve an iterative development process that includes users composing and testing their workflows on desktops, and scaling up to larger systems. In this paper, we present the design and implementation of Tigres, a workflow library that supports the iterativemore » workflow development cycle of data-intensive workflows. Tigres provides an application programming interface to a set of programming templates i.e., sequence, parallel, split, merge, that can be used to compose and execute computational and data pipelines. We discuss the results of our evaluation of scientific and synthetic workflows showing Tigres performs with minimal template overheads (mean of 13 seconds over all experiments). We also discuss various factors (e.g., I/O performance, execution mechanisms) that affect the performance of scientific workflows on HPC systems.« less

Spectral-element Seismic Wave Propagation on CUDA/OpenCL Hardware Accelerators

NASA Astrophysics Data System (ADS)

Peter, D. B.; Videau, B.; Pouget, K.; Komatitsch, D.

2015-12-01

Seismic wave propagation codes are essential tools to investigate a variety of wave phenomena in the Earth. Furthermore, they can now be used for seismic full-waveform inversions in regional- and global-scale adjoint tomography. Although these seismic wave propagation solvers are crucial ingredients to improve the resolution of tomographic images to answer important questions about the nature of Earth's internal processes and subsurface structure, their practical application is often limited due to high computational costs. They thus need high-performance computing (HPC) facilities to improving the current state of knowledge. At present, numerous large HPC systems embed many-core architectures such as graphics processing units (GPUs) to enhance numerical performance. Such hardware accelerators can be programmed using either the CUDA programming environment or the OpenCL language standard. CUDA software development targets NVIDIA graphic cards while OpenCL was adopted by additional hardware accelerators, like e.g. AMD graphic cards, ARM-based processors as well as Intel Xeon Phi coprocessors. For seismic wave propagation simulations using the open-source spectral-element code package SPECFEM3D_GLOBE, we incorporated an automatic source-to-source code generation tool (BOAST) which allows us to use meta-programming of all computational kernels for forward and adjoint runs. Using our BOAST kernels, we generate optimized source code for both CUDA and OpenCL languages within the source code package. Thus, seismic wave simulations are able now to fully utilize CUDA and OpenCL hardware accelerators. We show benchmarks of forward seismic wave propagation simulations using SPECFEM3D_GLOBE on CUDA/OpenCL GPUs, validating results and comparing performances for different simulations and hardware usages.

Experimental investigation on high performance RC column with manufactured sand and silica fume

NASA Astrophysics Data System (ADS)

Shanmuga Priya, T.

2017-11-01

In recent years, the use High Performance Concrete (HPC) has increased in construction industry. The ingredients of HPC depend on the availability and characteristics of suitable alternative materials. Those alternative materials are silica fume and manufactured sand, a by products from ferro silicon and quarry industries respectively. HPC made with silica fume as partial replacement of cement and manufactured sand as replacement of natural sand is considered as sustainable high performance concrete. In this present study the concrete was designed to get target strength of 60 MPa as per guide lines given by ACI 211- 4R (2008). The laboratory study was carried out experimentally to analyse the axial behavior of reinforced cement HPC column of size 100×100×1000mm and square in cross section. 10% of silica fume was preferred over ordinary portland cement. The natural sand was replaced by 0, 20, 40, 60, 80 and 100% with Manufactured Sand (M-Sand). In this investigation, totally 6 column specimens were cast for mixes M1 to M6 and were tested in 1000kN loading frame at 28 days. From this, Load-Mid height deflection curves were drawn and compared. Maximum ultimate load carrying capacity and the least deflection is obtained for the mix prepared by partial replacement of cement with 10% silica fume & natural sand by 100% M-Sand. The fine, amorphous and pozzalonic nature of silica fume and fine mineral particles in M- Sand increased the stiffness of HPC column. The test results revealed that HPC can be produced by using M-Sand with silica fume.

Forward and adjoint spectral-element simulations of seismic wave propagation using hardware accelerators

NASA Astrophysics Data System (ADS)

Peter, Daniel; Videau, Brice; Pouget, Kevin; Komatitsch, Dimitri

2015-04-01

Improving the resolution of tomographic images is crucial to answer important questions on the nature of Earth's subsurface structure and internal processes. Seismic tomography is the most prominent approach where seismic signals from ground-motion records are used to infer physical properties of internal structures such as compressional- and shear-wave speeds, anisotropy and attenuation. Recent advances in regional- and global-scale seismic inversions move towards full-waveform inversions which require accurate simulations of seismic wave propagation in complex 3D media, providing access to the full 3D seismic wavefields. However, these numerical simulations are computationally very expensive and need high-performance computing (HPC) facilities for further improving the current state of knowledge. During recent years, many-core architectures such as graphics processing units (GPUs) have been added to available large HPC systems. Such GPU-accelerated computing together with advances in multi-core central processing units (CPUs) can greatly accelerate scientific applications. There are mainly two possible choices of language support for GPU cards, the CUDA programming environment and OpenCL language standard. CUDA software development targets NVIDIA graphic cards while OpenCL was adopted mainly by AMD graphic cards. In order to employ such hardware accelerators for seismic wave propagation simulations, we incorporated a code generation tool BOAST into an existing spectral-element code package SPECFEM3D_GLOBE. This allows us to use meta-programming of computational kernels and generate optimized source code for both CUDA and OpenCL languages, running simulations on either CUDA or OpenCL hardware accelerators. We show here applications of forward and adjoint seismic wave propagation on CUDA/OpenCL GPUs, validating results and comparing performances for different simulations and hardware usages.

WinHPC System Configuration | High-Performance Computing | NREL

Science.gov Websites

CPUs with 48GB of memory. Node 04 has dual Intel Xeon E5530 CPUs with 24GB of memory. Nodes 05-20 have dual AMD Opteron 2374 HE CPUs with 16GB of memory. Nodes 21-30 have been decommissioned. Nodes 31-35 have dual Intel Xeon X5675 CPUs with 48GB of memory. Nodes 36-37 have dual Intel Xeon E5-2680 CPUs with

Institute for Sustained Performance, Energy, and Resilience (SuPER)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jagode, Heike; Bosilca, George; Danalis, Anthony

The University of Tennessee (UTK) and University of Texas at El Paso (UTEP) partnership supported the three main thrusts of the SUPER project---performance, energy, and resilience. The UTK-UTEP effort thus helped advance the main goal of SUPER, which was to ensure that DOE's computational scientists can successfully exploit the emerging generation of high performance computing (HPC) systems. This goal is being met by providing application scientists with strategies and tools to productively maximize performance, conserve energy, and attain resilience. The primary vehicle through which UTK provided performance measurement support to SUPER and the larger HPC community is the Performance Applicationmore » Programming Interface (PAPI). PAPI is an ongoing project that provides a consistent interface and methodology for collecting hardware performance information from various hardware and software components, including most major CPUs, GPUs and accelerators, interconnects, I/O systems, and power interfaces, as well as virtual cloud environments. The PAPI software is widely used for performance modeling of scientific and engineering applications---for example, the HOMME (High Order Methods Modeling Environment) climate code, and the GAMESS and NWChem computational chemistry codes---on DOE supercomputers. PAPI is widely deployed as middleware for use by higher-level profiling, tracing, and sampling tools (e.g., CrayPat, HPCToolkit, Scalasca, Score-P, TAU, Vampir, PerfExpert), making it the de facto standard for hardware counter analysis. PAPI has established itself as fundamental software infrastructure in every application domain (spanning academia, government, and industry), where improving performance can be mission critical. Ultimately, as more application scientists migrate their applications to HPC platforms, they will benefit from the extended capabilities this grant brought to PAPI to analyze and optimize performance in these environments, whether they use PAPI directly, or via third-party performance tools. Capabilities added to PAPI through this grant include support for new architectures such as the lastest GPU and Xeon Phi accelerators, and advanced power measurement and management features. Another important topic for the UTK team was providing support for a rich ecosystem of different fault management strategies in the context of parallel computing. Our long term efforts have been oriented toward proposing flexible strategies and providing building boxes that application developers can use to build the most efficient fault management technique for their application. These efforts span across the entire software spectrum, from theoretical models of existing strategies to easily assess their performance, to algorithmic modifications to take advantage of specific mathematical properties for data redundancy and to extensions to widely used programming paradigms to empower the application developers to deal with all types of faults. We have also continued our tight collaborations with users to help them adopt these technologies to ensure their application always deliver meaningful scientific data. Large supercomputer systems are becoming more and more power and energy constrained, and future systems and applications running on them will need to be optimized to run under power caps and/or minimize energy consumption. The UTEP team contributed to the SUPER energy thrust by developing power modeling methodologies and investigating power management strategies. Scalability modeling results showed that some applications can scale better with respect to an increasing power budget than with respect to only the number of processors. Power management, in particular shifting power to processors on the critical path of an application execution, can reduce perturbation due to system noise and other sources of runtime variability, which are growing problems on large-scale power-constrained computer systems.« less

Dynamic provisioning of local and remote compute resources with OpenStack

NASA Astrophysics Data System (ADS)

Giffels, M.; Hauth, T.; Polgart, F.; Quast, G.

2015-12-01

Modern high-energy physics experiments rely on the extensive usage of computing resources, both for the reconstruction of measured events as well as for Monte-Carlo simulation. The Institut fur Experimentelle Kernphysik (EKP) at KIT is participating in both the CMS and Belle experiments with computing and storage resources. In the upcoming years, these requirements are expected to increase due to growing amount of recorded data and the rise in complexity of the simulated events. It is therefore essential to increase the available computing capabilities by tapping into all resource pools. At the EKP institute, powerful desktop machines are available to users. Due to the multi-core nature of modern CPUs, vast amounts of CPU time are not utilized by common desktop usage patterns. Other important providers of compute capabilities are classical HPC data centers at universities or national research centers. Due to the shared nature of these installations, the standardized software stack required by HEP applications cannot be installed. A viable way to overcome this constraint and offer a standardized software environment in a transparent manner is the usage of virtualization technologies. The OpenStack project has become a widely adopted solution to virtualize hardware and offer additional services like storage and virtual machine management. This contribution will report on the incorporation of the institute's desktop machines into a private OpenStack Cloud. The additional compute resources provisioned via the virtual machines have been used for Monte-Carlo simulation and data analysis. Furthermore, a concept to integrate shared, remote HPC centers into regular HEP job workflows will be presented. In this approach, local and remote resources are merged to form a uniform, virtual compute cluster with a single point-of-entry for the user. Evaluations of the performance and stability of this setup and operational experiences will be discussed.

Influence of temperature and relative humidity conditions on the pan coating of hydroxypropyl cellulose molded capsules.

PubMed

Macchi, Elena; Zema, Lucia; Pandey, Preetanshu; Gazzaniga, Andrea; Felton, Linda A

2016-03-01

In a previous study, hydroxypropyl cellulose (HPC)-based capsular shells prepared by injection molding and intended for pulsatile release were successfully coated with 10mg/cm(2) Eudragit® L film. The suitability of HPC capsules for the development of a colon delivery platform based on a time dependent approach was demonstrated. In the present work, data logging devices (PyroButton®) were used to monitor the microenvironmental conditions, i.e. temperature (T) and relative humidity (RH), during coating processes performed under different spray rates (1.2, 2.5 and 5.5g/min). As HPC-based capsules present special features, a preliminary study was conducted on commercially available gelatin capsules for comparison purposes. By means of PyroButton data-loggers it was possible to acquire information about the impact of the effective T and RH conditions experienced by HPC substrates during the process on the technological properties and release performance of the coated systems. The use of increasing spray rates seemed to promote a tendency of the HPC shells to slightly swell at the beginning of the spraying process; moreover, capsules coated under spray rates of 1.2 and 2.5g/min showed the desired release performance, i.e. ability to withstand the acidic media followed by the pulsatile release expected for uncoated capsules. Preliminary stability studies seemed to show that coating conditions might also influence the release performance of the system upon storage. Copyright © 2015 Elsevier B.V. All rights reserved.

A review of transfusion practice before, during, and after hematopoietic progenitor cell transplantation

PubMed Central

Johnson, Viviana V.; Sandler, S. Gerald; Sayegh, Antoine; Klumpp, Thomas R.

2008-01-01

The increased use of hematopoietic progenitor cell (HPC) transplantation has implications and consequences for transfusion services: not only in hospitals where HPC transplantations are performed, but also in hospitals that do not perform HPC transplantations but manage patients before or after transplantation. Candidates for HPC transplantation have specific and specialized transfusion requirements before, during, and after transplantation that are necessary to avert the adverse consequences of alloimmunization to human leukocyte antigens, immunohematologic consequences of ABO-mismatched transplantations, or immunosuppression. Decisions concerning blood transfusions during any of these times may compromise the outcome of an otherwise successful transplantation. Years after an HPC transplantation, and even during clinical remission, recipients may continue to be immunosuppressed and may have critically important, special transfusion requirements. Without a thorough understanding of these special requirements, provision of compatible blood components may be delayed and often urgent transfusion needs prohibit appropriate consultation with the patient's transplantation specialist. To optimize the relevance of issues and communication between clinical hematologists, transplantation physicians, and transfusion medicine physicians, the data and opinions presented in this review are organized by sequence of patient presentation, namely, before, during, and after transplantation. PMID:18583566

FOSS GIS on the GFZ HPC cluster: Towards a service-oriented Scientific Geocomputation Environment

NASA Astrophysics Data System (ADS)

Loewe, P.; Klump, J.; Thaler, J.

2012-12-01

High performance compute clusters can be used as geocomputation workbenches. Their wealth of resources enables us to take on geocomputation tasks which exceed the limitations of smaller systems. These general capabilities can be harnessed via tools such as Geographic Information System (GIS), provided they are able to utilize the available cluster configuration/architecture and provide a sufficient degree of user friendliness to allow for wide application. While server-level computing is clearly not sufficient for the growing numbers of data- or computation-intense tasks undertaken, these tasks do not get even close to the requirements needed for access to "top shelf" national cluster facilities. So until recently such kind of geocomputation research was effectively barred due to lack access to of adequate resources. In this paper we report on the experiences gained by providing GRASS GIS as a software service on a HPC compute cluster at the German Research Centre for Geosciences using Platform Computing's Load Sharing Facility (LSF). GRASS GIS is the oldest and largest Free Open Source (FOSS) GIS project. During ramp up in 2011, multiple versions of GRASS GIS (v 6.4.2, 6.5 and 7.0) were installed on the HPC compute cluster, which currently consists of 234 nodes with 480 CPUs providing 3084 cores. Nineteen different processing queues with varying hardware capabilities and priorities are provided, allowing for fine-grained scheduling and load balancing. After successful initial testing, mechanisms were developed to deploy scripted geocomputation tasks onto dedicated processing queues. The mechanisms are based on earlier work by NETELER et al. (2008) and allow to use all 3084 cores for GRASS based geocomputation work. However, in practice applications are limited to fewer resources as assigned to their respective queue. Applications of the new GIS functionality comprise so far of hydrological analysis, remote sensing and the generation of maps of simulated tsunamis in the Mediterranean Sea for the Tsunami Atlas of the FP-7 TRIDEC Project (www.tridec-online.eu). This included the processing of complex problems, requiring significant amounts of processing time up to full 20 CPU days. This GRASS GIS-based service is provided as a research utility in the sense of "Software as a Service" (SaaS) and is a first step towards a GFZ corporate cloud service.

HPC on Competitive Cloud Resources

NASA Astrophysics Data System (ADS)

Bientinesi, Paolo; Iakymchuk, Roman; Napper, Jeff

Computing as a utility has reached the mainstream. Scientists can now easily rent time on large commercial clusters that can be expanded and reduced on-demand in real-time. However, current commercial cloud computing performance falls short of systems specifically designed for scientific applications. Scientific computing needs are quite different from those of the web applications that have been the focus of cloud computing vendors. In this chapter we demonstrate through empirical evaluation the computational efficiency of high-performance numerical applications in a commercial cloud environment when resources are shared under high contention. Using the Linpack benchmark as a case study, we show that cache utilization becomes highly unpredictable and similarly affects computation time. For some problems, not only is it more efficient to underutilize resources, but the solution can be reached sooner in realtime (wall-time). We also show that the smallest, cheapest (64-bit) instance on the studied environment is the best for price to performance ration. In light of the high-contention we witness, we believe that alternative definitions of efficiency for commercial cloud environments should be introduced where strong performance guarantees do not exist. Concepts like average, expected performance and execution time, expected cost to completion, and variance measures--traditionally ignored in the high-performance computing context--now should complement or even substitute the standard definitions of efficiency.

Combined Performance of Polypropylene Fibre and Weld Slag in High Performance Concrete

NASA Astrophysics Data System (ADS)

Ananthi, A.; Karthikeyan, J.

2017-12-01

The effect of polypropylene fibre and weld slag on the mechanical properties of High Performance Concrete (HPC) containing silica fume as the mineral admixtures was experimentally verified in this study. Sixteen series of HPC mixtures(70 MPa) were designed with varying fibre fractions and Weld Slag (WS). Fibre added at different proportion (0, 0.1, 0.3 and 0.6%) to the weight of cement. Weld slag was substituted to the fine aggregate (0, 10, 20 and 30%) at volume. The addition of fibre decreases the slump at 5, 9 and 14%, whereas the substitution of weld slag decreases by about 3, 11 and 21% with respect to the control mixture. Mechanical properties like compressive strength, split tensile strength, flexural strength, Ultrasonic Pulse Velocity test (UPV) and bond strength were tested. Durability studies such as Water absorption and Sorptivity test were conducted to check the absorption of water in HPC. Weld slag of 10% and fibre dosage of 0.3% in HPC, attains the maximum strength and hence this combination is most favourable for the structural applications.

Mantle Convection on Modern Supercomputers

NASA Astrophysics Data System (ADS)

Weismüller, J.; Gmeiner, B.; Huber, M.; John, L.; Mohr, M.; Rüde, U.; Wohlmuth, B.; Bunge, H. P.

2015-12-01

Mantle convection is the cause for plate tectonics, the formation of mountains and oceans, and the main driving mechanism behind earthquakes. The convection process is modeled by a system of partial differential equations describing the conservation of mass, momentum and energy. Characteristic to mantle flow is the vast disparity of length scales from global to microscopic, turning mantle convection simulations into a challenging application for high-performance computing. As system size and technical complexity of the simulations continue to increase, design and implementation of simulation models for next generation large-scale architectures is handled successfully only in an interdisciplinary context. A new priority program - named SPPEXA - by the German Research Foundation (DFG) addresses this issue, and brings together computer scientists, mathematicians and application scientists around grand challenges in HPC. Here we report from the TERRA-NEO project, which is part of the high visibility SPPEXA program, and a joint effort of four research groups. TERRA-NEO develops algorithms for future HPC infrastructures, focusing on high computational efficiency and resilience in next generation mantle convection models. We present software that can resolve the Earth's mantle with up to 1012 grid points and scales efficiently to massively parallel hardware with more than 50,000 processors. We use our simulations to explore the dynamic regime of mantle convection and assess the impact of small scale processes on global mantle flow.

The Livermore Brain: Massive Deep Learning Networks Enabled by High Performance Computing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Barry Y.

