Combinatorial parallel and scientific computing.
Pinar, Ali; Hendrickson, Bruce Alan
2005-04-01
Combinatorial algorithms have long played a pivotal enabling role in many applications of parallel computing. Graph algorithms in particular arise in load balancing, scheduling, mapping and many other aspects of the parallelization of irregular applications. These are still active research areas, mostly due to evolving computational techniques and rapidly changing computational platforms. But the relationship between parallel computing and discrete algorithms is much richer than the mere use of graph algorithms to support the parallelization of traditional scientific computations. Important, emerging areas of science are fundamentally discrete, and they are increasingly reliant on the power of parallel computing. Examples include computational biology, scientific data mining, and network analysis. These applications are changing the relationship between discrete algorithms and parallel computing. In addition to their traditional role as enablers of high performance, combinatorial algorithms are now customers for parallel computing. New parallelization techniques for combinatorial algorithms need to be developed to support these nontraditional scientific approaches. This chapter will describe some of the many areas of intersection between discrete algorithms and parallel scientific computing. Due to space limitations, this chapter is not a comprehensive survey, but rather an introduction to a diverse set of techniques and applications with a particular emphasis on work presented at the Eleventh SIAM Conference on Parallel Processing for Scientific Computing. Some topics highly relevant to this chapter (e.g. load balancing) are addressed elsewhere in this book, and so we will not discuss them here.
Parallel processing for scientific computations
NASA Technical Reports Server (NTRS)
Alkhatib, Hasan S.
1995-01-01
The scope of this project dealt with the investigation of the requirements to support distributed computing of scientific computations over a cluster of cooperative workstations. Various experiments on computations for the solution of simultaneous linear equations were performed in the early phase of the project to gain experience in the general nature and requirements of scientific applications. A specification of a distributed integrated computing environment, DICE, based on a distributed shared memory communication paradigm has been developed and evaluated. The distributed shared memory model facilitates porting existing parallel algorithms that have been designed for shared memory multiprocessor systems to the new environment. The potential of this new environment is to provide supercomputing capability through the utilization of the aggregate power of workstations cooperating in a cluster interconnected via a local area network. Workstations, generally, do not have the computing power to tackle complex scientific applications, making them primarily useful for visualization, data reduction, and filtering as far as complex scientific applications are concerned. There is a tremendous amount of computing power that is left unused in a network of workstations. Very often a workstation is simply sitting idle on a desk. A set of tools can be developed to take advantage of this potential computing power to create a platform suitable for large scientific computations. The integration of several workstations into a logical cluster of distributed, cooperative, computing stations presents an alternative to shared memory multiprocessor systems. In this project we designed and evaluated such a system.
Parallel processing for scientific computations
NASA Technical Reports Server (NTRS)
Alkhatib, Hasan S.
1991-01-01
The main contribution of the effort in the last two years is the introduction of the MOPPS system. After doing extensive literature search, we introduced the system which is described next. MOPPS employs a new solution to the problem of managing programs which solve scientific and engineering applications on a distributed processing environment. Autonomous computers cooperate efficiently in solving large scientific problems with this solution. MOPPS has the advantage of not assuming the presence of any particular network topology or configuration, computer architecture, or operating system. It imposes little overhead on network and processor resources while efficiently managing programs concurrently. The core of MOPPS is an intelligent program manager that builds a knowledge base of the execution performance of the parallel programs it is managing under various conditions. The manager applies this knowledge to improve the performance of future runs. The program manager learns from experience.
Parallel hypergraph partitioning for scientific computing.
Heaphy, Robert; Devine, Karen Dragon; Catalyurek, Umit; Bisseling, Robert; Hendrickson, Bruce Alan; Boman, Erik Gunnar
2005-07-01
Graph partitioning is often used for load balancing in parallel computing, but it is known that hypergraph partitioning has several advantages. First, hypergraphs more accurately model communication volume, and second, they are more expressive and can better represent nonsymmetric problems. Hypergraph partitioning is particularly suited to parallel sparse matrix-vector multiplication, a common kernel in scientific computing. We present a parallel software package for hypergraph (and sparse matrix) partitioning developed at Sandia National Labs. The algorithm is a variation on multilevel partitioning. Our parallel implementation is novel in that it uses a two-dimensional data distribution among processors. We present empirical results that show our parallel implementation achieves good speedup on several large problems (up to 33 million nonzeros) with up to 64 processors on a Linux cluster.
PISCES: An environment for parallel scientific computation
NASA Technical Reports Server (NTRS)
Pratt, T. W.