The proliferation of inexpensive sensor technologies like the ubiquitous digital image sensors has resulted in the collection and sharing of vast amounts of unsorted and unexploited raw data. Companies and governments who are able to collect and make sense of large datasets to help them make better decisions more rapidly will have a competitive advantage in the information era. Machine Learning technologies play a critical role for automating the data understanding process; however, to be maximally effective, useful intermediate representations of the data are required. These representations or “features” are transformations of the raw data into a form where patternsmore » are more easily recognized. Recent breakthroughs in Deep Learning have made it possible to learn these features from large amounts of labeled data. The focus of this project is to develop and extend Deep Learning algorithms for learning features from vast amounts of unlabeled data and to develop the HPC neural network training platform to support the training of massive network models. This LDRD project succeeded in developing new unsupervised feature learning algorithms for images and video and created a scalable neural network training toolkit for HPC. Additionally, this LDRD helped create the world’s largest freely-available image and video dataset supporting open multimedia research and used this dataset for training our deep neural networks. This research helped LLNL capture several work-for-others (WFO) projects, attract new talent, and establish collaborations with leading academic and commercial partners. Finally, this project demonstrated the successful training of the largest unsupervised image neural network using HPC resources and helped establish LLNL leadership at the intersection of Machine Learning and HPC research.« less

Mini-Ckpts: Surviving OS Failures in Persistent Memory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fiala, David; Mueller, Frank; Ferreira, Kurt Brian

Concern is growing in the high-performance computing (HPC) community on the reliability of future extreme-scale systems. Current efforts have focused on application fault-tolerance rather than the operating system (OS), despite the fact that recent studies have suggested that failures in OS memory are more likely. The OS is critical to a system's correct and efficient operation of the node and processes it governs -- and in HPC also for any other nodes a parallelized application runs on and communicates with: Any single node failure generally forces all processes of this application to terminate due to tight communication in HPC. Therefore,more » the OS itself must be capable of tolerating failures. In this work, we introduce mini-ckpts, a framework which enables application survival despite the occurrence of a fatal OS failure or crash. Mini-ckpts achieves this tolerance by ensuring that the critical data describing a process is preserved in persistent memory prior to the failure. Following the failure, the OS is rejuvenated via a warm reboot and the application continues execution effectively making the failure and restart transparent. The mini-ckpts rejuvenation and recovery process is measured to take between three to six seconds and has a failure-free overhead of between 3-5% for a number of key HPC workloads. In contrast to current fault-tolerance methods, this work ensures that the operating and runtime system can continue in the presence of faults. This is a much finer-grained and dynamic method of fault-tolerance than the current, coarse-grained, application-centric methods. Handling faults at this level has the potential to greatly reduce overheads and enables mitigation of additional fault scenarios.« less

Computing for Finance

ScienceCinema

None

2018-01-24

The finance sector is one of the driving forces for the use of distributed or Grid computing for business purposes. The speakers will review the state-of-the-art of high performance computing in the financial sector, and provide insight into how different types of Grid computing – from local clusters to global networks - are being applied to financial applications. They will also describe the use of software and techniques from physics, such as Monte Carlo simulations, in the financial world. There will be four talks of 20min each. The talk abstracts and speaker bios are listed below. This will be followed by a Q&A; panel session with the speakers. From 19:00 onwards there will be a networking cocktail for audience and speakers. This is an EGEE / CERN openlab event organized in collaboration with the regional business network rezonance.ch. A webcast of the event will be made available for subsequent viewing, along with powerpoint material presented by the speakers. Attendance is free and open to all. Registration is mandatory via www.rezonance.ch, including for CERN staff. 1. Overview of High Performance Computing in the Financial Industry Michael Yoo, Managing Director, Head of the Technical Council, UBS Presentation will describe the key business challenges driving the need for HPC solutions, describe the means in which those challenges are being addressed within UBS (such as GRID) as well as the limitations of some of these solutions, and assess some of the newer HPC technologies which may also play a role in the Financial Industry in the future. Speaker Bio: Michael originally joined the former Swiss Bank Corporation in 1994 in New York as a developer on a large data warehouse project. In 1996 he left SBC and took a role with Fidelity Investments in Boston. Unable to stay away for long, he returned to SBC in 1997 while working for Perot Systems in Singapore. Finally, in 1998 he formally returned to UBS in Stamford following the merger with SBC and has remained with UBS for the past 9 years. During his tenure at UBS, he has had a number of leadership roles within IT in development, support and architecture. In 2006 Michael relocated to Switzerland to take up his current role as head of the UBS IB Technical Council, responsible for the overall technology strategy and vision of the Investment Bank. One of Michael's key responsibilities is to manage the UBS High Performance Computing Research Lab and he has been involved in a number of initiatives in the HPC space. 2. Grid in the Commercial WorldFred Gedling, Chief Technology Officer EMEA and Senior Vice President Global Services, DataSynapse Grid computing gets mentions in the press for community programs starting last decade with Seti@Home. Government, national and supranational initiatives in grid receive some press. One of the IT-industries' best-kept secrets is the use of grid computing by commercial organizations with spectacular results. Grid Computing and its evolution into Application Virtualization is discussed and how this is key to the next generation data center. Speaker Bio: Fred Gedling holds the joint roles of Chief Technology Officer for EMEA and Senior Vice President of Global Services at DataSynapse, a global provider of application virtualisation software. Based in London and working closely with organisations seeking to optimise their IT infrastructures, Fred offers unique insights into the technology of virtualisation as well as the methodology of establishing ROI and rapid deployment to the immediate advantage of the business. Fred has more than fifteen years experience of enterprise middleware and high-performance infrastructures. Prior to DataSynapse he worked in high performance CRM middleware and was the CTO EMEA for New Era of Networks (NEON) during the rapid growth of Enterprise Application Integration. His 25-year career in technology also includes management positions at Goldman Sachs and Stratus Computer. Fred holds a First Class Bsc (Hons) degree in Physics with Astrophysics from the University of Leeds and had the privilege of being a summer student at CERN.3. Opportunities for gLite in finance and related industriesAdam Vile, Head of Grid, HPC and Technical Computing, Excelian Ltd.gLite, the Grid software developed by the EGEE project, has been exceedingly successful as an enabling infrastructure, and has been a massive success in bringing together scientific and technical communities to provide the compute power to address previously incomputable problems. Not so in the finance industry. In its current form gLite would be a business disabler. There are other middleware tools that solve the finance communities compute problems much better. Things are moving on, however. There are moves afoot in the open source community to evolve the technology to address other, more sophisticated needs such as utility and interactive computing. In this talk, I will describe how Excelian is providing Grid consultancy services for the finance community and how, through its relationship to the EGEE project, Excelian is helping to identify and exploit opportunities as the research and business worlds converge. Because of the strong third party presence in the finance industry, such opportunities are few and far between, but they are there, especially as we expand sideways into related verticals such as the smaller hedge funds and energy companies. This talk will give an overview of the barriers to adoption of gLite in the finance industry and highlight some of the opportunities offered in this and related industries as the ideas around Grid mature. Speaker Bio: Dr Adam Vile is a senior consultant and head of the Grid and HPC practice at Excelian, a consultancy that focuses on financial markets professional services. He has spent many years in investment banking, as a developer, project manager and architect in both front and back office. Before joining Excelian he was senior Grid and HPC architect at Barclays Capital. Prior to joining investment banking, Adam spent a number of years lecturing in IT and mathematics at a UK University and maintains links with academia through lectures, research and through validation and steering of postgraduate courses. He is a chartered mathematician and was the conference chair of the Institute of Mathematics and its Applications first conference in computational Finance. 4. From Monte Carlo to Wall Street Daniel Egloff, Head of Financial Engineering Computing Unit, Zürich Cantonal Bank High performance computing techniques provide new means to solve computationally hard problems in the financial service industry. First I consider Monte Carlo simulation and illustrate how it can be used to implement a sophisticated credit risk management and economic capital framework. From a HPC perspective, basic Monte Carlo simulation is embarrassingly parallel and can be implemented efficiently on distributed memory clusters. Additional difficulties arise for adaptive variance reduction schemes, if the information content in a sample is very small, and if the amount of simulated date becomes huge such that incremental processing algorithms are indispensable. We discuss the business value of an advanced credit risk quantification which is particularly compelling in these days. While Monte Carlo simulation is a very versatile tool it is not always the preferred solution for the pricing of complex products like multi asset options, structured products, or credit derivatives. As a second application I show how operator methods can be used to develop a pricing framework. The scalability of operator methods relies heavily on optimized dense matrix-matrix multiplications and requires specialized BLAS level-3 implementations provided by specialized FPGA or GPU boards. Speaker Bio: Daniel Egloff studied mathematics, theoretical physics, and computer science at the University of Zurich and the ETH Zurich. He holds a PhD in Mathematics from University of Fribourg, Switzerland. After his PhD he started to work for a large Swiss insurance company in the area of asset and liability management. He continued his professional career in the consulting industry. At KPMG and Arthur Andersen he consulted international clients and implemented quantitative risk management solutions for financial institutions and insurance companies. In 2002 he joined Zurich Cantonal Bank. He was assigned to develop and implement credit portfolio risk and economic capital methodologies. He built up a competence center for high performance and cluster computing. Currently, Daniel Egloff is heading the Financial Computing unit in the ZKB Financial Engineering division. He and his team is engineering and operating high performance cluster applications for computationally intensive problems in financial risk management.

Computing for Finance

ScienceCinema

None

2018-06-20

The finance sector is one of the driving forces for the use of distributed or Grid computing for business purposes. The speakers will review the state-of-the-art of high performance computing in the financial sector, and provide insight into how different types of Grid computing – from local clusters to global networks - are being applied to financial applications. They will also describe the use of software and techniques from physics, such as Monte Carlo simulations, in the financial world. There will be four talks of 20min each. The talk abstracts and speaker bios are listed below. This will be followed by a Q&A; panel session with the speakers. From 19:00 onwards there will be a networking cocktail for audience and speakers. This is an EGEE / CERN openlab event organized in collaboration with the regional business network rezonance.ch. A webcast of the event will be made available for subsequent viewing, along with powerpoint material presented by the speakers. Attendance is free and open to all. Registration is mandatory via www.rezonance.ch, including for CERN staff. 1. Overview of High Performance Computing in the Financial Industry. Michael Yoo, Managing Director, Head of the Technical Council, UBS. Presentation will describe the key business challenges driving the need for HPC solutions, describe the means in which those challenges are being addressed within UBS (such as GRID) as well as the limitations of some of these solutions, and assess some of the newer HPC technologies which may also play a role in the Financial Industry in the future. Speaker Bio: Michael originally joined the former Swiss Bank Corporation in 1994 in New York as a developer on a large data warehouse project. In 1996 he left SBC and took a role with Fidelity Investments in Boston. Unable to stay away for long, he returned to SBC in 1997 while working for Perot Systems in Singapore. Finally, in 1998 he formally returned to UBS in Stamford following the merger with SBC and has remained with UBS for the past 9 years. During his tenure at UBS, he has had a number of leadership roles within IT in development, support and architecture. In 2006 Michael relocated to Switzerland to take up his current role as head of the UBS IB Technical Council, responsible for the overall technology strategy and vision of the Investment Bank. One of Michael's key responsibilities is to manage the UBS High Performance Computing Research Lab and he has been involved in a number of initiatives in the HPC space. 2. Grid in the Commercial WorldFred Gedling, Chief Technology Officer EMEA and Senior Vice President Global Services, DataSynapse. Grid computing gets mentions in the press for community programs starting last decade with "Seti@Home". Government, national and supranational initiatives in grid receive some press. One of the IT-industries' best-kept secrets is the use of grid computing by commercial organizations with spectacular results. Grid Computing and its evolution into Application Virtualization is discussed and how this is key to the next generation data center. Speaker Bio: Fred Gedling holds the joint roles of Chief Technology Officer for EMEA and Senior Vice President of Global Services at DataSynapse, a global provider of application virtualisation software. Based in London and working closely with organisations seeking to optimise their IT infrastructures, Fred offers unique insights into the technology of virtualisation as well as the methodology of establishing ROI and rapid deployment to the immediate advantage of the business. Fred has more than fifteen years experience of enterprise middleware and high-performance infrastructures. Prior to DataSynapse he worked in high performance CRM middleware and was the CTO EMEA for New Era of Networks (NEON) during the rapid growth of Enterprise Application Integration. His 25-year career in technology also includes management positions at Goldman Sachs and Stratus Computer. Fred holds a First Class Bsc (Hons) degree in Physics with Astrophysics from the University of Leeds and had the privilege of being a summer student at CERN.3. Opportunities for gLite in finance and related industriesAdam Vile, Head of Grid, HPC and Technical Computing, Excelian Ltd.gLite, the Grid software developed by the EGEE project, has been exceedingly successful as an enabling infrastructure, and has been a massive success in bringing together scientific and technical communities to provide the compute power to address previously incomputable problems. Not so in the finance industry. In its current form gLite would be a business disabler. There are other middleware tools that solve the finance communities compute problems much better. Things are moving on, however. There are moves afoot in the open source community to evolve the technology to address other, more sophisticated needs such as utility and interactive computing. In this talk, I will describe how Excelian is providing Grid consultancy services for the finance community and how, through its relationship to the EGEE project, Excelian is helping to identify and exploit opportunities as the research and business worlds converge. Because of the strong third party presence in the finance industry, such opportunities are few and far between, but they are there, especially as we expand sideways into related verticals such as the smaller hedge funds and energy companies. This talk will give an overview of the barriers to adoption of gLite in the finance industry and highlight some of the opportunities offered in this and related industries as the ideas around Grid mature. Speaker Bio: Dr Adam Vile is a senior consultant and head of the Grid and HPC practice at Excelian, a consultancy that focuses on financial markets professional services. He has spent many years in investment banking, as a developer, project manager and architect in both front and back office. Before joining Excelian he was senior Grid and HPC architect at Barclays Capital. Prior to joining investment banking, Adam spent a number of years lecturing in IT and mathematics at a UK University and maintains links with academia through lectures, research and through validation and steering of postgraduate courses. He is a chartered mathematician and was the conference chair of the Institute of Mathematics and its Applications first conference in computational Finance.4. From Monte Carlo to Wall Street Daniel Egloff, Head of Financial Engineering Computing Unit, Zürich Cantonal Bank High performance computing techniques provide new means to solve computationally hard problems in the financial service industry. First I consider Monte Carlo simulation and illustrate how it can be used to implement a sophisticated credit risk management and economic capital framework. From a HPC perspective, basic Monte Carlo simulation is embarrassingly parallel and can be implemented efficiently on distributed memory clusters. Additional difficulties arise for adaptive variance reduction schemes, if the information content in a sample is very small, and if the amount of simulated date becomes huge such that incremental processing algorithms are indispensable. We discuss the business value of an advanced credit risk quantification which is particularly compelling in these days. While Monte Carlo simulation is a very versatile tool it is not always the preferred solution for the pricing of complex products like multi asset options, structured products, or credit derivatives. As a second application I show how operator methods can be used to develop a pricing framework. The scalability of operator methods relies heavily on optimized dense matrix-matrix multiplications and requires specialized BLAS level-3 implementations provided by specialized FPGA or GPU boards. Speaker Bio: Daniel Egloff studied mathematics, theoretical physics, and computer science at the University of Zurich and the ETH Zurich. He holds a PhD in Mathematics from University of Fribourg, Switzerland. After his PhD he started to work for a large Swiss insurance company in the area of asset and liability management. He continued his professional career in the consulting industry. At KPMG and Arthur Andersen he consulted international clients and implemented quantitative risk management solutions for financial institutions and insurance companies. In 2002 he joined Zurich Cantonal Bank. He was assigned to develop and implement credit portfolio risk and economic capital methodologies. He built up a competence center for high performance and cluster computing. Currently, Daniel Egloff is heading the Financial Computing unit in the ZKB Financial Engineering division. He and his team is engineering and operating high performance cluster applications for computationally intensive problems in financial risk management.

Computing for Finance

ScienceCinema

None

2018-01-25

The finance sector is one of the driving forces for the use of distributed or Grid computing for business purposes. The speakers will review the state-of-the-art of high performance computing in the financial sector, and provide insight into how different types of Grid computing – from local clusters to global networks - are being applied to financial applications. They will also describe the use of software and techniques from physics, such as Monte Carlo simulations, in the financial world. There will be four talks of 20min each. The talk abstracts and speaker bios are listed below. This will be followed by a Q&A; panel session with the speakers. From 19:00 onwards there will be a networking cocktail for audience and speakers. This is an EGEE / CERN openlab event organized in collaboration with the regional business network rezonance.ch. A webcast of the event will be made available for subsequent viewing, along with powerpoint material presented by the speakers. Attendance is free and open to all. Registration is mandatory via www.rezonance.ch, including for CERN staff. 1. Overview of High Performance Computing in the Financial Industry Michael Yoo, Managing Director, Head of the Technical Council, UBS Presentation will describe the key business challenges driving the need for HPC solutions, describe the means in which those challenges are being addressed within UBS (such as GRID) as well as the limitations of some of these solutions, and assess some of the newer HPC technologies which may also play a role in the Financial Industry in the future. Speaker Bio: Michael originally joined the former Swiss Bank Corporation in 1994 in New York as a developer on a large data warehouse project. In 1996 he left SBC and took a role with Fidelity Investments in Boston. Unable to stay away for long, he returned to SBC in 1997 while working for Perot Systems in Singapore. Finally, in 1998 he formally returned to UBS in Stamford following the merger with SBC and has remained with UBS for the past 9 years. During his tenure at UBS, he has had a number of leadership roles within IT in development, support and architecture. In 2006 Michael relocated to Switzerland to take up his current role as head of the UBS IB Technical Council, responsible for the overall technology strategy and vision of the Investment Bank. One of Michael's key responsibilities is to manage the UBS High Performance Computing Research Lab and he has been involved in a number of initiatives in the HPC space. 2. Grid in the Commercial WorldFred Gedling, Chief Technology Officer EMEA and Senior Vice President Global Services, DataSynapse Grid computing gets mentions in the press for community programs starting last decade with Government, national and supranational initiatives in grid receive some press. One of the IT-industries' best-kept secrets is the use of grid computing by commercial organizations with spectacular results. Grid Computing and its evolution into Application Virtualization is discussed and how this is key to the next generation data center. Speaker Bio: Fred Gedling holds the joint roles of Chief Technology Officer for EMEA and Senior Vice President of Global Services at DataSynapse, a global provider of application virtualisation software. Based in London and working closely with organisations seeking to optimise their IT infrastructures, Fred offers unique insights into the technology of virtualisation as well as the methodology of establishing ROI and rapid deployment to the immediate advantage of the business. Fred has more than fifteen years experience of enterprise middleware and high-performance infrastructures. Prior to DataSynapse he worked in high performance CRM middleware and was the CTO EMEA for New Era of Networks (NEON) during the rapid growth of Enterprise Application Integration. His 25-year career in technology also includes management positions at Goldman Sachs and Stratus Computer. Fred holds a First Class Bsc (Hons) degree in Physics with Astrophysics from the University of Leeds and had the privilege of being a summer student at CERN.3. Opportunities for gLite in finance and related industries Adam Vile, Head of Grid, HPC and Technical Computing, Excelian Ltd.gLite, the Grid software developed by the EGEE project, has been exceedingly successful as an enabling infrastructure, and has been a massive success in bringing together scientific and technical communities to provide the compute power to address previously incomputable problems. Not so in the finance industry. In its current form gLite would be a business disabler. There are other middleware tools that solve the finance communities compute problems much better. Things are moving on, however. There are moves afoot in the open source community to evolve the technology to address other, more sophisticated needs such as utility and interactive computing. In this talk, I will describe how Excelian is providing Grid consultancy services for the finance community and how, through its relationship to the EGEE project, Excelian is helping to identify and exploit opportunities as the research and business worlds converge. Because of the strong third party presence in the finance industry, such opportunities are few and far between, but they are there, especially as we expand sideways into related verticals such as the smaller hedge funds and energy companies. This talk will give an overview of the barriers to adoption of gLite in the finance industry and highlight some of the opportunities offered in this and related industries as the ideas around Grid mature. Speaker Bio: Dr Adam Vile is a senior consultant and head of the Grid and HPC practice at Excelian, a consultancy that focuses on financial markets professional services. He has spent many years in investment banking, as a developer, project manager and architect in both front and back office. Before joining Excelian he was senior Grid and HPC architect at Barclays Capital. Prior to joining investment banking, Adam spent a number of years lecturing in IT and mathematics at a UK University and maintains links with academia through lectures, research and through validation and steering of postgraduate courses. He is a chartered mathematician and was the conference chair of the Institute of Mathematics and its Applications first conference in computational Finance.4. From Monte Carlo to Wall Street Daniel Egloff, Head of Financial Engineering Computing Unit, Zürich Cantonal Bank High performance computing techniques provide new means to solve computationally hard problems in the financial service industry. First I consider Monte Carlo simulation and illustrate how it can be used to implement a sophisticated credit risk management and economic capital framework. From a HPC perspective, basic Monte Carlo simulation is embarrassingly parallel and can be implemented efficiently on distributed memory clusters. Additional difficulties arise for adaptive variance reduction schemes, if the information content in a sample is very small, and if the amount of simulated date becomes huge such that incremental processing algorithms are indispensable. We discuss the business value of an advanced credit risk quantification which is particularly compelling in these days. While Monte Carlo simulation is a very versatile tool it is not always the preferred solution for the pricing of complex products like multi asset options, structured products, or credit derivatives. As a second application I show how operator methods can be used to develop a pricing framework. The scalability of operator methods relies heavily on optimized dense matrix-matrix multiplications and requires specialized BLAS level-3 implementations provided by specialized FPGA or GPU boards. Speaker Bio: Daniel Egloff studied mathematics, theoretical physics, and computer science at the University of Zurich and the ETH Zurich. He holds a PhD in Mathematics from University of Fribourg, Switzerland. After his PhD he started to work for a large Swiss insurance company in the area of asset and liability management. He continued his professional career in the consulting industry. At KPMG and Arthur Andersen he consulted international clients and implemented quantitative risk management solutions for financial institutions and insurance companies. In 2002 he joined Zurich Cantonal Bank. He was assigned to develop and implement credit portfolio risk and economic capital methodologies. He built up a competence center for high performance and cluster computing. Currently, Daniel Egloff is heading the Financial Computing unit in the ZKB Financial Engineering division. He and his team is engineering and operating high performance cluster applications for computationally intensive problems in financial risk management.

Computing for Finance

ScienceCinema

None

2018-02-02

The finance sector is one of the driving forces for the use of distributed or Grid computing for business purposes. The speakers will review the state-of-the-art of high performance computing in the financial sector, and provide insight into how different types of Grid computing – from local clusters to global networks - are being applied to financial applications. They will also describe the use of software and techniques from physics, such as Monte Carlo simulations, in the financial world. There will be four talks of 20min each. The talk abstracts and speaker bios are listed below. This will be followed by a Q&A; panel session with the speakers. From 19:00 onwards there will be a networking cocktail for audience and speakers. This is an EGEE / CERN openlab event organized in collaboration with the regional business network rezonance.ch. A webcast of the event will be made available for subsequent viewing, along with powerpoint material presented by the speakers. Attendance is free and open to all. Registration is mandatory via www.rezonance.ch, including for CERN staff. 1. Overview of High Performance Computing in the Financial Industry Michael Yoo, Managing Director, Head of the Technical Council, UBS Presentation will describe the key business challenges driving the need for HPC solutions, describe the means in which those challenges are being addressed within UBS (such as GRID) as well as the limitations of some of these solutions, and assess some of the newer HPC technologies which may also play a role in the Financial Industry in the future. Speaker Bio: Michael originally joined the former Swiss Bank Corporation in 1994 in New York as a developer on a large data warehouse project. In 1996 he left SBC and took a role with Fidelity Investments in Boston. Unable to stay away for long, he returned to SBC in 1997 while working for Perot Systems in Singapore. Finally, in 1998 he formally returned to UBS in Stamford following the merger with SBC and has remained with UBS for the past 9 years. During his tenure at UBS, he has had a number of leadership roles within IT in development, support and architecture. In 2006 Michael relocated to Switzerland to take up his current role as head of the UBS IB Technical Council, responsible for the overall technology strategy and vision of the Investment Bank. One of Michael's key responsibilities is to manage the UBS High Performance Computing Research Lab and he has been involved in a number of initiatives in the HPC space. 2. Grid in the Commercial WorldFred Gedling, Chief Technology Officer EMEA and Senior Vice President Global Services, DataSynapse Grid computing gets mentions in the press for community programs starting last decade with Government, national and supranational initiatives in grid receive some press. One of the IT-industries' best-kept secrets is the use of grid computing by commercial organizations with spectacular results. Grid Computing and its evolution into Application Virtualization is discussed and how this is key to the next generation data center. Speaker Bio: Fred Gedling holds the joint roles of Chief Technology Officer for EMEA and Senior Vice President of Global Services at DataSynapse, a global provider of application virtualisation software. Based in London and working closely with organisations seeking to optimise their IT infrastructures, Fred offers unique insights into the technology of virtualisation as well as the methodology of establishing ROI and rapid deployment to the immediate advantage of the business. Fred has more than fifteen years experience of enterprise middleware and high-performance infrastructures. Prior to DataSynapse he worked in high performance CRM middleware and was the CTO EMEA for New Era of Networks (NEON) during the rapid growth of Enterprise Application Integration. His 25-year career in technology also includes management positions at Goldman Sachs and Stratus Computer. Fred holds a First Class Bsc (Hons) degree in Physics with Astrophysics from the University of Leeds and had the privilege of being a summer student at CERN.3. Opportunities for gLite in finance and related industriesAdam Vile, Head of Grid, HPC and Technical Computing, Excelian Ltd.gLite, the Grid software developed by the EGEE project, has been exceedingly successful as an enabling infrastructure, and has been a massive success in bringing together scientific and technical communities to provide the compute power to address previously incomputable problems. Not so in the finance industry. In its current form gLite would be a business disabler. There are other middleware tools that solve the finance communities compute problems much better. Things are moving on, however. There are moves afoot in the open source community to evolve the technology to address other, more sophisticated needs such as utility and interactive computing. In this talk, I will describe how Excelian is providing Grid consultancy services for the finance community and how, through its relationship to the EGEE project, Excelian is helping to identify and exploit opportunities as the research and business worlds converge. Because of the strong third party presence in the finance industry, such opportunities are few and far between, but they are there, especially as we expand sideways into related verticals such as the smaller hedge funds and energy companies. This talk will give an overview of the barriers to adoption of gLite in the finance industry and highlight some of the opportunities offered in this and related industries as the ideas around Grid mature. Speaker Bio: Dr Adam Vile is a senior consultant and head of the Grid and HPC practice at Excelian, a consultancy that focuses on financial markets professional services. He has spent many years in investment banking, as a developer, project manager and architect in both front and back office. Before joining Excelian he was senior Grid and HPC architect at Barclays Capital. Prior to joining investment banking, Adam spent a number of years lecturing in IT and mathematics at a UK University and maintains links with academia through lectures, research and through validation and steering of postgraduate courses. He is a chartered mathematician and was the conference chair of the Institute of Mathematics and its Applications first conference in computational Finance. 4. From Monte Carlo to Wall Street Daniel Egloff, Head of Financial Engineering Computing Unit, Zürich Cantonal Bank High performance computing techniques provide new means to solve computationally hard problems in the financial service industry. First I consider Monte Carlo simulation and illustrate how it can be used to implement a sophisticated credit risk management and economic capital framework. From a HPC perspective, basic Monte Carlo simulation is embarrassingly parallel and can be implemented efficiently on distributed memory clusters. Additional difficulties arise for adaptive variance reduction schemes, if the information content in a sample is very small, and if the amount of simulated date becomes huge such that incremental processing algorithms are indispensable. We discuss the business value of an advanced credit risk quantification which is particularly compelling in these days. While Monte Carlo simulation is a very versatile tool it is not always the preferred solution for the pricing of complex products like multi asset options, structured products, or credit derivatives. As a second application I show how operator methods can be used to develop a pricing framework. The scalability of operator methods relies heavily on optimized dense matrix-matrix multiplications and requires specialized BLAS level-3 implementations provided by specialized FPGA or GPU boards. Speaker Bio: Daniel Egloff studied mathematics, theoretical physics, and computer science at the University of Zurich and the ETH Zurich. He holds a PhD in Mathematics from University of Fribourg, Switzerland. After his PhD he started to work for a large Swiss insurance company in the area of asset and liability management. He continued his professional career in the consulting industry. At KPMG and Arthur Andersen he consulted international clients and implemented quantitative risk management solutions for financial institutions and insurance companies. In 2002 he joined Zurich Cantonal Bank. He was assigned to develop and implement credit portfolio risk and economic capital methodologies. He built up a competence center for high performance and cluster computing. Currently, Daniel Egloff is heading the Financial Computing unit in the ZKB Financial Engineering division. He and his team is engineering and operating high performance cluster applications for computationally intensive problems in financial risk management.

Computing for Finance

DOE Office of Scientific and Technical Information (OSTI.GOV)

None

The finance sector is one of the driving forces for the use of distributed or Grid computing for business purposes. The speakers will review the state-of-the-art of high performance computing in the financial sector, and provide insight into how different types of Grid computing – from local clusters to global networks - are being applied to financial applications. They will also describe the use of software and techniques from physics, such as Monte Carlo simulations, in the financial world. There will be four talks of 20min each. The talk abstracts and speaker bios are listed below. This will be followedmore » by a Q&A; panel session with the speakers. From 19:00 onwards there will be a networking cocktail for audience and speakers. This is an EGEE / CERN openlab event organized in collaboration with the regional business network rezonance.ch. A webcast of the event will be made available for subsequent viewing, along with powerpoint material presented by the speakers. Attendance is free and open to all. Registration is mandatory via www.rezonance.ch, including for CERN staff. 1. Overview of High Performance Computing in the Financial Industry Michael Yoo, Managing Director, Head of the Technical Council, UBS Presentation will describe the key business challenges driving the need for HPC solutions, describe the means in which those challenges are being addressed within UBS (such as GRID) as well as the limitations of some of these solutions, and assess some of the newer HPC technologies which may also play a role in the Financial Industry in the future. Speaker Bio: Michael originally joined the former Swiss Bank Corporation in 1994 in New York as a developer on a large data warehouse project. In 1996 he left SBC and took a role with Fidelity Investments in Boston. Unable to stay away for long, he returned to SBC in 1997 while working for Perot Systems in Singapore. Finally, in 1998 he formally returned to UBS in Stamford following the merger with SBC and has remained with UBS for the past 9 years. During his tenure at UBS, he has had a number of leadership roles within IT in development, support and architecture. In 2006 Michael relocated to Switzerland to take up his current role as head of the UBS IB Technical Council, responsible for the overall technology strategy and vision of the Investment Bank. One of Michael's key responsibilities is to manage the UBS High Performance Computing Research Lab and he has been involved in a number of initiatives in the HPC space. 2. Grid in the Commercial WorldFred Gedling, Chief Technology Officer EMEA and Senior Vice President Global Services, DataSynapse Grid computing gets mentions in the press for community programs starting last decade with Seti@Home. Government, national and supranational initiatives in grid receive some press. One of the IT-industries' best-kept secrets is the use of grid computing by commercial organizations with spectacular results. Grid Computing and its evolution into Application Virtualization is discussed and how this is key to the next generation data center. Speaker Bio: Fred Gedling holds the joint roles of Chief Technology Officer for EMEA and Senior Vice President of Global Services at DataSynapse, a global provider of application virtualisation software. Based in London and working closely with organisations seeking to optimise their IT infrastructures, Fred offers unique insights into the technology of virtualisation as well as the methodology of establishing ROI and rapid deployment to the immediate advantage of the business. Fred has more than fifteen years experience of enterprise middleware and high-performance infrastructures. Prior to DataSynapse he worked in high performance CRM middleware and was the CTO EMEA for New Era of Networks (NEON) during the rapid growth of Enterprise Application Integration. His 25-year career in technology also includes management positions at Goldman Sachs and Stratus Computer. Fred holds a First Class Bsc (Hons) degree in Physics with Astrophysics from the University of Leeds and had the privilege of being a summer student at CERN.3. Opportunities for gLite in finance and related industriesAdam Vile, Head of Grid, HPC and Technical Computing, Excelian Ltd.gLite, the Grid software developed by the EGEE project, has been exceedingly successful as an enabling infrastructure, and has been a massive success in bringing together scientific and technical communities to provide the compute power to address previously incomputable problems. Not so in the finance industry. In its current form gLite would be a business disabler. There are other middleware tools that solve the finance communities compute problems much better. Things are moving on, however. There are moves afoot in the open source community to evolve the technology to address other, more sophisticated needs such as utility and interactive computing. In this talk, I will describe how Excelian is providing Grid consultancy services for the finance community and how, through its relationship to the EGEE project, Excelian is helping to identify and exploit opportunities as the research and business worlds converge. Because of the strong third party presence in the finance industry, such opportunities are few and far between, but they are there, especially as we expand sideways into related verticals such as the smaller hedge funds and energy companies. This talk will give an overview of the barriers to adoption of gLite in the finance industry and highlight some of the opportunities offered in this and related industries as the ideas around Grid mature. Speaker Bio: Dr Adam Vile is a senior consultant and head of the Grid and HPC practice at Excelian, a consultancy that focuses on financial markets professional services. He has spent many years in investment banking, as a developer, project manager and architect in both front and back office. Before joining Excelian he was senior Grid and HPC architect at Barclays Capital. Prior to joining investment banking, Adam spent a number of years lecturing in IT and mathematics at a UK University and maintains links with academia through lectures, research and through validation and steering of postgraduate courses. He is a chartered mathematician and was the conference chair of the Institute of Mathematics and its Applications first conference in computational Finance. 4. From Monte Carlo to Wall Street Daniel Egloff, Head of Financial Engineering Computing Unit, Zürich Cantonal Bank High performance computing techniques provide new means to solve computationally hard problems in the financial service industry. First I consider Monte Carlo simulation and illustrate how it can be used to implement a sophisticated credit risk management and economic capital framework. From a HPC perspective, basic Monte Carlo simulation is embarrassingly parallel and can be implemented efficiently on distributed memory clusters. Additional difficulties arise for adaptive variance reduction schemes, if the information content in a sample is very small, and if the amount of simulated date becomes huge such that incremental processing algorithms are indispensable. We discuss the business value of an advanced credit risk quantification which is particularly compelling in these days. While Monte Carlo simulation is a very versatile tool it is not always the preferred solution for the pricing of complex products like multi asset options, structured products, or credit derivatives. As a second application I show how operator methods can be used to develop a pricing framework. The scalability of operator methods relies heavily on optimized dense matrix-matrix multiplications and requires specialized BLAS level-3 implementations provided by specialized FPGA or GPU boards. Speaker Bio: Daniel Egloff studied mathematics, theoretical physics, and computer science at the University of Zurich and the ETH Zurich. He holds a PhD in Mathematics from University of Fribourg, Switzerland. After his PhD he started to work for a large Swiss insurance company in the area of asset and liability management. He continued his professional career in the consulting industry. At KPMG and Arthur Andersen he consulted international clients and implemented quantitative risk management solutions for financial institutions and insurance companies. In 2002 he joined Zurich Cantonal Bank. He was assigned to develop and implement credit portfolio risk and economic capital methodologies. He built up a competence center for high performance and cluster computing. Currently, Daniel Egloff is heading the Financial Computing unit in the ZKB Financial Engineering division. He and his team is engineering and operating high performance cluster applications for computationally intensive problems in financial risk management.« less

Computing for Finance

DOE Office of Scientific and Technical Information (OSTI.GOV)

None

The finance sector is one of the driving forces for the use of distributed or Grid computing for business purposes. The speakers will review the state-of-the-art of high performance computing in the financial sector, and provide insight into how different types of Grid computing – from local clusters to global networks - are being applied to financial applications. They will also describe the use of software and techniques from physics, such as Monte Carlo simulations, in the financial world. There will be four talks of 20min each. The talk abstracts and speaker bios are listed below. This will be followedmore » by a Q&A; panel session with the speakers. From 19:00 onwards there will be a networking cocktail for audience and speakers. This is an EGEE / CERN openlab event organized in collaboration with the regional business network rezonance.ch. A webcast of the event will be made available for subsequent viewing, along with powerpoint material presented by the speakers. Attendance is free and open to all. Registration is mandatory via www.rezonance.ch, including for CERN staff. 1. Overview of High Performance Computing in the Financial Industry. Michael Yoo, Managing Director, Head of the Technical Council, UBS. Presentation will describe the key business challenges driving the need for HPC solutions, describe the means in which those challenges are being addressed within UBS (such as GRID) as well as the limitations of some of these solutions, and assess some of the newer HPC technologies which may also play a role in the Financial Industry in the future. Speaker Bio: Michael originally joined the former Swiss Bank Corporation in 1994 in New York as a developer on a large data warehouse project. In 1996 he left SBC and took a role with Fidelity Investments in Boston. Unable to stay away for long, he returned to SBC in 1997 while working for Perot Systems in Singapore. Finally, in 1998 he formally returned to UBS in Stamford following the merger with SBC and has remained with UBS for the past 9 years. During his tenure at UBS, he has had a number of leadership roles within IT in development, support and architecture. In 2006 Michael relocated to Switzerland to take up his current role as head of the UBS IB Technical Council, responsible for the overall technology strategy and vision of the Investment Bank. One of Michael's key responsibilities is to manage the UBS High Performance Computing Research Lab and he has been involved in a number of initiatives in the HPC space. 2. Grid in the Commercial WorldFred Gedling, Chief Technology Officer EMEA and Senior Vice President Global Services, DataSynapse. Grid computing gets mentions in the press for community programs starting last decade with "Seti@Home". Government, national and supranational initiatives in grid receive some press. One of the IT-industries' best-kept secrets is the use of grid computing by commercial organizations with spectacular results. Grid Computing and its evolution into Application Virtualization is discussed and how this is key to the next generation data center. Speaker Bio: Fred Gedling holds the joint roles of Chief Technology Officer for EMEA and Senior Vice President of Global Services at DataSynapse, a global provider of application virtualisation software. Based in London and working closely with organisations seeking to optimise their IT infrastructures, Fred offers unique insights into the technology of virtualisation as well as the methodology of establishing ROI and rapid deployment to the immediate advantage of the business. Fred has more than fifteen years experience of enterprise middleware and high-performance infrastructures. Prior to DataSynapse he worked in high performance CRM middleware and was the CTO EMEA for New Era of Networks (NEON) during the rapid growth of Enterprise Application Integration. His 25-year career in technology also includes management positions at Goldman Sachs and Stratus Computer. Fred holds a First Class Bsc (Hons) degree in Physics with Astrophysics from the University of Leeds and had the privilege of being a summer student at CERN.3. Opportunities for gLite in finance and related industriesAdam Vile, Head of Grid, HPC and Technical Computing, Excelian Ltd.gLite, the Grid software developed by the EGEE project, has been exceedingly successful as an enabling infrastructure, and has been a massive success in bringing together scientific and technical communities to provide the compute power to address previously incomputable problems. Not so in the finance industry. In its current form gLite would be a business disabler. There are other middleware tools that solve the finance communities compute problems much better. Things are moving on, however. There are moves afoot in the open source community to evolve the technology to address other, more sophisticated needs such as utility and interactive computing. In this talk, I will describe how Excelian is providing Grid consultancy services for the finance community and how, through its relationship to the EGEE project, Excelian is helping to identify and exploit opportunities as the research and business worlds converge. Because of the strong third party presence in the finance industry, such opportunities are few and far between, but they are there, especially as we expand sideways into related verticals such as the smaller hedge funds and energy companies. This talk will give an overview of the barriers to adoption of gLite in the finance industry and highlight some of the opportunities offered in this and related industries as the ideas around Grid mature. Speaker Bio: Dr Adam Vile is a senior consultant and head of the Grid and HPC practice at Excelian, a consultancy that focuses on financial markets professional services. He has spent many years in investment banking, as a developer, project manager and architect in both front and back office. Before joining Excelian he was senior Grid and HPC architect at Barclays Capital. Prior to joining investment banking, Adam spent a number of years lecturing in IT and mathematics at a UK University and maintains links with academia through lectures, research and through validation and steering of postgraduate courses. He is a chartered mathematician and was the conference chair of the Institute of Mathematics and its Applications first conference in computational Finance.4. From Monte Carlo to Wall Street Daniel Egloff, Head of Financial Engineering Computing Unit, Zürich Cantonal Bank High performance computing techniques provide new means to solve computationally hard problems in the financial service industry. First I consider Monte Carlo simulation and illustrate how it can be used to implement a sophisticated credit risk management and economic capital framework. From a HPC perspective, basic Monte Carlo simulation is embarrassingly parallel and can be implemented efficiently on distributed memory clusters. Additional difficulties arise for adaptive variance reduction schemes, if the information content in a sample is very small, and if the amount of simulated date becomes huge such that incremental processing algorithms are indispensable. We discuss the business value of an advanced credit risk quantification which is particularly compelling in these days. While Monte Carlo simulation is a very versatile tool it is not always the preferred solution for the pricing of complex products like multi asset options, structured products, or credit derivatives. As a second application I show how operator methods can be used to develop a pricing framework. The scalability of operator methods relies heavily on optimized dense matrix-matrix multiplications and requires specialized BLAS level-3 implementations provided by specialized FPGA or GPU boards. Speaker Bio: Daniel Egloff studied mathematics, theoretical physics, and computer science at the University of Zurich and the ETH Zurich. He holds a PhD in Mathematics from University of Fribourg, Switzerland. After his PhD he started to work for a large Swiss insurance company in the area of asset and liability management. He continued his professional career in the consulting industry. At KPMG and Arthur Andersen he consulted international clients and implemented quantitative risk management solutions for financial institutions and insurance companies. In 2002 he joined Zurich Cantonal Bank. He was assigned to develop and implement credit portfolio risk and economic capital methodologies. He built up a competence center for high performance and cluster computing. Currently, Daniel Egloff is heading the Financial Computing unit in the ZKB Financial Engineering division. He and his team is engineering and operating high performance cluster applications for computationally intensive problems in financial risk management.« less

Computing for Finance

DOE Office of Scientific and Technical Information (OSTI.GOV)

None

The finance sector is one of the driving forces for the use of distributed or Grid computing for business purposes. The speakers will review the state-of-the-art of high performance computing in the financial sector, and provide insight into how different types of Grid computing – from local clusters to global networks - are being applied to financial applications. They will also describe the use of software and techniques from physics, such as Monte Carlo simulations, in the financial world. There will be four talks of 20min each. The talk abstracts and speaker bios are listed below. This will be followedmore » by a Q&A; panel session with the speakers. From 19:00 onwards there will be a networking cocktail for audience and speakers. This is an EGEE / CERN openlab event organized in collaboration with the regional business network rezonance.ch. A webcast of the event will be made available for subsequent viewing, along with powerpoint material presented by the speakers. Attendance is free and open to all. Registration is mandatory via www.rezonance.ch, including for CERN staff. 1. Overview of High Performance Computing in the Financial Industry Michael Yoo, Managing Director, Head of the Technical Council, UBS Presentation will describe the key business challenges driving the need for HPC solutions, describe the means in which those challenges are being addressed within UBS (such as GRID) as well as the limitations of some of these solutions, and assess some of the newer HPC technologies which may also play a role in the Financial Industry in the future. Speaker Bio: Michael originally joined the former Swiss Bank Corporation in 1994 in New York as a developer on a large data warehouse project. In 1996 he left SBC and took a role with Fidelity Investments in Boston. Unable to stay away for long, he returned to SBC in 1997 while working for Perot Systems in Singapore. Finally, in 1998 he formally returned to UBS in Stamford following the merger with SBC and has remained with UBS for the past 9 years. During his tenure at UBS, he has had a number of leadership roles within IT in development, support and architecture. In 2006 Michael relocated to Switzerland to take up his current role as head of the UBS IB Technical Council, responsible for the overall technology strategy and vision of the Investment Bank. One of Michael's key responsibilities is to manage the UBS High Performance Computing Research Lab and he has been involved in a number of initiatives in the HPC space. 2. Grid in the Commercial WorldFred Gedling, Chief Technology Officer EMEA and Senior Vice President Global Services, DataSynapse Grid computing gets mentions in the press for community programs starting last decade with Government, national and supranational initiatives in grid receive some press. One of the IT-industries' best-kept secrets is the use of grid computing by commercial organizations with spectacular results. Grid Computing and its evolution into Application Virtualization is discussed and how this is key to the next generation data center. Speaker Bio: Fred Gedling holds the joint roles of Chief Technology Officer for EMEA and Senior Vice President of Global Services at DataSynapse, a global provider of application virtualisation software. Based in London and working closely with organisations seeking to optimise their IT infrastructures, Fred offers unique insights into the technology of virtualisation as well as the methodology of establishing ROI and rapid deployment to the immediate advantage of the business. Fred has more than fifteen years experience of enterprise middleware and high-performance infrastructures. Prior to DataSynapse he worked in high performance CRM middleware and was the CTO EMEA for New Era of Networks (NEON) during the rapid growth of Enterprise Application Integration. His 25-year career in technology also includes management positions at Goldman Sachs and Stratus Computer. Fred holds a First Class Bsc (Hons) degree in Physics with Astrophysics from the University of Leeds and had the privilege of being a summer student at CERN.3. Opportunities for gLite in finance and related industriesAdam Vile, Head of Grid, HPC and Technical Computing, Excelian Ltd.gLite, the Grid software developed by the EGEE project, has been exceedingly successful as an enabling infrastructure, and has been a massive success in bringing together scientific and technical communities to provide the compute power to address previously incomputable problems. Not so in the finance industry. In its current form gLite would be a business disabler. There are other middleware tools that solve the finance communities compute problems much better. Things are moving on, however. There are moves afoot in the open source community to evolve the technology to address other, more sophisticated needs such as utility and interactive computing. In this talk, I will describe how Excelian is providing Grid consultancy services for the finance community and how, through its relationship to the EGEE project, Excelian is helping to identify and exploit opportunities as the research and business worlds converge. Because of the strong third party presence in the finance industry, such opportunities are few and far between, but they are there, especially as we expand sideways into related verticals such as the smaller hedge funds and energy companies. This talk will give an overview of the barriers to adoption of gLite in the finance industry and highlight some of the opportunities offered in this and related industries as the ideas around Grid mature. Speaker Bio: Dr Adam Vile is a senior consultant and head of the Grid and HPC practice at Excelian, a consultancy that focuses on financial markets professional services. He has spent many years in investment banking, as a developer, project manager and architect in both front and back office. Before joining Excelian he was senior Grid and HPC architect at Barclays Capital. Prior to joining investment banking, Adam spent a number of years lecturing in IT and mathematics at a UK University and maintains links with academia through lectures, research and through validation and steering of postgraduate courses. He is a chartered mathematician and was the conference chair of the Institute of Mathematics and its Applications first conference in computational Finance. 4. From Monte Carlo to Wall Street Daniel Egloff, Head of Financial Engineering Computing Unit, Zürich Cantonal Bank High performance computing techniques provide new means to solve computationally hard problems in the financial service industry. First I consider Monte Carlo simulation and illustrate how it can be used to implement a sophisticated credit risk management and economic capital framework. From a HPC perspective, basic Monte Carlo simulation is embarrassingly parallel and can be implemented efficiently on distributed memory clusters. Additional difficulties arise for adaptive variance reduction schemes, if the information content in a sample is very small, and if the amount of simulated date becomes huge such that incremental processing algorithms are indispensable. We discuss the business value of an advanced credit risk quantification which is particularly compelling in these days. While Monte Carlo simulation is a very versatile tool it is not always the preferred solution for the pricing of complex products like multi asset options, structured products, or credit derivatives. As a second application I show how operator methods can be used to develop a pricing framework. The scalability of operator methods relies heavily on optimized dense matrix-matrix multiplications and requires specialized BLAS level-3 implementations provided by specialized FPGA or GPU boards. Speaker Bio: Daniel Egloff studied mathematics, theoretical physics, and computer science at the University of Zurich and the ETH Zurich. He holds a PhD in Mathematics from University of Fribourg, Switzerland. After his PhD he started to work for a large Swiss insurance company in the area of asset and liability management. He continued his professional career in the consulting industry. At KPMG and Arthur Andersen he consulted international clients and implemented quantitative risk management solutions for financial institutions and insurance companies. In 2002 he joined Zurich Cantonal Bank. He was assigned to develop and implement credit portfolio risk and economic capital methodologies. He built up a competence center for high performance and cluster computing. Currently, Daniel Egloff is heading the Financial Computing unit in the ZKB Financial Engineering division. He and his team is engineering and operating high performance cluster applications for computationally intensive problems in financial risk management.« less