1985-01-01
The parallel implementation of scientific computing environment (PISCES) is a project to provide high-level programming environments for parallel MIMD computers. Pisces 1, the first of these environments, is a FORTRAN 77 based environment which runs under the UNIX operating system. The Pisces 1 user programs in Pisces FORTRAN, an extension of FORTRAN 77 for parallel processing. The major emphasis in the Pisces 1 design is in providing a carefully specified virtual machine that defines the run-time environment within which Pisces FORTRAN programs are executed. Each implementation then provides the same virtual machine, regardless of differences in the underlying architecture. The design is intended to be portable to a variety of architectures. Currently Pisces 1 is implemented on a network of Apollo workstations and on a DEC VAX uniprocessor via simulation of the task level parallelism. An implementation for the Flexible Computing Corp. FLEX/32 is under construction. An introduction to the Pisces 1 virtual computer and the FORTRAN 77 extensions is presented. An example of an algorithm for the iterative solution of a system of equations is given. The most notable features of the design are the provision for several granularities of parallelism in programs and the provision of a window mechanism for distributed access to large arrays of data.
Advances in Domain Mapping of Massively Parallel Scientific Computations
Leland, Robert W.; Hendrickson, Bruce A.
2015-10-01
One of the most important concerns in parallel computing is the proper distribution of workload across processors. For most scientific applications on massively parallel machines, the best approach to this distribution is to employ data parallelism; that is, to break the datastructures supporting a computation into pieces and then to assign those pieces to different processors. Collectively, these partitioning and assignment tasks comprise the domain mapping problem.
On combining computational differentiation and toolkits for parallel scientific computing.
Bischof, C. H.; Buecker, H. M.; Hovland, P. D.
2000-06-08
Automatic differentiation is a powerful technique for evaluating derivatives of functions given in the form of a high-level programming language such as Fortran, C, or C++. The program is treated as a potentially very long sequence of elementary statements to which the chain rule of differential calculus is applied over and over again. Combining automatic differentiation and the organizational structure of toolkits for parallel scientific computing provides a mechanism for evaluating derivatives by exploiting mathematical insight on a higher level. In these toolkits, algorithmic structures such as BLAS-like operations, linear and nonlinear solvers, or integrators for ordinary differential equations can be identified by their standardized interfaces and recognized as high-level mathematical objects rather than as a sequence of elementary statements. In this note, the differentiation of a linear solver with respect to some parameter vector is taken as an example. Mathematical insight is used to reformulate this problem into the solution of multiple linear systems that share the same coefficient matrix but differ in their right-hand sides. The experiments reported here use ADIC, a tool for the automatic differentiation of C programs, and PETSC, an object-oriented toolkit for the parallel solution of scientific problems modeled by partial differential equations.
Lattice gauge theory on the Intel parallel scientific computer
NASA Astrophysics Data System (ADS)
Gottlieb, Steven
1990-08-01
Intel Scientific Computers (ISC) has just started producing its third general of parallel computer, the iPSC/860. Based on the i860 chip that has a peak performance of 80 Mflops and with a current maximum of 128 nodes, this computer should achieve speeds in excess of those obtainable on conventional vector supercomputers. The hardware, software and computing techniques appropriate for lattice gauge theory calculations are described. The differences between a staggered fermion conjugate gradient program written under CANOPY and for the iPSC are detailed.
Review of An Introduction to Parallel and Vector Scientific Computing
Bailey, David H.; Lefton, Lew
2006-06-30
On one hand, the field of high-performance scientific computing is thriving beyond measure. Performance of leading-edge systems on scientific calculations, as measured say by the Top500 list, has increased by an astounding factor of 8000 during the 15-year period from 1993 to 2008, which is slightly faster even than Moore's Law. Even more importantly, remarkable advances in numerical algorithms, numerical libraries and parallel programming environments have led to improvements in the scope of what can be computed that are entirely on a par with the advances in computing hardware. And these successes have spread far beyond the confines of large government-operated laboratories, many universities, modest-sized research institutes and private firms now operate clusters that differ only in scale from the behemoth systems at the large-scale facilities. In the wake of these recent successes, researchers from fields that heretofore have not been part of the scientific computing world have been drawn into the arena. For example, at the recent SC07 conference, the exhibit hall, which long has hosted displays from leading computer systems vendors and government laboratories, featured some 70 exhibitors who had not previously participated. In spite of all these exciting developments, and in spite of the clear need to present these concepts to a much broader technical audience, there is a perplexing dearth of training material and textbooks in the field, particularly at the introductory level. Only a handful of universities offer coursework in the specific area of highly parallel scientific computing, and instructors of such courses typically rely on custom-assembled material. For example, the present reviewer and Robert F. Lucas relied on materials assembled in a somewhat ad-hoc fashion from colleagues and personal resources when presenting a course on parallel scientific computing at the University of California, Berkeley, a few years ago. Thus it is indeed refreshing to see
SIAM Conference on Parallel Processing for Scientific Computing - March 12-14, 2008
Kolata, William G.