Computing for Finance

DOE Office of Scientific and Technical Information (OSTI.GOV)

None

The finance sector is one of the driving forces for the use of distributed or Grid computing for business purposes. The speakers will review the state-of-the-art of high performance computing in the financial sector, and provide insight into how different types of Grid computing – from local clusters to global networks - are being applied to financial applications. They will also describe the use of software and techniques from physics, such as Monte Carlo simulations, in the financial world. There will be four talks of 20min each. The talk abstracts and speaker bios are listed below. This will be followedmore » by a Q&A; panel session with the speakers. From 19:00 onwards there will be a networking cocktail for audience and speakers. This is an EGEE / CERN openlab event organized in collaboration with the regional business network rezonance.ch. A webcast of the event will be made available for subsequent viewing, along with powerpoint material presented by the speakers. Attendance is free and open to all. Registration is mandatory via www.rezonance.ch, including for CERN staff. 1. Overview of High Performance Computing in the Financial Industry Michael Yoo, Managing Director, Head of the Technical Council, UBS Presentation will describe the key business challenges driving the need for HPC solutions, describe the means in which those challenges are being addressed within UBS (such as GRID) as well as the limitations of some of these solutions, and assess some of the newer HPC technologies which may also play a role in the Financial Industry in the future. Speaker Bio: Michael originally joined the former Swiss Bank Corporation in 1994 in New York as a developer on a large data warehouse project. In 1996 he left SBC and took a role with Fidelity Investments in Boston. Unable to stay away for long, he returned to SBC in 1997 while working for Perot Systems in Singapore. Finally, in 1998 he formally returned to UBS in Stamford following the merger with SBC and has remained with UBS for the past 9 years. During his tenure at UBS, he has had a number of leadership roles within IT in development, support and architecture. In 2006 Michael relocated to Switzerland to take up his current role as head of the UBS IB Technical Council, responsible for the overall technology strategy and vision of the Investment Bank. One of Michael's key responsibilities is to manage the UBS High Performance Computing Research Lab and he has been involved in a number of initiatives in the HPC space. 2. Grid in the Commercial WorldFred Gedling, Chief Technology Officer EMEA and Senior Vice President Global Services, DataSynapse Grid computing gets mentions in the press for community programs starting last decade with Government, national and supranational initiatives in grid receive some press. One of the IT-industries' best-kept secrets is the use of grid computing by commercial organizations with spectacular results. Grid Computing and its evolution into Application Virtualization is discussed and how this is key to the next generation data center. Speaker Bio: Fred Gedling holds the joint roles of Chief Technology Officer for EMEA and Senior Vice President of Global Services at DataSynapse, a global provider of application virtualisation software. Based in London and working closely with organisations seeking to optimise their IT infrastructures, Fred offers unique insights into the technology of virtualisation as well as the methodology of establishing ROI and rapid deployment to the immediate advantage of the business. Fred has more than fifteen years experience of enterprise middleware and high-performance infrastructures. Prior to DataSynapse he worked in high performance CRM middleware and was the CTO EMEA for New Era of Networks (NEON) during the rapid growth of Enterprise Application Integration. His 25-year career in technology also includes management positions at Goldman Sachs and Stratus Computer. Fred holds a First Class Bsc (Hons) degree in Physics with Astrophysics from the University of Leeds and had the privilege of being a summer student at CERN.3. Opportunities for gLite in finance and related industriesAdam Vile, Head of Grid, HPC and Technical Computing, Excelian Ltd.gLite, the Grid software developed by the EGEE project, has been exceedingly successful as an enabling infrastructure, and has been a massive success in bringing together scientific and technical communities to provide the compute power to address previously incomputable problems. Not so in the finance industry. In its current form gLite would be a business disabler. There are other middleware tools that solve the finance communities compute problems much better. Things are moving on, however. There are moves afoot in the open source community to evolve the technology to address other, more sophisticated needs such as utility and interactive computing. In this talk, I will describe how Excelian is providing Grid consultancy services for the finance community and how, through its relationship to the EGEE project, Excelian is helping to identify and exploit opportunities as the research and business worlds converge. Because of the strong third party presence in the finance industry, such opportunities are few and far between, but they are there, especially as we expand sideways into related verticals such as the smaller hedge funds and energy companies. This talk will give an overview of the barriers to adoption of gLite in the finance industry and highlight some of the opportunities offered in this and related industries as the ideas around Grid mature. Speaker Bio: Dr Adam Vile is a senior consultant and head of the Grid and HPC practice at Excelian, a consultancy that focuses on financial markets professional services. He has spent many years in investment banking, as a developer, project manager and architect in both front and back office. Before joining Excelian he was senior Grid and HPC architect at Barclays Capital. Prior to joining investment banking, Adam spent a number of years lecturing in IT and mathematics at a UK University and maintains links with academia through lectures, research and through validation and steering of postgraduate courses. He is a chartered mathematician and was the conference chair of the Institute of Mathematics and its Applications first conference in computational Finance.4. From Monte Carlo to Wall Street Daniel Egloff, Head of Financial Engineering Computing Unit, Zürich Cantonal Bank High performance computing techniques provide new means to solve computationally hard problems in the financial service industry. First I consider Monte Carlo simulation and illustrate how it can be used to implement a sophisticated credit risk management and economic capital framework. From a HPC perspective, basic Monte Carlo simulation is embarrassingly parallel and can be implemented efficiently on distributed memory clusters. Additional difficulties arise for adaptive variance reduction schemes, if the information content in a sample is very small, and if the amount of simulated date becomes huge such that incremental processing algorithms are indispensable. We discuss the business value of an advanced credit risk quantification which is particularly compelling in these days. While Monte Carlo simulation is a very versatile tool it is not always the preferred solution for the pricing of complex products like multi asset options, structured products, or credit derivatives. As a second application I show how operator methods can be used to develop a pricing framework. The scalability of operator methods relies heavily on optimized dense matrix-matrix multiplications and requires specialized BLAS level-3 implementations provided by specialized FPGA or GPU boards. Speaker Bio: Daniel Egloff studied mathematics, theoretical physics, and computer science at the University of Zurich and the ETH Zurich. He holds a PhD in Mathematics from University of Fribourg, Switzerland. After his PhD he started to work for a large Swiss insurance company in the area of asset and liability management. He continued his professional career in the consulting industry. At KPMG and Arthur Andersen he consulted international clients and implemented quantitative risk management solutions for financial institutions and insurance companies. In 2002 he joined Zurich Cantonal Bank. He was assigned to develop and implement credit portfolio risk and economic capital methodologies. He built up a competence center for high performance and cluster computing. Currently, Daniel Egloff is heading the Financial Computing unit in the ZKB Financial Engineering division. He and his team is engineering and operating high performance cluster applications for computationally intensive problems in financial risk management.« less

Computing for Finance

DOE Office of Scientific and Technical Information (OSTI.GOV)

None

The finance sector is one of the driving forces for the use of distributed or Grid computing for business purposes. The speakers will review the state-of-the-art of high performance computing in the financial sector, and provide insight into how different types of Grid computing – from local clusters to global networks - are being applied to financial applications. They will also describe the use of software and techniques from physics, such as Monte Carlo simulations, in the financial world. There will be four talks of 20min each. The talk abstracts and speaker bios are listed below. This will be followedmore » by a Q&A; panel session with the speakers. From 19:00 onwards there will be a networking cocktail for audience and speakers. This is an EGEE / CERN openlab event organized in collaboration with the regional business network rezonance.ch. A webcast of the event will be made available for subsequent viewing, along with powerpoint material presented by the speakers. Attendance is free and open to all. Registration is mandatory via www.rezonance.ch, including for CERN staff. 1. Overview of High Performance Computing in the Financial Industry Michael Yoo, Managing Director, Head of the Technical Council, UBS Presentation will describe the key business challenges driving the need for HPC solutions, describe the means in which those challenges are being addressed within UBS (such as GRID) as well as the limitations of some of these solutions, and assess some of the newer HPC technologies which may also play a role in the Financial Industry in the future. Speaker Bio: Michael originally joined the former Swiss Bank Corporation in 1994 in New York as a developer on a large data warehouse project. In 1996 he left SBC and took a role with Fidelity Investments in Boston. Unable to stay away for long, he returned to SBC in 1997 while working for Perot Systems in Singapore. Finally, in 1998 he formally returned to UBS in Stamford following the merger with SBC and has remained with UBS for the past 9 years. During his tenure at UBS, he has had a number of leadership roles within IT in development, support and architecture. In 2006 Michael relocated to Switzerland to take up his current role as head of the UBS IB Technical Council, responsible for the overall technology strategy and vision of the Investment Bank. One of Michael's key responsibilities is to manage the UBS High Performance Computing Research Lab and he has been involved in a number of initiatives in the HPC space. 2. Grid in the Commercial WorldFred Gedling, Chief Technology Officer EMEA and Senior Vice President Global Services, DataSynapse Grid computing gets mentions in the press for community programs starting last decade with Government, national and supranational initiatives in grid receive some press. One of the IT-industries' best-kept secrets is the use of grid computing by commercial organizations with spectacular results. Grid Computing and its evolution into Application Virtualization is discussed and how this is key to the next generation data center. Speaker Bio: Fred Gedling holds the joint roles of Chief Technology Officer for EMEA and Senior Vice President of Global Services at DataSynapse, a global provider of application virtualisation software. Based in London and working closely with organisations seeking to optimise their IT infrastructures, Fred offers unique insights into the technology of virtualisation as well as the methodology of establishing ROI and rapid deployment to the immediate advantage of the business. Fred has more than fifteen years experience of enterprise middleware and high-performance infrastructures. Prior to DataSynapse he worked in high performance CRM middleware and was the CTO EMEA for New Era of Networks (NEON) during the rapid growth of Enterprise Application Integration. His 25-year career in technology also includes management positions at Goldman Sachs and Stratus Computer. Fred holds a First Class Bsc (Hons) degree in Physics with Astrophysics from the University of Leeds and had the privilege of being a summer student at CERN.3. Opportunities for gLite in finance and related industries Adam Vile, Head of Grid, HPC and Technical Computing, Excelian Ltd.gLite, the Grid software developed by the EGEE project, has been exceedingly successful as an enabling infrastructure, and has been a massive success in bringing together scientific and technical communities to provide the compute power to address previously incomputable problems. Not so in the finance industry. In its current form gLite would be a business disabler. There are other middleware tools that solve the finance communities compute problems much better. Things are moving on, however. There are moves afoot in the open source community to evolve the technology to address other, more sophisticated needs such as utility and interactive computing. In this talk, I will describe how Excelian is providing Grid consultancy services for the finance community and how, through its relationship to the EGEE project, Excelian is helping to identify and exploit opportunities as the research and business worlds converge. Because of the strong third party presence in the finance industry, such opportunities are few and far between, but they are there, especially as we expand sideways into related verticals such as the smaller hedge funds and energy companies. This talk will give an overview of the barriers to adoption of gLite in the finance industry and highlight some of the opportunities offered in this and related industries as the ideas around Grid mature. Speaker Bio: Dr Adam Vile is a senior consultant and head of the Grid and HPC practice at Excelian, a consultancy that focuses on financial markets professional services. He has spent many years in investment banking, as a developer, project manager and architect in both front and back office. Before joining Excelian he was senior Grid and HPC architect at Barclays Capital. Prior to joining investment banking, Adam spent a number of years lecturing in IT and mathematics at a UK University and maintains links with academia through lectures, research and through validation and steering of postgraduate courses. He is a chartered mathematician and was the conference chair of the Institute of Mathematics and its Applications first conference in computational Finance.4. From Monte Carlo to Wall Street Daniel Egloff, Head of Financial Engineering Computing Unit, Zürich Cantonal Bank High performance computing techniques provide new means to solve computationally hard problems in the financial service industry. First I consider Monte Carlo simulation and illustrate how it can be used to implement a sophisticated credit risk management and economic capital framework. From a HPC perspective, basic Monte Carlo simulation is embarrassingly parallel and can be implemented efficiently on distributed memory clusters. Additional difficulties arise for adaptive variance reduction schemes, if the information content in a sample is very small, and if the amount of simulated date becomes huge such that incremental processing algorithms are indispensable. We discuss the business value of an advanced credit risk quantification which is particularly compelling in these days. While Monte Carlo simulation is a very versatile tool it is not always the preferred solution for the pricing of complex products like multi asset options, structured products, or credit derivatives. As a second application I show how operator methods can be used to develop a pricing framework. The scalability of operator methods relies heavily on optimized dense matrix-matrix multiplications and requires specialized BLAS level-3 implementations provided by specialized FPGA or GPU boards. Speaker Bio: Daniel Egloff studied mathematics, theoretical physics, and computer science at the University of Zurich and the ETH Zurich. He holds a PhD in Mathematics from University of Fribourg, Switzerland. After his PhD he started to work for a large Swiss insurance company in the area of asset and liability management. He continued his professional career in the consulting industry. At KPMG and Arthur Andersen he consulted international clients and implemented quantitative risk management solutions for financial institutions and insurance companies. In 2002 he joined Zurich Cantonal Bank. He was assigned to develop and implement credit portfolio risk and economic capital methodologies. He built up a competence center for high performance and cluster computing. Currently, Daniel Egloff is heading the Financial Computing unit in the ZKB Financial Engineering division. He and his team is engineering and operating high performance cluster applications for computationally intensive problems in financial risk management.« less

Computing for Finance

DOE Office of Scientific and Technical Information (OSTI.GOV)

None

The finance sector is one of the driving forces for the use of distributed or Grid computing for business purposes. The speakers will review the state-of-the-art of high performance computing in the financial sector, and provide insight into how different types of Grid computing – from local clusters to global networks - are being applied to financial applications. They will also describe the use of software and techniques from physics, such as Monte Carlo simulations, in the financial world. There will be four talks of 20min each. The talk abstracts and speaker bios are listed below. This will be followedmore » by a Q&A; panel session with the speakers. From 19:00 onwards there will be a networking cocktail for audience and speakers. This is an EGEE / CERN openlab event organized in collaboration with the regional business network rezonance.ch. A webcast of the event will be made available for subsequent viewing, along with powerpoint material presented by the speakers. Attendance is free and open to all. Registration is mandatory via www.rezonance.ch, including for CERN staff. 1. Overview of High Performance Computing in the Financial Industry Michael Yoo, Managing Director, Head of the Technical Council, UBS Presentation will describe the key business challenges driving the need for HPC solutions, describe the means in which those challenges are being addressed within UBS (such as GRID) as well as the limitations of some of these solutions, and assess some of the newer HPC technologies which may also play a role in the Financial Industry in the future. Speaker Bio: Michael originally joined the former Swiss Bank Corporation in 1994 in New York as a developer on a large data warehouse project. In 1996 he left SBC and took a role with Fidelity Investments in Boston. Unable to stay away for long, he returned to SBC in 1997 while working for Perot Systems in Singapore. Finally, in 1998 he formally returned to UBS in Stamford following the merger with SBC and has remained with UBS for the past 9 years. During his tenure at UBS, he has had a number of leadership roles within IT in development, support and architecture. In 2006 Michael relocated to Switzerland to take up his current role as head of the UBS IB Technical Council, responsible for the overall technology strategy and vision of the Investment Bank. One of Michael's key responsibilities is to manage the UBS High Performance Computing Research Lab and he has been involved in a number of initiatives in the HPC space. 2. Grid in the Commercial WorldFred Gedling, Chief Technology Officer EMEA and Senior Vice President Global Services, DataSynapse Grid computing gets mentions in the press for community programs starting last decade with Seti@Home. Government, national and supranational initiatives in grid receive some press. One of the IT-industries' best-kept secrets is the use of grid computing by commercial organizations with spectacular results. Grid Computing and its evolution into Application Virtualization is discussed and how this is key to the next generation data center. Speaker Bio: Fred Gedling holds the joint roles of Chief Technology Officer for EMEA and Senior Vice President of Global Services at DataSynapse, a global provider of application virtualisation software. Based in London and working closely with organisations seeking to optimise their IT infrastructures, Fred offers unique insights into the technology of virtualisation as well as the methodology of establishing ROI and rapid deployment to the immediate advantage of the business. Fred has more than fifteen years experience of enterprise middleware and high-performance infrastructures. Prior to DataSynapse he worked in high performance CRM middleware and was the CTO EMEA for New Era of Networks (NEON) during the rapid growth of Enterprise Application Integration. His 25-year career in technology also includes management positions at Goldman Sachs and Stratus Computer. Fred holds a First Class Bsc (Hons) degree in Physics with Astrophysics from the University of Leeds and had the privilege of being a summer student at CERN. 3. Opportunities for gLite in finance and related industriesAdam Vile, Head of Grid, HPC and Technical Computing, Excelian Ltd.gLite, the Grid software developed by the EGEE project, has been exceedingly successful as an enabling infrastructure, and has been a massive success in bringing together scientific and technical communities to provide the compute power to address previously incomputable problems. Not so in the finance industry. In its current form gLite would be a business disabler. There are other middleware tools that solve the finance communities compute problems much better. Things are moving on, however. There are moves afoot in the open source community to evolve the technology to address other, more sophisticated needs such as utility and interactive computing. In this talk, I will describe how Excelian is providing Grid consultancy services for the finance community and how, through its relationship to the EGEE project, Excelian is helping to identify and exploit opportunities as the research and business worlds converge. Because of the strong third party presence in the finance industry, such opportunities are few and far between, but they are there, especially as we expand sideways into related verticals such as the smaller hedge funds and energy companies. This talk will give an overview of the barriers to adoption of gLite in the finance industry and highlight some of the opportunities offered in this and related industries as the ideas around Grid mature. Speaker Bio: Dr Adam Vile is a senior consultant and head of the Grid and HPC practice at Excelian, a consultancy that focuses on financial markets professional services. He has spent many years in investment banking, as a developer, project manager and architect in both front and back office. Before joining Excelian he was senior Grid and HPC architect at Barclays Capital. Prior to joining investment banking, Adam spent a number of years lecturing in IT and mathematics at a UK University and maintains links with academia through lectures, research and through validation and steering of postgraduate courses. He is a chartered mathematician and was the conference chair of the Institute of Mathematics and its Applications first conference in computational Finance.4. From Monte Carlo to Wall Street Daniel Egloff, Head of Financial Engineering Computing Unit, Zürich Cantonal Bank High performance computing techniques provide new means to solve computationally hard problems in the financial service industry. First I consider Monte Carlo simulation and illustrate how it can be used to implement a sophisticated credit risk management and economic capital framework. From a HPC perspective, basic Monte Carlo simulation is embarrassingly parallel and can be implemented efficiently on distributed memory clusters. Additional difficulties arise for adaptive variance reduction schemes, if the information content in a sample is very small, and if the amount of simulated date becomes huge such that incremental processing algorithms are indispensable. We discuss the business value of an advanced credit risk quantification which is particularly compelling in these days. While Monte Carlo simulation is a very versatile tool it is not always the preferred solution for the pricing of complex products like multi asset options, structured products, or credit derivatives. As a second application I show how operator methods can be used to develop a pricing framework. The scalability of operator methods relies heavily on optimized dense matrix-matrix multiplications and requires specialized BLAS level-3 implementations provided by specialized FPGA or GPU boards. Speaker Bio: Daniel Egloff studied mathematics, theoretical physics, and computer science at the University of Zurich and the ETH Zurich. He holds a PhD in Mathematics from University of Fribourg, Switzerland. After his PhD he started to work for a large Swiss insurance company in the area of asset and liability management. He continued his professional career in the consulting industry. At KPMG and Arthur Andersen he consulted international clients and implemented quantitative risk management solutions for financial institutions and insurance companies. In 2002 he joined Zurich Cantonal Bank. He was assigned to develop and implement credit portfolio risk and economic capital methodologies. He built up a competence center for high performance and cluster computing. Currently, Daniel Egloff is heading the Financial Computing unit in the ZKB Financial Engineering division. He and his team is engineering and operating high performance cluster applications for computationally intensive problems in financial risk management.« less

Computing for Finance

ScienceCinema

None

2018-02-01

The finance sector is one of the driving forces for the use of distributed or Grid computing for business purposes. The speakers will review the state-of-the-art of high performance computing in the financial sector, and provide insight into how different types of Grid computing – from local clusters to global networks - are being applied to financial applications. They will also describe the use of software and techniques from physics, such as Monte Carlo simulations, in the financial world. There will be four talks of 20min each. The talk abstracts and speaker bios are listed below. This will be followed by a Q&A; panel session with the speakers. From 19:00 onwards there will be a networking cocktail for audience and speakers. This is an EGEE / CERN openlab event organized in collaboration with the regional business network rezonance.ch. A webcast of the event will be made available for subsequent viewing, along with powerpoint material presented by the speakers. Attendance is free and open to all. Registration is mandatory via www.rezonance.ch, including for CERN staff. 1. Overview of High Performance Computing in the Financial Industry Michael Yoo, Managing Director, Head of the Technical Council, UBS Presentation will describe the key business challenges driving the need for HPC solutions, describe the means in which those challenges are being addressed within UBS (such as GRID) as well as the limitations of some of these solutions, and assess some of the newer HPC technologies which may also play a role in the Financial Industry in the future. Speaker Bio: Michael originally joined the former Swiss Bank Corporation in 1994 in New York as a developer on a large data warehouse project. In 1996 he left SBC and took a role with Fidelity Investments in Boston. Unable to stay away for long, he returned to SBC in 1997 while working for Perot Systems in Singapore. Finally, in 1998 he formally returned to UBS in Stamford following the merger with SBC and has remained with UBS for the past 9 years. During his tenure at UBS, he has had a number of leadership roles within IT in development, support and architecture. In 2006 Michael relocated to Switzerland to take up his current role as head of the UBS IB Technical Council, responsible for the overall technology strategy and vision of the Investment Bank. One of Michael's key responsibilities is to manage the UBS High Performance Computing Research Lab and he has been involved in a number of initiatives in the HPC space. 2. Grid in the Commercial WorldFred Gedling, Chief Technology Officer EMEA and Senior Vice President Global Services, DataSynapse Grid computing gets mentions in the press for community programs starting last decade with Government, national and supranational initiatives in grid receive some press. One of the IT-industries' best-kept secrets is the use of grid computing by commercial organizations with spectacular results. Grid Computing and its evolution into Application Virtualization is discussed and how this is key to the next generation data center. Speaker Bio: Fred Gedling holds the joint roles of Chief Technology Officer for EMEA and Senior Vice President of Global Services at DataSynapse, a global provider of application virtualisation software. Based in London and working closely with organisations seeking to optimise their IT infrastructures, Fred offers unique insights into the technology of virtualisation as well as the methodology of establishing ROI and rapid deployment to the immediate advantage of the business. Fred has more than fifteen years experience of enterprise middleware and high-performance infrastructures. Prior to DataSynapse he worked in high performance CRM middleware and was the CTO EMEA for New Era of Networks (NEON) during the rapid growth of Enterprise Application Integration. His 25-year career in technology also includes management positions at Goldman Sachs and Stratus Computer. Fred holds a First Class Bsc (Hons) degree in Physics with Astrophysics from the University of Leeds and had the privilege of being a summer student at CERN.3. Opportunities for gLite in finance and related industriesAdam Vile, Head of Grid, HPC and Technical Computing, Excelian Ltd.gLite, the Grid software developed by the EGEE project, has been exceedingly successful as an enabling infrastructure, and has been a massive success in bringing together scientific and technical communities to provide the compute power to address previously incomputable problems. Not so in the finance industry. In its current form gLite would be a business disabler. There are other middleware tools that solve the finance communities compute problems much better. Things are moving on, however. There are moves afoot in the open source community to evolve the technology to address other, more sophisticated needs such as utility and interactive computing. In this talk, I will describe how Excelian is providing Grid consultancy services for the finance community and how, through its relationship to the EGEE project, Excelian is helping to identify and exploit opportunities as the research and business worlds converge. Because of the strong third party presence in the finance industry, such opportunities are few and far between, but they are there, especially as we expand sideways into related verticals such as the smaller hedge funds and energy companies. This talk will give an overview of the barriers to adoption of gLite in the finance industry and highlight some of the opportunities offered in this and related industries as the ideas around Grid mature. Speaker Bio: Dr Adam Vile is a senior consultant and head of the Grid and HPC practice at Excelian, a consultancy that focuses on financial markets professional services. He has spent many years in investment banking, as a developer, project manager and architect in both front and back office. Before joining Excelian he was senior Grid and HPC architect at Barclays Capital. Prior to joining investment banking, Adam spent a number of years lecturing in IT and mathematics at a UK University and maintains links with academia through lectures, research and through validation and steering of postgraduate courses. He is a chartered mathematician and was the conference chair of the Institute of Mathematics and its Applications first conference in computational Finance.4. From Monte Carlo to Wall Street Daniel Egloff, Head of Financial Engineering Computing Unit, Zürich Cantonal Bank High performance computing techniques provide new means to solve computationally hard problems in the financial service industry. First I consider Monte Carlo simulation and illustrate how it can be used to implement a sophisticated credit risk management and economic capital framework. From a HPC perspective, basic Monte Carlo simulation is embarrassingly parallel and can be implemented efficiently on distributed memory clusters. Additional difficulties arise for adaptive variance reduction schemes, if the information content in a sample is very small, and if the amount of simulated date becomes huge such that incremental processing algorithms are indispensable. We discuss the business value of an advanced credit risk quantification which is particularly compelling in these days. While Monte Carlo simulation is a very versatile tool it is not always the preferred solution for the pricing of complex products like multi asset options, structured products, or credit derivatives. As a second application I show how operator methods can be used to develop a pricing framework. The scalability of operator methods relies heavily on optimized dense matrix-matrix multiplications and requires specialized BLAS level-3 implementations provided by specialized FPGA or GPU boards. Speaker Bio: Daniel Egloff studied mathematics, theoretical physics, and computer science at the University of Zurich and the ETH Zurich. He holds a PhD in Mathematics from University of Fribourg, Switzerland. After his PhD he started to work for a large Swiss insurance company in the area of asset and liability management. He continued his professional career in the consulting industry. At KPMG and Arthur Andersen he consulted international clients and implemented quantitative risk management solutions for financial institutions and insurance companies. In 2002 he joined Zurich Cantonal Bank. He was assigned to develop and implement credit portfolio risk and economic capital methodologies. He built up a competence center for high performance and cluster computing. Currently, Daniel Egloff is heading the Financial Computing unit in the ZKB Financial Engineering division. He and his team is engineering and operating high performance cluster applications for computationally intensive problems in financial risk management.

Computing for Finance

ScienceCinema

None

2018-01-24

The finance sector is one of the driving forces for the use of distributed or Grid computing for business purposes. The speakers will review the state-of-the-art of high performance computing in the financial sector, and provide insight into how different types of Grid computing – from local clusters to global networks - are being applied to financial applications. They will also describe the use of software and techniques from physics, such as Monte Carlo simulations, in the financial world. There will be four talks of 20min each. The talk abstracts and speaker bios are listed below. This will be followed by a Q&A; panel session with the speakers. From 19:00 onwards there will be a networking cocktail for audience and speakers. This is an EGEE / CERN openlab event organized in collaboration with the regional business network rezonance.ch. A webcast of the event will be made available for subsequent viewing, along with powerpoint material presented by the speakers. Attendance is free and open to all. Registration is mandatory via www.rezonance.ch, including for CERN staff. 1. Overview of High Performance Computing in the Financial Industry Michael Yoo, Managing Director, Head of the Technical Council, UBS Presentation will describe the key business challenges driving the need for HPC solutions, describe the means in which those challenges are being addressed within UBS (such as GRID) as well as the limitations of some of these solutions, and assess some of the newer HPC technologies which may also play a role in the Financial Industry in the future. Speaker Bio: Michael originally joined the former Swiss Bank Corporation in 1994 in New York as a developer on a large data warehouse project. In 1996 he left SBC and took a role with Fidelity Investments in Boston. Unable to stay away for long, he returned to SBC in 1997 while working for Perot Systems in Singapore. Finally, in 1998 he formally returned to UBS in Stamford following the merger with SBC and has remained with UBS for the past 9 years. During his tenure at UBS, he has had a number of leadership roles within IT in development, support and architecture. In 2006 Michael relocated to Switzerland to take up his current role as head of the UBS IB Technical Council, responsible for the overall technology strategy and vision of the Investment Bank. One of Michael's key responsibilities is to manage the UBS High Performance Computing Research Lab and he has been involved in a number of initiatives in the HPC space. 2. Grid in the Commercial WorldFred Gedling, Chief Technology Officer EMEA and Senior Vice President Global Services, DataSynapse Grid computing gets mentions in the press for community programs starting last decade with Seti@Home. Government, national and supranational initiatives in grid receive some press. One of the IT-industries' best-kept secrets is the use of grid computing by commercial organizations with spectacular results. Grid Computing and its evolution into Application Virtualization is discussed and how this is key to the next generation data center. Speaker Bio: Fred Gedling holds the joint roles of Chief Technology Officer for EMEA and Senior Vice President of Global Services at DataSynapse, a global provider of application virtualisation software. Based in London and working closely with organisations seeking to optimise their IT infrastructures, Fred offers unique insights into the technology of virtualisation as well as the methodology of establishing ROI and rapid deployment to the immediate advantage of the business. Fred has more than fifteen years experience of enterprise middleware and high-performance infrastructures. Prior to DataSynapse he worked in high performance CRM middleware and was the CTO EMEA for New Era of Networks (NEON) during the rapid growth of Enterprise Application Integration. His 25-year career in technology also includes management positions at Goldman Sachs and Stratus Computer. Fred holds a First Class Bsc (Hons) degree in Physics with Astrophysics from the University of Leeds and had the privilege of being a summer student at CERN. 3. Opportunities for gLite in finance and related industriesAdam Vile, Head of Grid, HPC and Technical Computing, Excelian Ltd.gLite, the Grid software developed by the EGEE project, has been exceedingly successful as an enabling infrastructure, and has been a massive success in bringing together scientific and technical communities to provide the compute power to address previously incomputable problems. Not so in the finance industry. In its current form gLite would be a business disabler. There are other middleware tools that solve the finance communities compute problems much better. Things are moving on, however. There are moves afoot in the open source community to evolve the technology to address other, more sophisticated needs such as utility and interactive computing. In this talk, I will describe how Excelian is providing Grid consultancy services for the finance community and how, through its relationship to the EGEE project, Excelian is helping to identify and exploit opportunities as the research and business worlds converge. Because of the strong third party presence in the finance industry, such opportunities are few and far between, but they are there, especially as we expand sideways into related verticals such as the smaller hedge funds and energy companies. This talk will give an overview of the barriers to adoption of gLite in the finance industry and highlight some of the opportunities offered in this and related industries as the ideas around Grid mature. Speaker Bio: Dr Adam Vile is a senior consultant and head of the Grid and HPC practice at Excelian, a consultancy that focuses on financial markets professional services. He has spent many years in investment banking, as a developer, project manager and architect in both front and back office. Before joining Excelian he was senior Grid and HPC architect at Barclays Capital. Prior to joining investment banking, Adam spent a number of years lecturing in IT and mathematics at a UK University and maintains links with academia through lectures, research and through validation and steering of postgraduate courses. He is a chartered mathematician and was the conference chair of the Institute of Mathematics and its Applications first conference in computational Finance.4. From Monte Carlo to Wall Street Daniel Egloff, Head of Financial Engineering Computing Unit, Zürich Cantonal Bank High performance computing techniques provide new means to solve computationally hard problems in the financial service industry. First I consider Monte Carlo simulation and illustrate how it can be used to implement a sophisticated credit risk management and economic capital framework. From a HPC perspective, basic Monte Carlo simulation is embarrassingly parallel and can be implemented efficiently on distributed memory clusters. Additional difficulties arise for adaptive variance reduction schemes, if the information content in a sample is very small, and if the amount of simulated date becomes huge such that incremental processing algorithms are indispensable. We discuss the business value of an advanced credit risk quantification which is particularly compelling in these days. While Monte Carlo simulation is a very versatile tool it is not always the preferred solution for the pricing of complex products like multi asset options, structured products, or credit derivatives. As a second application I show how operator methods can be used to develop a pricing framework. The scalability of operator methods relies heavily on optimized dense matrix-matrix multiplications and requires specialized BLAS level-3 implementations provided by specialized FPGA or GPU boards. Speaker Bio: Daniel Egloff studied mathematics, theoretical physics, and computer science at the University of Zurich and the ETH Zurich. He holds a PhD in Mathematics from University of Fribourg, Switzerland. After his PhD he started to work for a large Swiss insurance company in the area of asset and liability management. He continued his professional career in the consulting industry. At KPMG and Arthur Andersen he consulted international clients and implemented quantitative risk management solutions for financial institutions and insurance companies. In 2002 he joined Zurich Cantonal Bank. He was assigned to develop and implement credit portfolio risk and economic capital methodologies. He built up a competence center for high performance and cluster computing. Currently, Daniel Egloff is heading the Financial Computing unit in the ZKB Financial Engineering division. He and his team is engineering and operating high performance cluster applications for computationally intensive problems in financial risk management.

ALDH1 is an immunohistochemical diagnostic marker for solitary fibrous tumours and haemangiopericytomas of the meninges emerging from gene profiling study

PubMed Central

2013-01-01

Background Solitary Fibrous Tumours (SFT) and haemangiopericytomas (HPC) are rare meningeal tumours that have to be distinguished from meningiomas and more rarely from synovial sarcomas. We recently found that ALDH1A1 was overexpressed in SFT and HPC as compared to soft tissue sarcomas. Using whole-genome DNA microarrays, we defined the gene expression profiles of 16 SFT/HPC (9 HPC and 7 SFT). Expression profiles were compared to publicly available expression profiles of additional SFT or HPC, meningiomas and synovial sarcomas. We also performed an immunohistochemical (IHC) study with anti-ALDH1 and anti-CD34 antibodies on Tissue Micro-Arrays including 38 SFT (25 meningeal and 13 extrameningeal), 55 meningeal haemangiopericytomas (24 grade II, 31 grade III), 163 meningiomas (86 grade I, 62 grade II, 15 grade III) and 98 genetically confirmed synovial sarcomas. Results ALDH1A1 gene was overexpressed in SFT/HPC, as compared to meningiomas and synovial sarcomas. These findings were confirmed at the protein level. 84% of the SFT and 85.4% of the HPC were positive with anti-ALDH1 antibody, while only 7.1% of synovial sarcomas and 1.2% of meningiomas showed consistent expression. Positivity was usually more diffuse in SFT/HPC compared to other tumours with more than 50% of tumour cells immunostained in 32% of SFT and 50.8% of HPC. ALDH1 was a sensitive and specific marker for the diagnosis of SFT (SE = 84%, SP = 98.8%) and HPC (SE = 84.5%, SP = 98.7%) of the meninges. In association with CD34, ALDH1 expression had a specificity and positive predictive value of 100%. Conclusion We show that ALDH1, a stem cell marker, is an accurate diagnostic marker for SFT and HPC, which improves the diagnostic value of CD34. ALDH1 could also be a new therapeutic target for these tumours which are not sensitive to conventional chemotherapy. PMID:24252471

Fatigue and shear behavior of HPC bulb tee girders : LTRC technical summary report.

DOT National Transportation Integrated Search

2008-04-01

The objectives of the research were (1) to provide assurance that full size, deep prestressed concrete girders made with HPC would perform satisfactorily under flexural fatigue, static shear, and static flexural loading conditions; (2) to determine i...

Long-term monitoring of the HPC Charenton Canal Bridge.

DOT National Transportation Integrated Search

2011-08-01

The report contains long-term monitoring data collection and analysis of the first fully high : performance concrete (HPC) bridge in Louisiana, the Charenton Canal Bridge. The design of this : bridge started in 1997, and it was built and opened to tr...

CFD Ventilation Study for the Human Powered Centrifuge at the International Space Station

NASA Technical Reports Server (NTRS)

Son, Chang H.

2011-01-01

The Human Powered Centrifuge (HPC) is a hyper gravity facility that will be installed on board the International Space Station (ISS) to enable crew exercises under the artificial gravity conditions. The HPC equipment includes a bicycle for long-term exercises of a crewmember that provides power for rotation of HPC at a speed of 30 rpm. The crewmember exercising vigorously on the centrifuge generates the amount of carbon dioxide of several times higher than a crewmember in ordinary conditions. The goal of the study is to analyze the airflow and carbon dioxide distribution within Pressurized Multipurpose Module (PMM) cabin. The 3D computational model included PMM cabin. The full unsteady formulation was used for airflow and CO2 transport modeling with the so-called sliding mesh concept is considered in the rotating reference frame while the rest of the cabin volume is considered in the stationary reference frame. The localized effects of carbon dioxide dispersion are examined. Strong influence of the rotating HPC equipment on the CO2 distribution is detected and discussed.

Differential Acetylcholine Release in the Prefrontal Cortex and Hippocampus During Pavlovian Trace and Delay Conditioning

PubMed Central

Flesher, M. Melissa; Butt, Allen E.; Kinney-Hurd, Brandee L.

2011-01-01

Pavlovian trace conditioning critically depends on the medial prefrontal cortex (mPFC) and hippocampus (HPC), whereas delay conditioning does not depend on these brain structures. Given that the cholinergic basal forebrain system modulates activity in both the mPFC and HPC, it was reasoned that the level of acetylcholine (ACh) release in these regions would show distinct profiles during testing in trace and delay conditioning paradigms. To test this assumption, microdialysis probes were implanted unilaterally into the mPFC and HPC of rats that were pre-trained in appetitive trace and delay conditioning paradigms using different conditional stimuli in the two tasks. On the day of microdialysis testing, dialysate samples were collected during a quiet baseline interval before trials were initiated, and again during performance in separate blocks of trace and delay conditioning trials in each animal. ACh levels were quantified using high performance liquid chromatography and electrochemical detection techniques. Consistent with our hypothesis, results showed that ACh release in the mPFC was greater during trace conditioning than during delay conditioning. The level of ACh released during trace conditioning in the HPC was also greater than the levels observed during delay conditioning. While ACh efflux in both the mPFC and HPC selectively increased during trace conditioning, ACh levels in the mPFC during trace conditioning testing showed the greatest increases observed. These results demonstrate a dissociation in cholinergic activation of the mPFC and HPC during performance in trace but not delay appetitive conditioning, where this cholinergic activity may contribute to attentional mechanisms, adaptive response timing, or memory consolidation necessary for successful trace conditioning. PMID:21514394

DoD HPC Insights Fall 2016A publication of the Department of Defense High Performance Computing Modernization Program

DTIC Science & Technology

2016-09-01

HPCMP will continue to be a key resource in solving challenging problems for the Department of Defense . 1 Fall 2016 High-F idel i ty Simulat ions of...laser interactions. The group had studied plasma expansion experimentally, but this wasn’t sufficient to understand the problem . Feister adapted and...focused on increasing the efficiency of jet turbine engines and extending aircraft flight ranges by changing the shape (articulation) of the turbine

High-Bandwidth Tactical-Network Data Analysis in a High-Performance-Computing (HPC) Environment: Voice Call Analysis

DTIC Science & Technology

2015-09-01

Gateway 2 4. Voice Packet Flow: SIP , Session Description Protocol (SDP), and RTP 3 5. Voice Data Analysis 5 6. Call Analysis 6 7. Call Metrics 6...analysis processing is designed for a general VoIP system architecture based on Session Initiation Protocol ( SIP ) for negotiating call sessions and...employs Skinny Client Control Protocol for network communication between the phone and the local CallManager (e.g., for each dialed digit), SIP

DCDM1: Lessons Learned from the World's Most Energy Efficient Data Center

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sickinger, David E; Van Geet, Otto D; Carter, Thomas

This presentation discusses the holistic approach to design the world's most energy-efficient data center, which is located at the U.S. Department of Energy National Renewable Energy Laboratory (NREL). This high-performance computing (HPC) data center has achieved a trailing twelve-month average power usage effectiveness (PUE) of 1.04 and features a chiller-less design, component-level warm-water liquid cooling, and waste heat capture and reuse. We provide details of the demonstrated PUE and energy reuse effectiveness (ERE) and lessons learned during four years of production operation. Recent efforts to dramatically reduce the water footprint will also be discussed. Johnson Controls partnered with NREL andmore » Sandia National Laboratories to deploy a thermosyphon cooler (TSC) as a test bed at NREL's HPC data center that resulted in a 50% reduction in water usage during the first year of operation. The Thermosyphon Cooler Hybrid System (TCHS) integrates the control of a dry heat rejection device with an open cooling tower.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Morris, Karla

Although the high-performance computing (HPC) community increasingly embraces object-oriented programming (OOP), most HPC OOP projects employ the C++ programming language. Until recently, Fortran programmers interested in mining the benefits of OOP had to emulate OOP in Fortran 90/95. The advent of widespread compiler support for Fortran 2003 now facilitates explicitly constructing object-oriented class hierarchies via inheritance and leveraging related class behaviors such as dynamic polymorphism. Although C++ allows a class to inherit from multiple parent classes, Fortran and several other OOP languages restrict or prohibit explicit multiple inheritance relationships in order to circumvent several pitfalls associated with them. Nonetheless, whatmore » appears as an intrinsic feature in one language can be modeled as a user-constructed design pattern in another language. The present paper demonstrates how to apply the facade structural design pattern to support a multiple inheritance class relationship in Fortran 2003. As a result, the design unleashes the power of the associated class relationships for modeling complicated data structures yet avoids the ambiguities that plague some multiple inheritance scenarios.« less

ATLAS and LHC computing on CRAY

NASA Astrophysics Data System (ADS)

Sciacca, F. G.; Haug, S.; ATLAS Collaboration

2017-10-01

Access and exploitation of large scale computing resources, such as those offered by general purpose HPC centres, is one important measure for ATLAS and the other Large Hadron Collider experiments in order to meet the challenge posed by the full exploitation of the future data within the constraints of flat budgets. We report on the effort of moving the Swiss WLCG T2 computing, serving ATLAS, CMS and LHCb, from a dedicated cluster to the large Cray systems at the Swiss National Supercomputing Centre CSCS. These systems do not only offer very efficient hardware, cooling and highly competent operators, but also have large backfill potentials due to size and multidisciplinary usage and potential gains due to economy at scale. Technical solutions, performance, expected return and future plans are discussed.