2008-09-08
The themes of the 2008 conference included, but were not limited to: Programming languages, models, and compilation techniques; The transition to ubiquitous multicore/manycore processors; Scientific computing on special-purpose processors (Cell, GPUs, etc.); Architecture-aware algorithms; From scalable algorithms to scalable software; Tools for software development and performance evaluation; Global perspectives on HPC; Parallel computing in industry; Distributed/grid computing; Fault tolerance; Parallel visualization and large scale data management; and The future of parallel architectures.
Treveaven, P.
1989-01-01
This book presents an introduction to object-oriented, functional, and logic parallel computing on which the fifth generation of computer systems will be based. Coverage includes concepts for parallel computing languages, a parallel object-oriented system (DOOM) and its language (POOL), an object-oriented multilevel VLSI simulator using POOL, and implementation of lazy functional languages on parallel architectures.
Seventh SIAM Conference on Parallel Processing for Scientific Computing. Final technical report
1996-10-01
The Seventh SIAM Conference on Parallel Processing for Scientific Computing was held in downtown San Francisco on the dates above. More than 400 people attended the meeting. This SIAM conference is, in this organizer`s opinion, the premier forum for developments in parallel numerical algorithms, a field that has seen very lively and fruitful developments over the past decade, and whose health is still robust. Other, related areas, most notably parallel software and applications, are also well represented. The strong contributed sessions and minisymposia at the meeting attest to these claims.
NASA Astrophysics Data System (ADS)
Huberman, Bernardo A.
1989-11-01
This paper reviews three different aspects of parallel computation which are useful for physics. The first part deals with special architectures for parallel computing (SIMD and MIMD machines) and their differences, with examples of their uses. The second section discusses the speedup that can be achieved in parallel computation and the constraints generated by the issues of communication and synchrony. The third part describes computation by distributed networks of powerful workstations without global controls and the issues involved in understanding their behavior.
Not Available
1991-10-23
An account of the Caltech Concurrent Computation Program (C{sup 3}P), a five year project that focused on answering the question: Can parallel computers be used to do large-scale scientific computations '' As the title indicates, the question is answered in the affirmative, by implementing numerous scientific applications on real parallel computers and doing computations that produced new scientific results. In the process of doing so, C{sup 3}P helped design and build several new computers, designed and implemented basic system software, developed algorithms for frequently used mathematical computations on massively parallel machines, devised performance models and measured the performance of many computers, and created a high performance computing facility based exclusively on parallel computers. While the initial focus of C{sup 3}P was the hypercube architecture developed by C. Seitz, many of the methods developed and lessons learned have been applied successfully on other massively parallel architectures.
Eighth SIAM conference on parallel processing for scientific computing: Final program and abstracts
1997-12-31
This SIAM conference is the premier forum for developments in parallel numerical algorithms, a field that has seen very lively and fruitful developments over the past decade, and whose health is still robust. Themes for this conference were: combinatorial optimization; data-parallel languages; large-scale parallel applications; message-passing; molecular modeling; parallel I/O; parallel libraries; parallel software tools; parallel compilers; particle simulations; problem-solving environments; and sparse matrix computations.
Barrett, Richard Frederick; Heroux, Michael Allen; Vaughan, Courtenay Thomas
2012-04-01
A broad range of scientific computation involves the use of difference stencils. In a parallel computing environment, this computation is typically implemented by decomposing the spacial domain, inducing a 'halo exchange' of process-owned boundary data. This approach adheres to the Bulk Synchronous Parallel (BSP) model. Because commonly available architectures provide strong inter-node bandwidth relative to latency costs, many codes 'bulk up' these messages by aggregating data into a message as a means of reducing the number of messages. A renewed focus on non-traditional architectures and architecture features provides new opportunities for exploring alternatives to this programming approach. In this report we describe miniGhost, a 'miniapp' designed for exploration of the capabilities of current as well as emerging and future architectures within the context of these sorts of applications. MiniGhost joins the suite of miniapps developed as part of the Mantevo project.
NASA Technical Reports Server (NTRS)
Denning, Peter J.; Tichy, Walter F.
1990-01-01
Among the highly parallel computing architectures required for advanced scientific computation, those designated 'MIMD' and 'SIMD' have yielded the best results to date. The present development status evaluation of such architectures shown neither to have attained a decisive advantage in most near-homogeneous problems' treatment; in the cases of problems involving numerous dissimilar parts, however, such currently speculative architectures as 'neural networks' or 'data flow' machines may be entailed. Data flow computers are the most practical form of MIMD fine-grained parallel computers yet conceived; they automatically solve the problem of assigning virtual processors to the real processors in the machine.