A task-based parallelism and vectorized approach to 3D Method of Characteristics (MOC) reactor simulation for high performance computing architectures

NASA Astrophysics Data System (ADS)

Tramm, John R.; Gunow, Geoffrey; He, Tim; Smith, Kord S.; Forget, Benoit; Siegel, Andrew R.

2016-05-01

In this study we present and analyze a formulation of the 3D Method of Characteristics (MOC) technique applied to the simulation of full core nuclear reactors. Key features of the algorithm include a task-based parallelism model that allows independent MOC tracks to be assigned to threads dynamically, ensuring load balancing, and a wide vectorizable inner loop that takes advantage of modern SIMD computer architectures. The algorithm is implemented in a set of highly optimized proxy applications in order to investigate its performance characteristics on CPU, GPU, and Intel Xeon Phi architectures. Speed, power, and hardware cost efficiencies are compared. Additionally, performance bottlenecks are identified for each architecture in order to determine the prospects for continued scalability of the algorithm on next generation HPC architectures.

Hierarchically porous carbon with manganese oxides as highly efficient electrode for asymmetric supercapacitors.

PubMed

Chou, Tsu-Chin; Doong, Ruey-An; Hu, Chi-Chang; Zhang, Bingsen; Su, Dang Sheng

2014-03-01

A promising energy storage material, MnO2 /hierarchically porous carbon (HPC) nanocomposites, with exceptional electrochemical performance and ultrahigh energy density was developed for asymmetric supercapacitor applications. The microstructures of MnO2 /HPC nanocomposites were characterized by transmission electron microscopy, scanning transmission electron microscopy, and electron dispersive X-ray elemental mapping analysis. The 3-5 nm MnO2 nanocrystals at mass loadings of 7.3-10.8 wt % are homogeneously distributed onto the HPCs, and the utilization efficiency of MnO2 on specific capacitance can be enhanced to 94-96 %. By combining the ultrahigh utilization efficiency of MnO2 and the conductive and ion-transport advantages of HPCs, MnO2 /HPC electrodes can achieve higher specific capacitance values (196 F g(-1) ) than those of pure carbon electrodes (60.8 F g(-1) ), and maintain their superior rate capability in neutral electrolyte solutions. The asymmetric supercapacitor consisting of a MnO2 /HPC cathode and a HPC anode shows an excellent performance with energy and power densities of 15.3 Wh kg(-1) and 19.8 kW kg(-1) , respectively, at a cell voltage of 2 V. Results obtained herein demonstrate the excellence of MnO2 /HPC nanocomposites as energy storage material and open an avenue to fabricate the next generation supercapacitors with both high power and energy densities. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Anterior hippocampal dysconnectivity in posttraumatic stress disorder: a dimensional and multimodal approach.

PubMed

Abdallah, C G; Wrocklage, K M; Averill, C L; Akiki, T; Schweinsburg, B; Roy, A; Martini, B; Southwick, S M; Krystal, J H; Scott, J C

2017-02-28

The anterior hippocampus (aHPC) has a central role in the regulation of anxiety-related behavior, stress response, emotional memory and fear. However, little is known about the presence and extent of aHPC abnormalities in posttraumatic stress disorder (PTSD). In this study, we used a multimodal approach, along with graph-based measures of global brain connectivity (GBC) termed functional GBC with global signal regression (f-GBCr) and diffusion GBC (d-GBC), in combat-exposed US Veterans with and without PTSD. Seed-based aHPC anatomical connectivity analyses were also performed. A whole-brain voxel-wise data-driven investigation revealed a significant association between elevated PTSD symptoms and reduced medial temporal f-GBCr, particularly in the aHPC. Similarly, aHPC d-GBC negatively correlated with PTSD severity. Both functional and anatomical aHPC dysconnectivity measures remained significant after controlling for hippocampal volume, age, gender, intelligence, education, combat severity, depression, anxiety, medication status, traumatic brain injury and alcohol/substance comorbidities. Depression-like PTSD dimensions were associated with reduced connectivity in the ventromedial and dorsolateral prefrontal cortex. In contrast, hyperarousal symptoms were positively correlated with ventromedial and dorsolateral prefrontal connectivity. We believe the findings provide first evidence of functional and anatomical dysconnectivity in the aHPC of veterans with high PTSD symptomatology. The data support the putative utility of aHPC connectivity as a measure of overall PTSD severity. Moreover, prefrontal global connectivity may be of clinical value as a brain biomarker to potentially distinguish between PTSD subgroups.

A Big Data Approach to Analyzing Market Volatility

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, Kesheng; Bethel, E. Wes; Gu, Ming

2013-06-05

Understanding the microstructure of the financial market requires the processing of a vast amount of data related to individual trades, and sometimes even multiple levels of quotes. Analyzing such a large volume of data requires tremendous computing power that is not easily available to financial academics and regulators. Fortunately, public funded High Performance Computing (HPC) power is widely available at the National Laboratories in the US. In this paper we demonstrate that the HPC resource and the techniques for data-intensive sciences can be used to greatly accelerate the computation of an early warning indicator called Volume-synchronized Probability of Informed tradingmore » (VPIN). The test data used in this study contains five and a half year's worth of trading data for about 100 most liquid futures contracts, includes about 3 billion trades, and takes 140GB as text files. By using (1) a more efficient file format for storing the trading records, (2) more effective data structures and algorithms, and (3) parallelizing the computations, we are able to explore 16,000 different ways of computing VPIN in less than 20 hours on a 32-core IBM DataPlex machine. Our test demonstrates that a modest computer is sufficient to monitor a vast number of trading activities in real-time – an ability that could be valuable to regulators. Our test results also confirm that VPIN is a strong predictor of liquidity-induced volatility. With appropriate parameter choices, the false positive rates are about 7% averaged over all the futures contracts in the test data set. More specifically, when VPIN values rise above a threshold (CDF > 0.99), the volatility in the subsequent time windows is higher than the average in 93% of the cases.« less

Optimizing CyberShake Seismic Hazard Workflows for Large HPC Resources

NASA Astrophysics Data System (ADS)

Callaghan, S.; Maechling, P. J.; Juve, G.; Vahi, K.; Deelman, E.; Jordan, T. H.

2014-12-01

The CyberShake computational platform is a well-integrated collection of scientific software and middleware that calculates 3D simulation-based probabilistic seismic hazard curves and hazard maps for the Los Angeles region. Currently each CyberShake model comprises about 235 million synthetic seismograms from about 415,000 rupture variations computed at 286 sites. CyberShake integrates large-scale parallel and high-throughput serial seismological research codes into a processing framework in which early stages produce files used as inputs by later stages. Scientific workflow tools are used to manage the jobs, data, and metadata. The Southern California Earthquake Center (SCEC) developed the CyberShake platform using USC High Performance Computing and Communications systems and open-science NSF resources.CyberShake calculations were migrated to the NSF Track 1 system NCSA Blue Waters when it became operational in 2013, via an interdisciplinary team approach including domain scientists, computer scientists, and middleware developers. Due to the excellent performance of Blue Waters and CyberShake software optimizations, we reduced the makespan (a measure of wallclock time-to-solution) of a CyberShake study from 1467 to 342 hours. We will describe the technical enhancements behind this improvement, including judicious introduction of new GPU software, improved scientific software components, increased workflow-based automation, and Blue Waters-specific workflow optimizations.Our CyberShake performance improvements highlight the benefits of scientific workflow tools. The CyberShake workflow software stack includes the Pegasus Workflow Management System (Pegasus-WMS, which includes Condor DAGMan), HTCondor, and Globus GRAM, with Pegasus-mpi-cluster managing the high-throughput tasks on the HPC resources. The workflow tools handle data management, automatically transferring about 13 TB back to SCEC storage.We will present performance metrics from the most recent CyberShake study, executed on Blue Waters. We will compare the performance of CPU and GPU versions of our large-scale parallel wave propagation code, AWP-ODC-SGT. Finally, we will discuss how these enhancements have enabled SCEC to move forward with plans to increase the CyberShake simulation frequency to 1.0 Hz.

Exploring Infiniband Hardware Virtualization in OpenNebula towards Efficient High-Performance Computing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pais Pitta de Lacerda Ruivo, Tiago; Bernabeu Altayo, Gerard; Garzoglio, Gabriele

2014-11-11

has been widely accepted that software virtualization has a big negative impact on high-performance computing (HPC) application performance. This work explores the potential use of Infiniband hardware virtualization in an OpenNebula cloud towards the efficient support of MPI-based workloads. We have implemented, deployed, and tested an Infiniband network on the FermiCloud private Infrastructure-as-a-Service (IaaS) cloud. To avoid software virtualization towards minimizing the virtualization overhead, we employed a technique called Single Root Input/Output Virtualization (SRIOV). Our solution spanned modifications to the Linux’s Hypervisor as well as the OpenNebula manager. We evaluated the performance of the hardware virtualization on up to 56more » virtual machines connected by up to 8 DDR Infiniband network links, with micro-benchmarks (latency and bandwidth) as well as w a MPI-intensive application (the HPL Linpack benchmark).« less

Evaluating the Efficacy of the Cloud for Cluster Computation

NASA Technical Reports Server (NTRS)

Knight, David; Shams, Khawaja; Chang, George; Soderstrom, Tom

2012-01-01

Computing requirements vary by industry, and it follows that NASA and other research organizations have computing demands that fall outside the mainstream. While cloud computing made rapid inroads for tasks such as powering web applications, performance issues on highly distributed tasks hindered early adoption for scientific computation. One venture to address this problem is Nebula, NASA's homegrown cloud project tasked with delivering science-quality cloud computing resources. However, another industry development is Amazon's high-performance computing (HPC) instances on Elastic Cloud Compute (EC2) that promises improved performance for cluster computation. This paper presents results from a series of benchmarks run on Amazon EC2 and discusses the efficacy of current commercial cloud technology for running scientific applications across a cluster. In particular, a 240-core cluster of cloud instances achieved 2 TFLOPS on High-Performance Linpack (HPL) at 70% of theoretical computational performance. The cluster's local network also demonstrated sub-100 ?s inter-process latency with sustained inter-node throughput in excess of 8 Gbps. Beyond HPL, a real-world Hadoop image processing task from NASA's Lunar Mapping and Modeling Project (LMMP) was run on a 29 instance cluster to process lunar and Martian surface images with sizes on the order of tens of gigapixels. These results demonstrate that while not a rival of dedicated supercomputing clusters, commercial cloud technology is now a feasible option for moderately demanding scientific workloads.

Characterizing output bottlenecks in a supercomputer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xie, Bing; Chase, Jeffrey; Dillow, David A

2012-01-01

Supercomputer I/O loads are often dominated by writes. HPC (High Performance Computing) file systems are designed to absorb these bursty outputs at high bandwidth through massive parallelism. However, the delivered write bandwidth often falls well below the peak. This paper characterizes the data absorption behavior of a center-wide shared Lustre parallel file system on the Jaguar supercomputer. We use a statistical methodology to address the challenges of accurately measuring a shared machine under production load and to obtain the distribution of bandwidth across samples of compute nodes, storage targets, and time intervals. We observe and quantify limitations from competing traffic,more » contention on storage servers and I/O routers, concurrency limitations in the client compute node operating systems, and the impact of variance (stragglers) on coupled output such as striping. We then examine the implications of our results for application performance and the design of I/O middleware systems on shared supercomputers.« less

On the energy footprint of I/O management in Exascale HPC systems

DOE PAGES

Dorier, Matthieu; Yildiz, Orcun; Ibrahim, Shadi; ...

2016-03-21

The advent of unprecedentedly scalable yet energy hungry Exascale supercomputers poses a major challenge in sustaining a high performance-per-watt ratio. With I/O management acquiring a crucial role in supporting scientific simulations, various I/O management approaches have been proposed to achieve high performance and scalability. But, the details of how these approaches affect energy consumption have not been studied yet. Therefore, this paper aims to explore how much energy a supercomputer consumes while running scientific simulations when adopting various I/O management approaches. In particular, we closely examine three radically different I/O schemes including time partitioning, dedicated cores, and dedicated nodes. Tomore » accomplish this, we implement the three approaches within the Damaris I/O middleware and perform extensive experiments with one of the target HPC applications of the Blue Waters sustained-petaflop supercomputer project: the CM1 atmospheric model. Our experimental results obtained on the French Grid'5000 platform highlight the differences among these three approaches and illustrate in which way various configurations of the application and of the system can impact performance and energy consumption. Moreover, we propose and validate a mathematical model that estimates the energy consumption of a HPC simulation under different I/O approaches. This proposed model gives hints to pre-select the most energy-efficient I/O approach for a particular simulation on a particular HPC system and therefore provides a step towards energy-efficient HPC simulations in Exascale systems. To the best of our knowledge, our work provides the first in-depth look into the energy-performance tradeoffs of I/O management approaches.« less

Synergistic effect of Nitrogen-doped hierarchical porous carbon/graphene with enhanced catalytic performance for oxygen reduction reaction

NASA Astrophysics Data System (ADS)

Kong, Dewang; Yuan, Wenjing; Li, Cun; Song, Jiming; Xie, Anjian; Shen, Yuhua

2017-01-01

Developing efficient and economical catalysts for the oxygen reduction reaction (ORR) is important to promote the commercialization of fuel cells. Here, we report a simple and environmentally friendly method to prepare nitrogen (N) -doped hierarchical porous carbon (HPC)/reduced graphene oxide (RGO) composites by reusing waste biomass (pomelo peel) coupled with graphene oxide (GO). This method is green, low-cost and without using any acid or alkali activator. The typical sample (N-HPC/RGO-1) contains 5.96 at.% nitrogen and larger BET surface area (1194 m2/g). Electrochemical measurements show that N-HPC/RGO-1 exhibits not only a relatively positive onset potential and high current density, but also considerable methanol tolerance and long-term durability in alkaline media as well as in acidic media. The electron transfer number is close to 4, which means that it is mostly via a four-electron pathway toward ORR. The excellent catalytic performance of N-HPC/RGO-1 is due to the synergistic effect of the inherent interwoven network structure of HPC, the good electrical conductivity of RGO, and the heteroatom doping for the composite. More importantly, this work demonstrates a good example for turning discarded rubbish into valuable functional products and addresses the disposal issue of waste biomass simultaneously for environment clean.

The role of dedicated data computing centers in the age of cloud computing

NASA Astrophysics Data System (ADS)

Caramarcu, Costin; Hollowell, Christopher; Strecker-Kellogg, William; Wong, Antonio; Zaytsev, Alexandr

2017-10-01

Brookhaven National Laboratory (BNL) anticipates significant growth in scientific programs with large computing and data storage needs in the near future and has recently reorganized support for scientific computing to meet these needs. A key component is the enhanced role of the RHIC-ATLAS Computing Facility (RACF) in support of high-throughput and high-performance computing (HTC and HPC) at BNL. This presentation discusses the evolving role of the RACF at BNL, in light of its growing portfolio of responsibilities and its increasing integration with cloud (academic and for-profit) computing activities. We also discuss BNL’s plan to build a new computing center to support the new responsibilities of the RACF and present a summary of the cost benefit analysis done, including the types of computing activities that benefit most from a local data center vs. cloud computing. This analysis is partly based on an updated cost comparison of Amazon EC2 computing services and the RACF, which was originally conducted in 2012.

Effect of rice husk ash and fly ash on the compressive strength of high performance concrete

NASA Astrophysics Data System (ADS)

Van Lam, Tang; Bulgakov, Boris; Aleksandrova, Olga; Larsen, Oksana; Anh, Pham Ngoc

2018-03-01

The usage of industrial and agricultural wastes for building materials production plays an important role to improve the environment and economy by preserving nature materials and land resources, reducing land, water and air pollution as well as organizing and storing waste costs. This study mainly focuses on mathematical modeling dependence of the compressive strength of high performance concrete (HPC) at the ages of 3, 7 and 28 days on the amount of rice husk ash (RHA) and fly ash (FA), which are added to the concrete mixtures by using the Central composite rotatable design. The result of this study provides the second-order regression equation of objective function, the images of the surface expression and the corresponding contours of the objective function of the regression equation, as the optimal points of HPC compressive strength. These objective functions, which are the compressive strength values of HPC at the ages of 3, 7 and 28 days, depend on two input variables as: x1 (amount of RHA) and x2 (amount of FA). The Maple 13 program, solving the second-order regression equation, determines the optimum composition of the concrete mixture for obtaining high performance concrete and calculates the maximum value of the HPC compressive strength at the ages of 28 days. The results containMaxR28HPC = 76.716 MPa when RHA = 0.1251 and FA = 0.3119 by mass of Portland cement.

Humic acids-based hierarchical porous carbons as high-rate performance electrodes for symmetric supercapacitors.

PubMed

Qiao, Zhi-jun; Chen, Ming-ming; Wang, Cheng-yang; Yuan, Yun-cai

2014-07-01

Two kinds of hierarchical porous carbons (HPCs) with specific surface areas of 2000 m(2)g(-1) were synthesized using leonardite humic acids (LHA) or biotechnology humic acids (BHA) precursors via a KOH activation process. Humic acids have a high content of oxygen-containing groups which enabled them to dissolve in aqueous KOH and facilitated the homogeneous KOH activation. The LHA-based HPC is made up of abundant micro-, meso-, and macropores and in 6M KOH it has a specific capacitance of 178 F g(-1) at 100 Ag(-1) and its capacitance retention on going from 0.05 to 100 A g(-1) is 64%. In contrast, the BHA-based HPC exhibits a lower capacitance retention of 54% and a specific capacitance of 157 F g(-1) at 100 A g(-1) which is due to the excessive micropores in the BHA-HPC. Moreover, LHA-HPC is produced in a higher yield than BHA-HPC (51 vs. 17 wt%). Copyright © 2014 Elsevier Ltd. All rights reserved.

Message passing interface and multithreading hybrid for parallel molecular docking of large databases on petascale high performance computing machines.

PubMed

Zhang, Xiaohua; Wong, Sergio E; Lightstone, Felice C

2013-04-30

A mixed parallel scheme that combines message passing interface (MPI) and multithreading was implemented in the AutoDock Vina molecular docking program. The resulting program, named VinaLC, was tested on the petascale high performance computing (HPC) machines at Lawrence Livermore National Laboratory. To exploit the typical cluster-type supercomputers, thousands of docking calculations were dispatched by the master process to run simultaneously on thousands of slave processes, where each docking calculation takes one slave process on one node, and within the node each docking calculation runs via multithreading on multiple CPU cores and shared memory. Input and output of the program and the data handling within the program were carefully designed to deal with large databases and ultimately achieve HPC on a large number of CPU cores. Parallel performance analysis of the VinaLC program shows that the code scales up to more than 15K CPUs with a very low overhead cost of 3.94%. One million flexible compound docking calculations took only 1.4 h to finish on about 15K CPUs. The docking accuracy of VinaLC has been validated against the DUD data set by the re-docking of X-ray ligands and an enrichment study, 64.4% of the top scoring poses have RMSD values under 2.0 Å. The program has been demonstrated to have good enrichment performance on 70% of the targets in the DUD data set. An analysis of the enrichment factors calculated at various percentages of the screening database indicates VinaLC has very good early recovery of actives. Copyright © 2013 Wiley Periodicals, Inc.

An Interface for Biomedical Big Data Processing on the Tianhe-2 Supercomputer.

PubMed

Yang, Xi; Wu, Chengkun; Lu, Kai; Fang, Lin; Zhang, Yong; Li, Shengkang; Guo, Guixin; Du, YunFei

2017-12-01

Big data, cloud computing, and high-performance computing (HPC) are at the verge of convergence. Cloud computing is already playing an active part in big data processing with the help of big data frameworks like Hadoop and Spark. The recent upsurge of high-performance computing in China provides extra possibilities and capacity to address the challenges associated with big data. In this paper, we propose Orion-a big data interface on the Tianhe-2 supercomputer-to enable big data applications to run on Tianhe-2 via a single command or a shell script. Orion supports multiple users, and each user can launch multiple tasks. It minimizes the effort needed to initiate big data applications on the Tianhe-2 supercomputer via automated configuration. Orion follows the "allocate-when-needed" paradigm, and it avoids the idle occupation of computational resources. We tested the utility and performance of Orion using a big genomic dataset and achieved a satisfactory performance on Tianhe-2 with very few modifications to existing applications that were implemented in Hadoop/Spark. In summary, Orion provides a practical and economical interface for big data processing on Tianhe-2.

Costa - Introduction to 2015 Annual Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Costa, James E.