NASA Technical Reports Server (NTRS)
Denning, Peter J.; Tichy, Walter F.
1990-01-01
Highly parallel computing architectures are the only means to achieve the computation rates demanded by advanced scientific problems. A decade of research has demonstrated the feasibility of such machines and current research focuses on which architectures designated as multiple instruction multiple datastream (MIMD) and single instruction multiple datastream (SIMD) have produced the best results to date; neither shows a decisive advantage for most near-homogeneous scientific problems. For scientific problems with many dissimilar parts, more speculative architectures such as neural networks or data flow may be needed.
2016-11-09
are summarized in an article submitted to Journal of Computational Physics [1]. Bibliography : [1] C. Ngo and W. Huang, A study on moving mesh...computing, numerical analysis, scientific computing, scientific visualization, computational fluid dynamics, computational chemistry, computational...involving simultaneous parallel computing and parallel visualization: unstructured meshing, scientific visualization, computational fluid dynamics
NASA Technical Reports Server (NTRS)
Dongarra, Jack (Editor); Messina, Paul (Editor); Sorensen, Danny C. (Editor); Voigt, Robert G. (Editor)
1990-01-01
Attention is given to such topics as an evaluation of block algorithm variants in LAPACK and presents a large-grain parallel sparse system solver, a multiprocessor method for the solution of the generalized Eigenvalue problem on an interval, and a parallel QR algorithm for iterative subspace methods on the CM2. A discussion of numerical methods includes the topics of asynchronous numerical solutions of PDEs on parallel computers, parallel homotopy curve tracking on a hypercube, and solving Navier-Stokes equations on the Cedar Multi-Cluster system. A section on differential equations includes a discussion of a six-color procedure for the parallel solution of elliptic systems using the finite quadtree structure, data parallel algorithms for the finite element method, and domain decomposition methods in aerodynamics. Topics dealing with massively parallel computing include hypercube vs. 2-dimensional meshes and massively parallel computation of conservation laws. Performance and tools are also discussed.
Research in parallel computing
NASA Technical Reports Server (NTRS)
Ortega, James M.; Henderson, Charles
1994-01-01
This report summarizes work on parallel computations for NASA Grant NAG-1-1529 for the period 1 Jan. - 30 June 1994. Short summaries on highly parallel preconditioners, target-specific parallel reductions, and simulation of delta-cache protocols are provided.
Parallel and Distributed Computing.
1986-12-12
program was devoted to parallel and distributed computing . Support for this part of the program was obtained from the present Army contract and a...Umesh Vazirani. A workshop on parallel and distributed computing was held from May 19 to May 23, 1986 and drew 141 participants. Keywords: Mathematical programming; Protocols; Randomized algorithms. (Author)
Bassetti, F.; Davis, K.; Quinlan, D.
1998-09-01
Application codes reliably achieve performance far less than the advertised capabilities of existing architectures, and this problem is worsening with increasingly-parallel machines. For large-scale numerical applications, stencil operations often impose the great part of the computational cost, and the primary sources of inefficiency are the costs of message passing and poor cache utilization. This paper proposes and demonstrates optimizations for stencil and stencil-like computations for both serial and parallel environments that ameliorate these sources of inefficiency. Achieving scalability, they believe, requires both algorithm design and compile-time support. The optimizations they present are automatable because the stencil-like computations are implemented at a high level of abstraction using object-oriented parallel array class libraries. These optimizations, which are beyond the capabilities of today compilers, may be performed automatically by a preprocessor such as the one they are currently developing.
Merlin - Massively parallel heterogeneous computing
NASA Technical Reports Server (NTRS)
Wittie, Larry; Maples, Creve
1989-01-01
Hardware and software for Merlin, a new kind of massively parallel computing system, are described. Eight computers are linked as a 300-MIPS prototype to develop system software for a larger Merlin network with 16 to 64 nodes, totaling 600 to 3000 MIPS. These working prototypes help refine a mapped reflective memory technique that offers a new, very general way of linking many types of computer to form supercomputers. Processors share data selectively and rapidly on a word-by-word basis. Fast firmware virtual circuits are reconfigured to match topological needs of individual application programs. Merlin's low-latency memory-sharing interfaces solve many problems in the design of high-performance computing systems. The Merlin prototypes are intended to run parallel programs for scientific applications and to determine hardware and software needs for a future Teraflops Merlin network.