In parallel with Sandia National Laboratories having two major locations (NM and CA), along with a number of smaller facilities across the nation, so too is the distribution of scientific, engineering and computing resources. As a part of Sandia’s Institutional Computing Program, CA site-based Sandia computer scientists and engineers have been providing mission and research staff with local CA resident expertise on computing options while also focusing on two growing high performance computing research problems. The first is how to increase system resilience to failure, as machines grow larger, more complex and heterogeneous. The second is how to ensure thatmore » computer hardware and configurations are optimized for specialized data analytical mission needs within the overall Sandia computing environment, including the HPC subenvironment. All of these activities support the larger Sandia effort in accelerating development and integration of high performance computing into national security missions. Sandia continues to both promote national R&D objectives, including the recent Presidential Executive Order establishing the National Strategic Computing Initiative and work to ensure that the full range of computing services and capabilities are available for all mission responsibilities, from national security to energy to homeland defense.« less

Optimisation of multiplet identifier processing on a PLAYSTATION® 3

NASA Astrophysics Data System (ADS)

Hattori, Masami; Mizuno, Takashi

2010-02-01

To enable high-performance computing (HPC) for applications with large datasets using a Sony® PLAYSTATION® 3 (PS3™) video game console, we configured a hybrid system consisting of a Windows® PC and a PS3™. To validate this system, we implemented the real-time multiplet identifier (RTMI) application, which identifies multiplets of microearthquakes in terms of the similarity of their waveforms. The cross-correlation computation, which is a core algorithm of the RTMI application, was optimised for the PS3™ platform, while the rest of the computation, including data input and output remained on the PC. With this configuration, the core part of the algorithm ran 69 times faster than the original program, accelerating total computation speed more than five times. As a result, the system processed up to 2100 total microseismic events, whereas the original implementation had a limit of 400 events. These results indicate that this system enables high-performance computing for large datasets using the PS3™, as long as data transfer time is negligible compared with computation time.

Accelerating activity coefficient calculations using multicore platforms, and profiling the energy use resulting from such calculations.

NASA Astrophysics Data System (ADS)

Topping, David; Alibay, Irfan; Bane, Michael

2017-04-01

To predict the evolving concentration, chemical composition and ability of aerosol particles to act as cloud droplets, we rely on numerical modeling. Mechanistic models attempt to account for the movement of compounds between the gaseous and condensed phases at a molecular level. This 'bottom up' approach is designed to increase our fundamental understanding. However, such models rely on predicting the properties of molecules and subsequent mixtures. For partitioning between the gaseous and condensed phases this includes: saturation vapour pressures; Henrys law coefficients; activity coefficients; diffusion coefficients and reaction rates. Current gas phase chemical mechanisms predict the existence of potentially millions of individual species. Within a dynamic ensemble model, this can often be used as justification for neglecting computationally expensive process descriptions. Indeed, on whether we can quantify the true sensitivity to uncertainties in molecular properties, even at the single aerosol particle level it has been impossible to embed fully coupled representations of process level knowledge with all possible compounds, typically relying on heavily parameterised descriptions. Relying on emerging numerical frameworks, and designed for the changing landscape of high-performance computing (HPC), in this study we focus specifically on the ability to capture activity coefficients in liquid solutions using the UNIFAC method. Activity coefficients are often neglected with the largely untested hypothesis that they are simply too computationally expensive to include in dynamic frameworks. We present results demonstrating increased computational efficiency for a range of typical scenarios, including a profiling of the energy use resulting from reliance on such computations. As the landscape of HPC changes, the latter aspect is important to consider in future applications.

INFN-Pisa scientific computation environment (GRID, HPC and Interactive Analysis)

NASA Astrophysics Data System (ADS)

Arezzini, S.; Carboni, A.; Caruso, G.; Ciampa, A.; Coscetti, S.; Mazzoni, E.; Piras, S.

2014-06-01

The INFN-Pisa Tier2 infrastructure is described, optimized not only for GRID CPU and Storage access, but also for a more interactive use of the resources in order to provide good solutions for the final data analysis step. The Data Center, equipped with about 6700 production cores, permits the use of modern analysis techniques realized via advanced statistical tools (like RooFit and RooStat) implemented in multicore systems. In particular a POSIX file storage access integrated with standard SRM access is provided. Therefore the unified storage infrastructure is described, based on GPFS and Xrootd, used both for SRM data repository and interactive POSIX access. Such a common infrastructure allows a transparent access to the Tier2 data to the users for their interactive analysis. The organization of a specialized many cores CPU facility devoted to interactive analysis is also described along with the login mechanism integrated with the INFN-AAI (National INFN Infrastructure) to extend the site access and use to a geographical distributed community. Such infrastructure is used also for a national computing facility in use to the INFN theoretical community, it enables a synergic use of computing and storage resources. Our Center initially developed for the HEP community is now growing and includes also HPC resources fully integrated. In recent years has been installed and managed a cluster facility (1000 cores, parallel use via InfiniBand connection) and we are now updating this facility that will provide resources for all the intermediate level HPC computing needs of the INFN theoretical national community.

GEOS. User Tutorials

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fu, Pengchen; Settgast, Randolph R.; Johnson, Scott M.

2014-12-17

GEOS is a massively parallel, multi-physics simulation application utilizing high performance computing (HPC) to address subsurface reservoir stimulation activities with the goal of optimizing current operations and evaluating innovative stimulation methods. GEOS enables coupling of di erent solvers associated with the various physical processes occurring during reservoir stimulation in unique and sophisticated ways, adapted to various geologic settings, materials and stimulation methods. Developed at the Lawrence Livermore National Laboratory (LLNL) as a part of a Laboratory-Directed Research and Development (LDRD) Strategic Initiative (SI) project, GEOS represents the culmination of a multi-year ongoing code development and improvement e ort that hasmore » leveraged existing code capabilities and sta expertise to design new computational geosciences software.« less

Open-Source, Distributed Computational Environment for Virtual Materials Exploration

DTIC Science & Technology

2015-01-01

compromising structural integrity.  For  example, advanced designs could specify advanced materials processing techniques such as heat  treatments  in specific...orchestration of execution of multiple standalone codes at varying  length scales will need advanced  high ‐performance computing (HPC) integration in...possible hooks that could be used to  coordinate larger  workflows spanning tools developed by different groups.    The  high  level approach explored

Dynamic updating of hippocampal object representations reflects new conceptual knowledge

PubMed Central

Mack, Michael L.; Love, Bradley C.; Preston, Alison R.

2016-01-01

Concepts organize the relationship among individual stimuli or events by highlighting shared features. Often, new goals require updating conceptual knowledge to reflect relationships based on different goal-relevant features. Here, our aim is to determine how hippocampal (HPC) object representations are organized and updated to reflect changing conceptual knowledge. Participants learned two classification tasks in which successful learning required attention to different stimulus features, thus providing a means to index how representations of individual stimuli are reorganized according to changing task goals. We used a computational learning model to capture how people attended to goal-relevant features and organized object representations based on those features during learning. Using representational similarity analyses of functional magnetic resonance imaging data, we demonstrate that neural representations in left anterior HPC correspond with model predictions of concept organization. Moreover, we show that during early learning, when concept updating is most consequential, HPC is functionally coupled with prefrontal regions. Based on these findings, we propose that when task goals change, object representations in HPC can be organized in new ways, resulting in updated concepts that highlight the features most critical to the new goal. PMID:27803320

Scaling GDL for Multi-cores to Process Planck HFI Beams Monte Carlo on HPC

NASA Astrophysics Data System (ADS)

Coulais, A.; Schellens, M.; Duvert, G.; Park, J.; Arabas, S.; Erard, S.; Roudier, G.; Hivon, E.; Mottet, S.; Laurent, B.; Pinter, M.; Kasradze, N.; Ayad, M.

2014-05-01

After reviewing the majors progress done in GDL -now in 0.9.4- on performance and plotting capabilities since ADASS XXI paper (Coulais et al. 2012), we detail how a large code for Planck HFI beams Monte Carlo was successfully transposed from IDL to GDL on HPC.

CI-WATER HPC Model: Cyberinfrastructure to Advance High Performance Water Resources Modeling in the Intermountain Western U.S

NASA Astrophysics Data System (ADS)

Ogden, F. L.; Lai, W.; Douglas, C. C.; Miller, S. N.; Zhang, Y.

2012-12-01

The CI-WATER project is a cooperative effort between the Utah and Wyoming EPSCoR jurisdictions, and is funded through a cooperative agreement with the U.S. National Science Foundation EPSCoR. The CI-WATER project is acquiring hardware and developing software cyberinfrastructure (CI) to enhance accessibility of High Performance Computing for water resources modeling in the Western U.S. One of the components of the project is development of a large-scale, high-resolution, physically-based, data-driven, integrated computational water resources model, which we call the CI-WATER HPC model. The objective of this model development is to enable evaluation of integrated system behavior to guide and support water system planning and management by individual users, cities, or states. The model is first being tested in the Green River basin of Wyoming, which is the largest tributary to the Colorado River. The model will ultimately be applied to simulate the entire Upper Colorado River basin for hydrological studies, watershed management, economic analysis, as well as evaluation of potential changes in environmental policy and law, population, land use, and climate. In addition to hydrologically important processes simulated in many hydrological models, the CI-WATER HPC model will emphasize anthropogenic influences such as land use change, water resources infrastructure, irrigation practices, trans-basin diversions, and urban/suburban development. The model operates on an unstructured mesh, employing adaptive mesh at grid sizes as small as 10 m as needed- particularly in high elevation snow melt regions. Data for the model are derived from remote sensing sources, atmospheric models and geophysical techniques. Monte-Carlo techniques and ensemble Kalman filtering methodologies are employed for data assimilation. The model includes application programming interface (API) standards to allow easy substitution of alternative process-level simulation routines, and provide post-processing, visualization, and communication of massive amounts of output. The open-source CI-WATER model represents a significant advance in water resources modeling, and will be useful to water managers, planners, resource economists, and the hydrologic research community in general.

Fine-needle aspiration cytology of hemangiopericytoma: A report of five cases.

PubMed

Chhieng, D; Cohen, J M; Waisman, J; Fernandez, G; Cangiarella, J

1999-08-25

Hemangiopericytoma (HPC) is a relatively rare neoplasm, accounting for approximately 2.5% of all soft tissue tumors. Its histopathology has been well documented but to the authors' knowledge reports regarding its fine-needle aspiration (FNA) cytology rarely are encountered. In the current study the authors report the cytologic findings in FNA specimens from nine confirmed cases of HPC and attempt to correlate the cytologic features with the biologic outcomes. FNA was performed with or without radiologic guidance. Corresponding sections of tissue were reviewed in conjunction with the cytologic preparations. Nine FNAs were performed in 5 patients (3 men and 2 women) with an age range of 38-77 years (mean, 56 years). Two lesions were primary soft tissue lesions arising in the lower extremities; seven were recurrent or metastatic lesions from bone (one lesion), kidney (one lesion), pelvic fossa (one lesion), lower extremities (two lesions), trunk (one lesion), and breast (one lesion). All aspirates were cellular and were comprised of single and tightly packed clusters of oval to spindle-shaped cells aggregated around branched capillaries. Basement membrane material was observed in 6 cases (67%). The nuclei were uniform and oval, with finely granular chromatin and inconspicuous nucleoli in all cases except one. No mitotic figures or areas of necrosis were identified. A correct diagnosis of HPC was made on one primary lesion and all recurrent or metastatic lesions. HPCs show a spindle cell pattern in cytologic preparations and must be distinguished from more common spindle cell lesions. The presence of branched capillaries and abundant basement membrane material supports a diagnosis of HPC. Immunohistochemistry and electron microscopy performed on FNA samples may be helpful in the differential diagnosis. FNA is a useful and accurate tool with which to confirm recurrent or metastatic HPC; however, prediction of the biologic behavior of HPC based on cytologic features is not feasible. Cancer (Cancer Cytopathol) Copyright 1999 American Cancer Society.

LAPACKrc: Fast linear algebra kernels/solvers for FPGA accelerators

NASA Astrophysics Data System (ADS)

Gonzalez, Juan; Núñez, Rafael C.

2009-07-01

We present LAPACKrc, a family of FPGA-based linear algebra solvers able to achieve more than 100x speedup per commodity processor on certain problems. LAPACKrc subsumes some of the LAPACK and ScaLAPACK functionalities, and it also incorporates sparse direct and iterative matrix solvers. Current LAPACKrc prototypes demonstrate between 40x-150x speedup compared against top-of-the-line hardware/software systems. A technology roadmap is in place to validate current performance of LAPACKrc in HPC applications, and to increase the computational throughput by factors of hundreds within the next few years.

The Effect of Apatinib on the Metabolism of Carvedilol Both in vitro and in vivo.

PubMed

Lin, Dan; Wang, Zhe; Li, Junwei; Wang, Li; Wang, Shuanghu; Hu, Guo-Xin; Liu, Xinshe

2016-01-01

In light of the growing number of cancer survivors, the incidence of cardiovascular complications in these patients had also increased, while the effect of apatinib on the pharmacokinetic of cardioprotective drug (carvedilol) in rats or human is still unknown. The present work was to study the impact of apatinib on the metabolism of carvedilol both in vitro and vivo. A specific and sensitive ultra-performance liquid-chromatography tandem mass spectrometry method was applied to determine the concentration of carvedilol and its metabolites (4'-hydroxyphenyl carvedilol [4'-HPC], 5'-hydroxyphenyl carvedilol [5'-HPC] and o-desmethyl carvedilol [o-DMC]). The inhibition ratios in human liver microsomes were 10.28, 10.89 and 5.94% for 4'-HPC, 5'-HPC and o-DMC, respectively, while in rat liver microsomes, they were 3.22, 1.58 and 1.81%, respectively. The data in vitro of rat microsomes were consistent with the data in vivo that the inhibition of 4'-HPC and 5'-HPC formation was higher than the control group. Our study showed that apatinib could significantly inhibit the formation of carvedilol metabolites both in human and rat liver microsomes. It is recommended that the effect of apatinib on the metabolism of carvedilol should be noted and carvedilol plasma concentration should be monitored. © 2015 S. Karger AG, Basel.

A harmonic polynomial cell (HPC) method for 3D Laplace equation with application in marine hydrodynamics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shao, Yan-Lin, E-mail: yanlin.shao@dnvgl.com; Faltinsen, Odd M.

2014-10-01

We propose a new efficient and accurate numerical method based on harmonic polynomials to solve boundary value problems governed by 3D Laplace equation. The computational domain is discretized by overlapping cells. Within each cell, the velocity potential is represented by the linear superposition of a complete set of harmonic polynomials, which are the elementary solutions of Laplace equation. By its definition, the method is named as Harmonic Polynomial Cell (HPC) method. The characteristics of the accuracy and efficiency of the HPC method are demonstrated by studying analytical cases. Comparisons will be made with some other existing boundary element based methods,more » e.g. Quadratic Boundary Element Method (QBEM) and the Fast Multipole Accelerated QBEM (FMA-QBEM) and a fourth order Finite Difference Method (FDM). To demonstrate the applications of the method, it is applied to some studies relevant for marine hydrodynamics. Sloshing in 3D rectangular tanks, a fully-nonlinear numerical wave tank, fully-nonlinear wave focusing on a semi-circular shoal, and the nonlinear wave diffraction of a bottom-mounted cylinder in regular waves are studied. The comparisons with the experimental results and other numerical results are all in satisfactory agreement, indicating that the present HPC method is a promising method in solving potential-flow problems. The underlying procedure of the HPC method could also be useful in other fields than marine hydrodynamics involved with solving Laplace equation.« less

GW Calculations of Materials on the Intel Xeon-Phi Architecture

NASA Astrophysics Data System (ADS)

Deslippe, Jack; da Jornada, Felipe H.; Vigil-Fowler, Derek; Biller, Ariel; Chelikowsky, James R.; Louie, Steven G.

Intel Xeon-Phi processors are expected to power a large number of High-Performance Computing (HPC) systems around the United States and the world in the near future. We evaluate the ability of GW and pre-requisite Density Functional Theory (DFT) calculations for materials on utilizing the Xeon-Phi architecture. We describe the optimization process and performance improvements achieved. We find that the GW method, like other higher level Many-Body methods beyond standard local/semilocal approximations to Kohn-Sham DFT, is particularly well suited for many-core architectures due to the ability to exploit a large amount of parallelism over plane-waves, band-pairs and frequencies. Support provided by the SCIDAC program, Department of Energy, Office of Science, Advanced Scientic Computing Research and Basic Energy Sciences. Grant Numbers DE-SC0008877 (Austin) and DE-AC02-05CH11231 (LBNL).

The VERCE Science Gateway: enabling user friendly seismic waves simulations across European HPC infrastructures

NASA Astrophysics Data System (ADS)

Spinuso, Alessandro; Krause, Amy; Ramos Garcia, Clàudia; Casarotti, Emanuele; Magnoni, Federica; Klampanos, Iraklis A.; Frobert, Laurent; Krischer, Lion; Trani, Luca; David, Mario; Leong, Siew Hoon; Muraleedharan, Visakh

2014-05-01

The EU-funded project VERCE (Virtual Earthquake and seismology Research Community in Europe) aims to deploy technologies which satisfy the HPC and data-intensive requirements of modern seismology. As a result of VERCE's official collaboration with the EU project SCI-BUS, access to computational resources, like local clusters and international infrastructures (EGI and PRACE), is made homogeneous and integrated within a dedicated science gateway based on the gUSE framework. In this presentation we give a detailed overview on the progress achieved with the developments of the VERCE Science Gateway, according to a use-case driven implementation strategy. More specifically, we show how the computational technologies and data services have been integrated within a tool for Seismic Forward Modelling, whose objective is to offer the possibility to perform simulations of seismic waves as a service to the seismological community. We will introduce the interactive components of the OGC map based web interface and how it supports the user with setting up the simulation. We will go through the selection of input data, which are either fetched from federated seismological web services, adopting community standards, or provided by the users themselves by accessing their own document data store. The HPC scientific codes can be selected from a number of waveform simulators, currently available to the seismological community as batch tools or with limited configuration capabilities in their interactive online versions. The results will be staged out from the HPC via a secure GridFTP transfer to a VERCE data layer managed by iRODS. The provenance information of the simulation will be automatically cataloged by the data layer via NoSQL techonologies. We will try to demonstrate how data access, validation and visualisation can be supported by a general purpose provenance framework which, besides common provenance concepts imported from the OPM and the W3C-PROV initiatives, also offers an extensible metadata archive including community and user defined metadata and annotations. Finally, we will show how the VERCE Gateway platform will allow the customisation of pre and post processing phases of the simulation workflows, thanks to the availability of a registry of processing elements (PEs,) which are easily developed and maintained by the seismologists.

2009 fault tolerance for extreme-scale computing workshop, Albuquerque, NM - March 19-20, 2009.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Katz, D. S.; Daly, J.; DeBardeleben, N.

2009-02-01

This is a report on the third in a series of petascale workshops co-sponsored by Blue Waters and TeraGrid to address challenges and opportunities for making effective use of emerging extreme-scale computing. This workshop was held to discuss fault tolerance on large systems for running large, possibly long-running applications. The main point of the workshop was to have systems people, middleware people (including fault-tolerance experts), and applications people talk about the issues and figure out what needs to be done, mostly at the middleware and application levels, to run such applications on the emerging petascale systems, without having faults causemore » large numbers of application failures. The workshop found that there is considerable interest in fault tolerance, resilience, and reliability of high-performance computing (HPC) systems in general, at all levels of HPC. The only way to recover from faults is through the use of some redundancy, either in space or in time. Redundancy in time, in the form of writing checkpoints to disk and restarting at the most recent checkpoint after a fault that cause an application to crash/halt, is the most common tool used in applications today, but there are questions about how long this can continue to be a good solution as systems and memories grow faster than I/O bandwidth to disk. There is interest in both modifications to this, such as checkpoints to memory, partial checkpoints, and message logging, and alternative ideas, such as in-memory recovery using residues. We believe that systematic exploration of these ideas holds the most promise for the scientific applications community. Fault tolerance has been an issue of discussion in the HPC community for at least the past 10 years; but much like other issues, the community has managed to put off addressing it during this period. There is a growing recognition that as systems continue to grow to petascale and beyond, the field is approaching the point where we don't have any choice but to address this through R&D efforts.« less

Meningeal hemangiopericytoma and solitary fibrous tumors carry the NAB2-STAT6 fusion and can be diagnosed by nuclear expression of STAT6 protein.

PubMed

Schweizer, Leonille; Koelsche, Christian; Sahm, Felix; Piro, Rosario M; Capper, David; Reuss, David E; Pusch, Stefan; Habel, Antje; Meyer, Jochen; Göck, Tanja; Jones, David T W; Mawrin, Christian; Schittenhelm, Jens; Becker, Albert; Heim, Stephanie; Simon, Matthias; Herold-Mende, Christel; Mechtersheimer, Gunhild; Paulus, Werner; König, Rainer; Wiestler, Otmar D; Pfister, Stefan M; von Deimling, Andreas

2013-05-01

Non-central nervous system hemangiopericytoma (HPC) and solitary fibrous tumor (SFT) are considered by pathologists as two variants of a single tumor entity now subsumed under the entity SFT. Recent detection of frequent NAB2-STAT6 fusions in both, HPC and SFT, provided additional support for this view. On the other hand, current neuropathological practice still distinguishes between HPC and SFT. The present study set out to identify genes involved in the formation of meningeal HPC. We performed exome sequencing and detected the NAB2-STAT6 fusion in DNA of 8/10 meningeal HPC thereby providing evidence of close relationship of these tumors with peripheral SFT. Due to the considerable effort required for exome sequencing, we sought to explore surrogate markers for the NAB2-STAT6 fusion protein. We adopted the Duolink proximity ligation assay and demonstrated the presence of NAB2-STAT6 fusion protein in 17/17 HPC and the absence in 15/15 meningiomas. More practical, presence of the NAB2-STAT6 fusion protein resulted in a strong nuclear signal in STAT6 immunohistochemistry. The nuclear reallocation of STAT6 was detected in 35/37 meningeal HPC and 25/25 meningeal SFT but not in 87 meningiomas representing the most important differential diagnosis. Tissues not harboring the NAB2-STAT6 fusion protein presented with nuclear expression of NAB2 and cytoplasmic expression of STAT6 proteins. In conclusion, we provide strong evidence for meningeal HPC and SFT to constitute variants of a single entity which is defined by NAB2-STAT6 fusion. In addition, we demonstrate that this fusion can be rapidly detected by STAT6 immunohistochemistry which shows a consistent nuclear reallocation. This immunohistochemical assay may prove valuable for the differentiation of HPC and SFT from other mesenchymal neoplasms.