Merlin - Massively parallel heterogeneous computing
NASA Technical Reports Server (NTRS)
Wittie, Larry; Maples, Creve
1989-01-01
Hardware and software for Merlin, a new kind of massively parallel computing system, are described. Eight computers are linked as a 300-MIPS prototype to develop system software for a larger Merlin network with 16 to 64 nodes, totaling 600 to 3000 MIPS. These working prototypes help refine a mapped reflective memory technique that offers a new, very general way of linking many types of computer to form supercomputers. Processors share data selectively and rapidly on a word-by-word basis. Fast firmware virtual circuits are reconfigured to match topological needs of individual application programs. Merlin's low-latency memory-sharing interfaces solve many problems in the design of high-performance computing systems. The Merlin prototypes are intended to run parallel programs for scientific applications and to determine hardware and software needs for a future Teraflops Merlin network.
Visualizing Parallel Computer System Performance
NASA Technical Reports Server (NTRS)
Malony, Allen D.; Reed, Daniel A.
1988-01-01
Parallel computer systems are among the most complex of man's creations, making satisfactory performance characterization difficult. Despite this complexity, there are strong, indeed, almost irresistible, incentives to quantify parallel system performance using a single metric. The fallacy lies in succumbing to such temptations. A complete performance characterization requires not only an analysis of the system's constituent levels, it also requires both static and dynamic characterizations. Static or average behavior analysis may mask transients that dramatically alter system performance. Although the human visual system is remarkedly adept at interpreting and identifying anomalies in false color data, the importance of dynamic, visual scientific data presentation has only recently been recognized Large, complex parallel system pose equally vexing performance interpretation problems. Data from hardware and software performance monitors must be presented in ways that emphasize important events while eluding irrelevant details. Design approaches and tools for performance visualization are the subject of this paper.
Parallel Computing in Optimization.
1984-10-01
include : Heller [1978] and Sameh [1977] (surveys of algorithms), Duff [1983], Fong and Jordan [1977]. Jordan [1979]. and Rodrigue [1982] (all mainly...constrained concave function by partition of feasible domain", Mathematics of Operations Research 8, pp. A. Sameh [1977, "Numerical parallel algorithms...a survey", in High Speed Computer and Algorithm Organization, D. Kuck, D. Lawrie, and A. Sameh , eds., Academic Press, pp. 207-228. 1,. J. Siegel
DeHart, Mark D; Williams, Mark L; Bowman, Stephen M
2010-01-01
The SCALE computational architecture has remained basically the same since its inception 30 years ago, although constituent modules and capabilities have changed significantly. This SCALE concept was intended to provide a framework whereby independent codes can be linked to provide a more comprehensive capability than possible with the individual programs - allowing flexibility to address a wide variety of applications. However, the current system was designed originally for mainframe computers with a single CPU and with significantly less memory than today's personal computers. It has been recognized that the present SCALE computation system could be restructured to take advantage of modern hardware and software capabilities, while retaining many of the modular features of the present system. Preliminary work is being done to define specifications and capabilities for a more advanced computational architecture. This paper describes the state of current SCALE development activities and plans for future development. With the release of SCALE 6.1 in 2010, a new phase of evolutionary development will be available to SCALE users within the TRITON and NEWT modules. The SCALE (Standardized Computer Analyses for Licensing Evaluation) code system developed by Oak Ridge National Laboratory (ORNL) provides a comprehensive and integrated package of codes and nuclear data for a wide range of applications in criticality safety, reactor physics, shielding, isotopic depletion and decay, and sensitivity/uncertainty (S/U) analysis. Over the last three years, since the release of version 5.1 in 2006, several important new codes have been introduced within SCALE, and significant advances applied to existing codes. Many of these new features became available with the release of SCALE 6.0 in early 2009. However, beginning with SCALE 6.1, a first generation of parallel computing is being introduced. In addition to near-term improvements, a plan for longer term SCALE enhancement
Massively parallel processor computer
NASA Technical Reports Server (NTRS)
Fung, L. W. (Inventor)
1983-01-01
An apparatus for processing multidimensional data with strong spatial characteristics, such as raw image data, characterized by a large number of parallel data streams in an ordered array is described. It comprises a large number (e.g., 16,384 in a 128 x 128 array) of parallel processing elements operating simultaneously and independently on single bit slices of a corresponding array of incoming data streams under control of a single set of instructions. Each of the processing elements comprises a bidirectional data bus in communication with a register for storing single bit slices together with a random access memory unit and associated circuitry, including a binary counter/shift register device, for performing logical and arithmetical computations on the bit slices, and an I/O unit for interfacing the bidirectional data bus with the data stream source. The massively parallel processor architecture enables very high speed processing of large amounts of ordered parallel data, including spatial translation by shifting or sliding of bits vertically or horizontally to neighboring processing elements.
Parallel distributed computing using Python
NASA Astrophysics Data System (ADS)
Dalcin, Lisandro D.; Paz, Rodrigo R.; Kler, Pablo A.; Cosimo, Alejandro
2011-09-01
This work presents two software components aimed to relieve the costs of accessing high-performance parallel computing resources within a Python programming environment: MPI for Python and PETSc for Python. MPI for Python is a general-purpose Python package that provides bindings for the Message Passing Interface (MPI) standard using any back-end MPI implementation. Its facilities allow parallel Python programs to easily exploit multiple processors using the message passing paradigm. PETSc for Python provides access to the Portable, Extensible Toolkit for Scientific Computation (PETSc) libraries. Its facilities allow sequential and parallel Python applications to exploit state of the art algorithms and data structures readily available in PETSc for the solution of large-scale problems in science and engineering. MPI for Python and PETSc for Python are fully integrated to PETSc-FEM, an MPI and PETSc based parallel, multiphysics, finite elements code developed at CIMEC laboratory. This software infrastructure supports research activities related to simulation of fluid flows with applications ranging from the design of microfluidic devices for biochemical analysis to modeling of large-scale stream/aquifer interactions.
Parallel Computational Protein Design
Zhou, Yichao; Donald, Bruce R.; Zeng, Jianyang
2016-01-01
Computational structure-based protein design (CSPD) is an important problem in computational biology, which aims to design or improve a prescribed protein function based on a protein structure template. It provides a practical tool for real-world protein engineering applications. A popular CSPD method that guarantees to find the global minimum energy solution (GMEC) is to combine both dead-end elimination (DEE) and A* tree search algorithms. However, in this framework, the A* search algorithm can run in exponential time in the worst case, which may become the computation bottleneck of large-scale computational protein design process. To address this issue, we extend and add a new module to the OSPREY program that was previously developed in the Donald lab [1] to implement a GPU-based massively parallel A* algorithm for improving protein design pipeline. By exploiting the modern GPU computational framework and optimizing the computation of the heuristic function for A* search, our new program, called gOSPREY, can provide up to four orders of magnitude speedups in large protein design cases with a small memory overhead comparing to the traditional A* search algorithm implementation, while still guaranteeing the optimality. In addition, gOSPREY can be configured to run in a bounded-memory mode to tackle the problems in which the conformation space is too large and the global optimal solution cannot be computed previously. Furthermore, the GPU-based A* algorithm implemented in the gOSPREY program can be combined with the state-of-the-art rotamer pruning algorithms such as iMinDEE [2] and DEEPer [3] to also consider continuous backbone and side-chain flexibility. PMID:27914056
NASA Technical Reports Server (NTRS)
VanZandt, John
1994-01-01
The usage model of supercomputers for scientific applications, such as computational fluid dynamics (CFD), has changed over the years. Scientific visualization has moved scientists away from looking at numbers to looking at three-dimensional images, which capture the meaning of the data. This change has impacted the system models for computing. This report details the model which is used by scientists at NASA's research centers.
Coveney, Peter V
2005-08-15
We introduce a definition of Grid computing which is adhered to throughout this Theme Issue. We compare the evolution of the World Wide Web with current aspirations for Grid computing and indicate areas that need further research and development before a generally usable Grid infrastructure becomes available. We discuss work that has been done in order to make scientific Grid computing a viable proposition, including the building of Grids, middleware developments, computational steering and visualization. We review science that has been enabled by contemporary computational Grids, and associated progress made through the widening availability of high performance computing.
Introduction to Parallel Computing
1992-05-01
Topology C, Ada, C++, Data-parallel FORTRAN, 2D mesh of node boards, each node FORTRAN-90 (late 1992) board has 1 application processor Devopment Tools ...parallel machines become the wave of the present, tools are increasingly needed to assist programmers in creating parallel tasks and coordinating...their activities. Linda was designed to be such a tool . Linda was designed with three important goals in mind: to be portable, efficient, and easy to use
Large scale scientific computing
Deuflhard, P. ); Engquist, B. )
1987-01-01
This book presents papers on large scale scientific computing. It includes: Initial value problems of ODE's and parabolic PDE's; Boundary value problems of ODE's and elliptic PDE's; Hyperbolic PDE's; Inverse problems; Optimization and optimal control problems; and Algorithm adaptation on supercomputers.
Computers in Scientific Instrumentation.