The Reduced Basis Method in Geosciences: Practical examples for numerical forward simulations

NASA Astrophysics Data System (ADS)

Degen, D.; Veroy, K.; Wellmann, F.

2017-12-01

Due to the highly heterogeneous character of the earth's subsurface, the complex coupling of thermal, hydrological, mechanical, and chemical processes, and the limited accessibility we have to face high-dimensional problems associated with high uncertainties in geosciences. Performing the obviously necessary uncertainty quantifications with a reasonable number of parameters is often not possible due to the high-dimensional character of the problem. Therefore, we are presenting the reduced basis (RB) method, being a model order reduction (MOR) technique, that constructs low-order approximations to, for instance, the finite element (FE) space. We use the RB method to address this computationally challenging simulations because this method significantly reduces the degrees of freedom. The RB method is decomposed into an offline and online stage, allowing to make the expensive pre-computations beforehand to get real-time results during field campaigns. Generally, the RB approach is most beneficial in the many-query and real-time context.We will illustrate the advantages of the RB method for the field of geosciences through two examples of numerical forward simulations.The first example is a geothermal conduction problem demonstrating the implementation of the RB method for a steady-state case. The second examples, a Darcy flow problem, shows the benefits for transient scenarios. In both cases, a quality evaluation of the approximations is given. Additionally, the runtimes for both the FE and the RB simulations are compared. We will emphasize the advantages of this method for repetitive simulations by showing the speed-up for the RB solution in contrast to the FE solution. Finally, we will demonstrate how the used implementation is usable in high-performance computing (HPC) infrastructures and evaluate its performance for such infrastructures. Hence, we will especially point out its scalability, yielding in an optimal usage on HPC infrastructures and normal working stations.

WImpiBLAST: web interface for mpiBLAST to help biologists perform large-scale annotation using high performance computing.

PubMed

Sharma, Parichit; Mantri, Shrikant S

2014-01-01

The function of a newly sequenced gene can be discovered by determining its sequence homology with known proteins. BLAST is the most extensively used sequence analysis program for sequence similarity search in large databases of sequences. With the advent of next generation sequencing technologies it has now become possible to study genes and their expression at a genome-wide scale through RNA-seq and metagenome sequencing experiments. Functional annotation of all the genes is done by sequence similarity search against multiple protein databases. This annotation task is computationally very intensive and can take days to obtain complete results. The program mpiBLAST, an open-source parallelization of BLAST that achieves superlinear speedup, can be used to accelerate large-scale annotation by using supercomputers and high performance computing (HPC) clusters. Although many parallel bioinformatics applications using the Message Passing Interface (MPI) are available in the public domain, researchers are reluctant to use them due to lack of expertise in the Linux command line and relevant programming experience. With these limitations, it becomes difficult for biologists to use mpiBLAST for accelerating annotation. No web interface is available in the open-source domain for mpiBLAST. We have developed WImpiBLAST, a user-friendly open-source web interface for parallel BLAST searches. It is implemented in Struts 1.3 using a Java backbone and runs atop the open-source Apache Tomcat Server. WImpiBLAST supports script creation and job submission features and also provides a robust job management interface for system administrators. It combines script creation and modification features with job monitoring and management through the Torque resource manager on a Linux-based HPC cluster. Use case information highlights the acceleration of annotation analysis achieved by using WImpiBLAST. Here, we describe the WImpiBLAST web interface features and architecture, explain design decisions, describe workflows and provide a detailed analysis.

WImpiBLAST: Web Interface for mpiBLAST to Help Biologists Perform Large-Scale Annotation Using High Performance Computing

PubMed Central

Sharma, Parichit; Mantri, Shrikant S.

2014-01-01

The function of a newly sequenced gene can be discovered by determining its sequence homology with known proteins. BLAST is the most extensively used sequence analysis program for sequence similarity search in large databases of sequences. With the advent of next generation sequencing technologies it has now become possible to study genes and their expression at a genome-wide scale through RNA-seq and metagenome sequencing experiments. Functional annotation of all the genes is done by sequence similarity search against multiple protein databases. This annotation task is computationally very intensive and can take days to obtain complete results. The program mpiBLAST, an open-source parallelization of BLAST that achieves superlinear speedup, can be used to accelerate large-scale annotation by using supercomputers and high performance computing (HPC) clusters. Although many parallel bioinformatics applications using the Message Passing Interface (MPI) are available in the public domain, researchers are reluctant to use them due to lack of expertise in the Linux command line and relevant programming experience. With these limitations, it becomes difficult for biologists to use mpiBLAST for accelerating annotation. No web interface is available in the open-source domain for mpiBLAST. We have developed WImpiBLAST, a user-friendly open-source web interface for parallel BLAST searches. It is implemented in Struts 1.3 using a Java backbone and runs atop the open-source Apache Tomcat Server. WImpiBLAST supports script creation and job submission features and also provides a robust job management interface for system administrators. It combines script creation and modification features with job monitoring and management through the Torque resource manager on a Linux-based HPC cluster. Use case information highlights the acceleration of annotation analysis achieved by using WImpiBLAST. Here, we describe the WImpiBLAST web interface features and architecture, explain design decisions, describe workflows and provide a detailed analysis. PMID:24979410

Underground Coal Thermal Treatment: Task 6 Topical Report, Utah Clean Coal Program

DOE Office of Scientific and Technical Information (OSTI.GOV)

Smith, P.J.; Deo, M.; Edding, E.G.

The long-term objective of this task is to develop a transformational energy production technology by in- situ thermal treatment of a coal seam for the production of substitute natural gas and/or liquid transportation fuels while leaving much of the coal’s carbon in the ground. This process converts coal to a high-efficiency, low-greenhouse gas (GHG) emitting fuel. It holds the potential of providing environmentally acceptable access to previously unusable coal resources. This task focused on three areas: Experimental. The Underground Coal Thermal Treatment (UCTT) team focused on experiments at two scales, bench-top and slightly larger, to develop data to understand themore » feasibility of a UCTT process as well as to develop validation/uncertainty quantification (V/UQ) data for the simulation team. Simulation. The investigators completed development of High Performance Computing (HPC) simulations of UCTT. This built on our simulation developments over the course of the task and included the application of Computational Fluid Dynamics (CFD)- based tools to perform HPC simulations of a realistically sized domain representative of an actual coal field located in Utah. CO 2 storage. In order to help determine the amount of CO 2 that can be sequestered in a coal formation that has undergone UCTT, adsorption isotherms were performed on coals treated to 325, 450, and 600°C with slow heating rates. Raw material was sourced from the Sufco (Utah), Carlinville (Illinois), and North Antelope (Wyoming) mines. The study indicated that adsorptive capacity for the coals increased with treatment temperature and that coals treated to 325°C showed less or similar capacity to the untreated coals.« less

Visualization assisted by parallel processing

NASA Astrophysics Data System (ADS)

Lange, B.; Rey, H.; Vasques, X.; Puech, W.; Rodriguez, N.

2011-01-01

This paper discusses the experimental results of our visualization model for data extracted from sensors. The objective of this paper is to find a computationally efficient method to produce a real time rendering visualization for a large amount of data. We develop visualization method to monitor temperature variance of a data center. Sensors are placed on three layers and do not cover all the room. We use particle paradigm to interpolate data sensors. Particles model the "space" of the room. In this work we use a partition of the particle set, using two mathematical methods: Delaunay triangulation and Voronoý cells. Avis and Bhattacharya present these two algorithms in. Particles provide information on the room temperature at different coordinates over time. To locate and update particles data we define a computational cost function. To solve this function in an efficient way, we use a client server paradigm. Server computes data and client display this data on different kind of hardware. This paper is organized as follows. The first part presents related algorithm used to visualize large flow of data. The second part presents different platforms and methods used, which was evaluated in order to determine the better solution for the task proposed. The benchmark use the computational cost of our algorithm that formed based on located particles compared to sensors and on update of particles value. The benchmark was done on a personal computer using CPU, multi core programming, GPU programming and hybrid GPU/CPU. GPU programming method is growing in the research field; this method allows getting a real time rendering instates of a precompute rendering. For improving our results, we compute our algorithm on a High Performance Computing (HPC), this benchmark was used to improve multi-core method. HPC is commonly used in data visualization (astronomy, physic, etc) for improving the rendering and getting real-time.

ASCR/HEP Exascale Requirements Review Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Habib, Salman; Roser, Robert; Gerber, Richard

This draft report summarizes and details the findings, results, and recommendations derived from the ASCR/HEP Exascale Requirements Review meeting held in June, 2015. The main conclusions are as follows. 1) Larger, more capable computing and data facilities are needed to support HEP science goals in all three frontiers: Energy, Intensity, and Cosmic. The expected scale of the demand at the 2025 timescale is at least two orders of magnitude -- and in some cases greater -- than that available currently. 2) The growth rate of data produced by simulations is overwhelming the current ability, of both facilities and researchers, tomore » store and analyze it. Additional resources and new techniques for data analysis are urgently needed. 3) Data rates and volumes from HEP experimental facilities are also straining the ability to store and analyze large and complex data volumes. Appropriately configured leadership-class facilities can play a transformational role in enabling scientific discovery from these datasets. 4) A close integration of HPC simulation and data analysis will aid greatly in interpreting results from HEP experiments. Such an integration will minimize data movement and facilitate interdependent workflows. 5) Long-range planning between HEP and ASCR will be required to meet HEP's research needs. To best use ASCR HPC resources the experimental HEP program needs a) an established long-term plan for access to ASCR computational and data resources, b) an ability to map workflows onto HPC resources, c) the ability for ASCR facilities to accommodate workflows run by collaborations that can have thousands of individual members, d) to transition codes to the next-generation HPC platforms that will be available at ASCR facilities, e) to build up and train a workforce capable of developing and using simulations and analysis to support HEP scientific research on next-generation systems.« less

ASCR/HEP Exascale Requirements Review Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Habib, Salman; et al.

2016-03-30

This draft report summarizes and details the findings, results, and recommendations derived from the ASCR/HEP Exascale Requirements Review meeting held in June, 2015. The main conclusions are as follows. 1) Larger, more capable computing and data facilities are needed to support HEP science goals in all three frontiers: Energy, Intensity, and Cosmic. The expected scale of the demand at the 2025 timescale is at least two orders of magnitude -- and in some cases greater -- than that available currently. 2) The growth rate of data produced by simulations is overwhelming the current ability, of both facilities and researchers, tomore » store and analyze it. Additional resources and new techniques for data analysis are urgently needed. 3) Data rates and volumes from HEP experimental facilities are also straining the ability to store and analyze large and complex data volumes. Appropriately configured leadership-class facilities can play a transformational role in enabling scientific discovery from these datasets. 4) A close integration of HPC simulation and data analysis will aid greatly in interpreting results from HEP experiments. Such an integration will minimize data movement and facilitate interdependent workflows. 5) Long-range planning between HEP and ASCR will be required to meet HEP's research needs. To best use ASCR HPC resources the experimental HEP program needs a) an established long-term plan for access to ASCR computational and data resources, b) an ability to map workflows onto HPC resources, c) the ability for ASCR facilities to accommodate workflows run by collaborations that can have thousands of individual members, d) to transition codes to the next-generation HPC platforms that will be available at ASCR facilities, e) to build up and train a workforce capable of developing and using simulations and analysis to support HEP scientific research on next-generation systems.« less

Python and HPC for High Energy Physics Data Analyses

DOE PAGES

Sehrish, S.; Kowalkowski, J.; Paterno, M.; ...

2017-01-01

High level abstractions in Python that can utilize computing hardware well seem to be an attractive option for writing data reduction and analysis tasks. In this paper, we explore the features available in Python which are useful and efficient for end user analysis in High Energy Physics (HEP). A typical vertical slice of an HEP data analysis is somewhat fragmented: the state of the reduction/analysis process must be saved at certain stages to allow for selective reprocessing of only parts of a generally time-consuming workflow. Also, algorithms tend to to be modular because of the heterogeneous nature of most detectorsmore » and the need to analyze different parts of the detector separately before combining the information. This fragmentation causes difficulties for interactive data analysis, and as data sets increase in size and complexity (O10 TiB for a “small” neutrino experiment to the O10 PiB currently held by the CMS experiment at the LHC), data analysis methods traditional to the field must evolve to make optimum use of emerging HPC technologies and platforms. Mainstream big data tools, while suggesting a direction in terms of what can be done if an entire data set can be available across a system and analysed with high-level programming abstractions, are not designed with either scientific computing generally, or modern HPC platform features in particular, such as data caching levels, in mind. Our example HPC use case is a search for a new elementary particle which might explain the phenomenon known as “Dark Matter”. Here, using data from the CMS detector, we will use HDF5 as our input data format, and MPI with Python to implement our use case.« less

The HARNESS Workbench: Unified and Adaptive Access to Diverse HPC Platforms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sunderam, Vaidy S.

2012-03-20

The primary goal of the Harness WorkBench (HWB) project is to investigate innovative software environments that will help enhance the overall productivity of applications science on diverse HPC platforms. Two complementary frameworks were designed: one, a virtualized command toolkit for application building, deployment, and execution, that provides a common view across diverse HPC systems, in particular the DOE leadership computing platforms (Cray, IBM, SGI, and clusters); and two, a unified runtime environment that consolidates access to runtime services via an adaptive framework for execution-time and post processing activities. A prototype of the first was developed based on the concept ofmore » a 'system-call virtual machine' (SCVM), to enhance portability of the HPC application deployment process across heterogeneous high-end machines. The SCVM approach to portable builds is based on the insertion of toolkit-interpretable directives into original application build scripts. Modifications resulting from these directives preserve the semantics of the original build instruction flow. The execution of the build script is controlled by our toolkit that intercepts build script commands in a manner transparent to the end-user. We have applied this approach to a scientific production code (Gamess-US) on the Cray-XT5 machine. The second facet, termed Unibus, aims to facilitate provisioning and aggregation of multifaceted resources from resource providers and end-users perspectives. To achieve that, Unibus proposes a Capability Model and mediators (resource drivers) to virtualize access to diverse resources, and soft and successive conditioning to enable automatic and user-transparent resource provisioning. A proof of concept implementation has demonstrated the viability of this approach on high end machines, grid systems and computing clouds.« less

Emulating multiple inheritance in Fortran 2003/2008

DOE PAGES

Morris, Karla

2015-01-24

Although the high-performance computing (HPC) community increasingly embraces object-oriented programming (OOP), most HPC OOP projects employ the C++ programming language. Until recently, Fortran programmers interested in mining the benefits of OOP had to emulate OOP in Fortran 90/95. The advent of widespread compiler support for Fortran 2003 now facilitates explicitly constructing object-oriented class hierarchies via inheritance and leveraging related class behaviors such as dynamic polymorphism. Although C++ allows a class to inherit from multiple parent classes, Fortran and several other OOP languages restrict or prohibit explicit multiple inheritance relationships in order to circumvent several pitfalls associated with them. Nonetheless, whatmore » appears as an intrinsic feature in one language can be modeled as a user-constructed design pattern in another language. The present paper demonstrates how to apply the facade structural design pattern to support a multiple inheritance class relationship in Fortran 2003. As a result, the design unleashes the power of the associated class relationships for modeling complicated data structures yet avoids the ambiguities that plague some multiple inheritance scenarios.« less

eWaterCycle: A high resolution global hydrological model

NASA Astrophysics Data System (ADS)

van de Giesen, Nick; Bierkens, Marc; Drost, Niels; Hut, Rolf; Sutanudjaja, Edwin

2014-05-01

In 2013, the eWaterCycle project was started, which has the ambitious goal to run a high resolution global hydrological model. Starting point was the PCR-GLOBWB built by Utrecht University. The software behind this model will partially be re-engineered in order to enable to run it in a High Performance Computing (HPC) environment. The aim is to have a spatial resolution of 1km x 1km. The idea is also to run the model in real-time and forecasting mode, using data assimilation. An on-demand hydraulic model will be available for detailed flow and flood forecasting in support of navigation and disaster management. The project faces a set of scientific challenges. First, to enable the model to run in a HPC environment, model runs were analyzed to examine on which parts of the program most CPU time was spent. These parts were re-coded in Open MPI to allow for parallel processing. Different parallelization strategies are thinkable. In our case, it was decided to use watershed logic as a first step to distribute the analysis. There is rather limited recent experience with HPC in hydrology and there is much to be learned and adjusted, both on the hydrological modeling side and the computer science side. For example, an interesting early observation was that hydrological models are, due to their localized parameterization, much more memory intensive than models of sister-disciplines such as meteorology and oceanography. Because it would be deadly to have to swap information between CPU and hard drive, memory management becomes crucial. A standard Ensemble Kalman Filter (enKF) would, for example, have excessive memory demands. To circumvent these problems, an alternative to the enKF was developed that produces equivalent results. This presentation shows the most recent results from the model, including a 5km x 5km simulation and a proof of concept for the new data assimilation approach. Finally, some early ideas about financial sustainability of an operational global hydrological model are presented.

Hierarchical porous carbon/MnO2 hybrids as supercapacitor electrodes.

PubMed

Lee, Min Eui; Yun, Young Soo; Jin, Hyoung-Joon

2014-12-01

Hybrid electrodes of hierarchical porous carbon (HPC) and manganese oxide (MnO2) were synthesized using a fast surface redox reaction of potassium permanganate under facile immersion methods. The HPC/MnO2 hybrids had a number of micropores and macropores and the MnO2 nanoparticles acted as a pseudocapacitive material. The synergistic effects of electric double-layer capacitor (EDLC)-induced capacitance and pseudocapacitance brought about a better electrochemical performance of the HPC/MnO2 hybrid electrodes compared to that obtained with a single component. The hybrids showed a specific capacitance of 228 F g(-1) and good cycle stability over 1000 cycles.

Decaf: Decoupled Dataflows for In Situ High-Performance Workflows

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dreher, M.; Peterka, T.

Decaf is a dataflow system for the parallel communication of coupled tasks in an HPC workflow. The dataflow can perform arbitrary data transformations ranging from simply forwarding data to complex data redistribution. Decaf does this by allowing the user to allocate resources and execute custom code in the dataflow. All communication through the dataflow is efficient parallel message passing over MPI. The runtime for calling tasks is entirely message-driven; Decaf executes a task when all messages for the task have been received. Such a messagedriven runtime allows cyclic task dependencies in the workflow graph, for example, to enact computational steeringmore » based on the result of downstream tasks. Decaf includes a simple Python API for describing the workflow graph. This allows Decaf to stand alone as a complete workflow system, but Decaf can also be used as the dataflow layer by one or more other workflow systems to form a heterogeneous task-based computing environment. In one experiment, we couple a molecular dynamics code with a visualization tool using the FlowVR and Damaris workflow systems and Decaf for the dataflow. In another experiment, we test the coupling of a cosmology code with Voronoi tessellation and density estimation codes using MPI for the simulation, the DIY programming model for the two analysis codes, and Decaf for the dataflow. Such workflows consisting of heterogeneous software infrastructures exist because components are developed separately with different programming models and runtimes, and this is the first time that such heterogeneous coupling of diverse components was demonstrated in situ on HPC systems.« less

Performance Analysis, Design Considerations, and Applications of Extreme-Scale In Situ Infrastructures

DOE PAGES

Ayachit, Utkarsh; Bauer, Andrew; Duque, Earl P. N.; ...

2016-11-01

A key trend facing extreme-scale computational science is the widening gap between computational and I/O rates, and the challenge that follows is how to best gain insight from simulation data when it is increasingly impractical to save it to persistent storage for subsequent visual exploration and analysis. One approach to this challenge is centered around the idea of in situ processing, where visualization and analysis processing is performed while data is still resident in memory. Our paper examines several key design and performance issues related to the idea of in situ processing at extreme scale on modern platforms: Scalability, overhead,more » performance measurement and analysis, comparison and contrast with a traditional post hoc approach, and interfacing with simulation codes. We illustrate these principles in practice with studies, conducted on large-scale HPC platforms, that include a miniapplication and multiple science application codes, one of which demonstrates in situ methods in use at greater than 1M-way concurrency.« less

Managing Scientific Software Complexity with Bocca and CCA

DOE PAGES

Allan, Benjamin A.; Norris, Boyana; Elwasif, Wael R.; ...

2008-01-01

In high-performance scientific software development, the emphasis is often on short time to first solution. Even when the development of new components mostly reuses existing components or libraries and only small amounts of new code must be created, dealing with the component glue code and software build processes to obtain complete applications is still tedious and error-prone. Component-based software meant to reduce complexity at the application level increases complexity to the extent that the user must learn and remember the interfaces and conventions of the component model itself. To address these needs, we introduce Bocca, the first tool to enablemore » application developers to perform rapid component prototyping while maintaining robust software-engineering practices suitable to HPC environments. Bocca provides project management and a comprehensive build environment for creating and managing applications composed of Common Component Architecture components. Of critical importance for high-performance computing (HPC) applications, Bocca is designed to operate in a language-agnostic way, simultaneously handling components written in any of the languages commonly used in scientific applications: C, C++, Fortran, Python and Java. Bocca automates the tasks related to the component glue code, freeing the user to focus on the scientific aspects of the application. Bocca embraces the philosophy pioneered by Ruby on Rails for web applications: start with something that works, and evolve it to the user's purpose.« less

EMHP: an accurate automated hole masking algorithm for single-particle cryo-EM image processing.

PubMed

Berndsen, Zachary; Bowman, Charles; Jang, Haerin; Ward, Andrew B

2017-12-01

The Electron Microscopy Hole Punch (EMHP) is a streamlined suite of tools for quick assessment, sorting and hole masking of electron micrographs. With recent advances in single-particle electron cryo-microscopy (cryo-EM) data processing allowing for the rapid determination of protein structures using a smaller computational footprint, we saw the need for a fast and simple tool for data pre-processing that could run independent of existing high-performance computing (HPC) infrastructures. EMHP provides a data preprocessing platform in a small package that requires minimal python dependencies to function. https://www.bitbucket.org/chazbot/emhp Apache 2.0 License. bowman@scripps.edu. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.