1982-01-13
The CPU bus Attachment. In the first applications or d 4ata are connected to the central ues parallel digital lines for data an computers to...simple function se- Mg on ae results of its previous opera- designing instruments that can provide hotios by being directly labeled for the ties. In...that the signal from the sensor is with an operating system is powerful , that might be found in appropriately pro- interpretable to give the sought- for
Parallel algorithms for mapping pipelined and parallel computations
NASA Technical Reports Server (NTRS)
Nicol, David M.
1988-01-01
Many computational problems in image processing, signal processing, and scientific computing are naturally structured for either pipelined or parallel computation. When mapping such problems onto a parallel architecture it is often necessary to aggregate an obvious problem decomposition. Even in this context the general mapping problem is known to be computationally intractable, but recent advances have been made in identifying classes of problems and architectures for which optimal solutions can be found in polynomial time. Among these, the mapping of pipelined or parallel computations onto linear array, shared memory, and host-satellite systems figures prominently. This paper extends that work first by showing how to improve existing serial mapping algorithms. These improvements have significantly lower time and space complexities: in one case a published O(nm sup 3) time algorithm for mapping m modules onto n processors is reduced to an O(nm log m) time complexity, and its space requirements reduced from O(nm sup 2) to O(m). Run time complexity is further reduced with parallel mapping algorithms based on these improvements, which run on the architecture for which they create the mappings.
Accelerating Scientific Computations using FPGAs
NASA Astrophysics Data System (ADS)
Pell, O.; Atasu, K.; Mencer, O.
Field Programmable Gate Arrays (FPGAs) are semiconductor devices that contain a grid of programmable cells, which the user configures to implement any digital circuit of up to a few million gates. Modern FPGAs allow the user to reconfigure these circuits many times each second, making FPGAs fully programmable and general purpose. Recent FPGA technology provides sufficient resources to tackle scientific applications on large-scale parallel systems. As a case study, we implement the Fast Fourier Transform [1] in a flexible floating point implementation. We utilize A Stream Compiler [2] (ASC) which combines C++ syntax with flexible floating point support by providing a 'HWfloat' data-type. The resulting FFT can be targeted to a variety of FPGA platforms in FFTW-style, though not yet completely automatically. The resulting FFT circuit can be adapted to the particular resources available on the system. The optimal implementation of an FFT accelerator depends on the length and dimensionality of the FFT, the available FPGA area, the available hard DSP blocks, the FPGA board architecture, and the precision and range of the application [3]. Software-style object-orientated abstractions allow us to pursue an accelerated pace of development by maximizing re-use of design patterns. ASC allows a few core hardware descriptions to generate hundreds of different circuit variants to meet particular speed, area and precision goals. The key to achieving maximum acceleration of FFT computation is to match memory and compute bandwidths so that maximum use is made of computational resources. Modern FPGAs contain up to hundreds of independent SRAM banks to store intermediate results, providing ample scope for optimizing memory parallelism. At 175Mhz, one of Maxeler's Radix-4 FFT cores computes 4x as many 1024pt FFTs per second as a dual Pentium-IV Xeon machine running FFTW. Eight such parallel cores fit onto the largest FPGA in the Xilinx Virtex-4 family, providing a 32x speed-up over
Scientific Computing with Python
NASA Astrophysics Data System (ADS)
Beazley, D. M.
Scripting languages have become a powerful tool for the construction of flexible scientific software because they provide scientists with an interpreted programming environment, can be easily interfaced with existing software written in C, C++, and Fortran, and can serve as a framework for modular software construction. In this paper, I describe the process of adding a scripting language to a scientific computing project by focusing on the use of Python with a large-scale molecular dynamics code developed for materials science research at Los Alamos National Laboratory. Although this application is not related to astronomical data analysis, the problems, solutions, and lessons learned may be of interest to researchers who are considering the use of scripting languages with their own projects.
Parallel Architecture For Robotics Computation
NASA Technical Reports Server (NTRS)
Fijany, Amir; Bejczy, Antal K.
1990-01-01
Universal Real-Time Robotic Controller and Simulator (URRCS) is highly parallel computing architecture for control and simulation of robot motion. Result of extensive algorithmic study of different kinematic and dynamic computational problems arising in control and simulation of robot motion. Study led to development of class of efficient parallel algorithms for these problems. Represents algorithmically specialized architecture, in sense capable of exploiting common properties of this class of parallel algorithms. System with both MIMD and SIMD capabilities. Regarded as processor attached to bus of external host processor, as part of bus memory.
Parallel computing in atmospheric chemistry models
Rotman, D.
1996-02-01
Studies of atmospheric chemistry are of high scientific interest, involve computations that are complex and intense, and require enormous amounts of I/O. Current supercomputer computational capabilities are limiting the studies of stratospheric and tropospheric chemistry and will certainly not be able to handle the upcoming coupled chemistry/climate models. To enable such calculations, the authors have developed a computing framework that allows computations on a wide range of computational platforms, including massively parallel machines. Because of the fast paced changes in this field, the modeling framework and scientific modules have been developed to be highly portable and efficient. Here, the authors present the important features of the framework and focus on the atmospheric chemistry module, named IMPACT, and its capabilities. Applications of IMPACT to aircraft studies will be presented.
Computational electromagnetics and parallel dense matrix computations
Forsman, K.; Kettunen, L.; Gropp, W.; Levine, D.
1995-06-01
We present computational results using CORAL, a parallel, three-dimensional, nonlinear magnetostatic code based on a volume integral equation formulation. A key feature of CORAL is the ability to solve, in parallel, the large, dense systems of linear equations that are inherent in the use of integral equation methods. Using the Chameleon and PSLES libraries ensures portability and access to the latest linear algebra solution technology.
Computational electromagnetics and parallel dense matrix computations
Forsman, K.; Kettunen, L.; Gropp, W.
1995-12-01
We present computational results using CORAL, a parallel, three-dimensional, nonlinear magnetostatic code based on a volume integral equation formulation. A key feature of CORAL is the ability to solve, in parallel, the large, dense systems of linear equations that are inherent in the use of integral equation methods. Using the Chameleon and PSLES libraries ensures portability and access to the latest linear algebra solution technology.
Parallel Tensor Compression for Large-Scale Scientific Data.
Kolda, Tamara G.; Ballard, Grey; Austin, Woody Nathan
2015-10-01
As parallel computing trends towards the exascale, scientific data produced by high-fidelity simulations are growing increasingly massive. For instance, a simulation on a three-dimensional spatial grid with 512 points per dimension that tracks 64 variables per grid point for 128 time steps yields 8 TB of data. By viewing the data as a dense five way tensor, we can compute a Tucker decomposition to find inherent low-dimensional multilinear structure, achieving compression ratios of up to 10000 on real-world data sets with negligible loss in accuracy. So that we can operate on such massive data, we present the first-ever distributed memory parallel implementation for the Tucker decomposition, whose key computations correspond to parallel linear algebra operations, albeit with nonstandard data layouts. Our approach specifies a data distribution for tensors that avoids any tensor data redistribution, either locally or in parallel. We provide accompanying analysis of the computation and communication costs of the algorithms. To demonstrate the compression and accuracy of the method, we apply our approach to real-world data sets from combustion science simulations. We also provide detailed performance results, including parallel performance in both weak and strong scaling experiments.
The science of computing - Parallel computation
NASA Technical Reports Server (NTRS)
Denning, P. J.
1985-01-01
Although parallel computation architectures have been known for computers since the 1920s, it was only in the 1970s that microelectronic components technologies advanced to the point where it became feasible to incorporate multiple processors in one machine. Concommitantly, the development of algorithms for parallel processing also lagged due to hardware limitations. The speed of computing with solid-state chips is limited by gate switching delays. The physical limit implies that a 1 Gflop operational speed is the maximum for sequential processors. A computer recently introduced features a 'hypercube' architecture with 128 processors connected in networks at 5, 6 or 7 points per grid, depending on the design choice. Its computing speed rivals that of supercomputers, but at a fraction of the cost. The added speed with less hardware is due to parallel processing, which utilizes algorithms representing different parts of an equation that can be broken into simpler statements and processed simultaneously. Present, highly developed computer languages like FORTRAN, PASCAL, COBOL, etc., rely on sequential instructions. Thus, increased emphasis will now be directed at parallel processing algorithms to exploit the new architectures.
Computing contingency statistics in parallel.
Bennett, Janine Camille; Thompson, David; Pebay, Philippe Pierre
2010-09-01
Statistical analysis is typically used to reduce the dimensionality of and infer meaning from data. A key challenge of any statistical analysis package aimed at large-scale, distributed data is to address the orthogonal issues of parallel scalability and numerical stability. Many statistical techniques, e.g., descriptive statistics or principal component analysis, are based on moments and co-moments and, using robust online update formulas, can be computed in an embarrassingly parallel manner, amenable to a map-reduce style implementation. In this paper we focus on contingency tables, through which numerous derived statistics such as joint and marginal probability, point-wise mutual information, information entropy, and {chi}{sup 2} independence statistics can be directly obtained. However, contingency tables can become large as data size increases, requiring a correspondingly large amount of communication between processors. This potential increase in communication prevents optimal parallel speedup and is the main difference with moment-based statistics where the amount of inter-processor communication is independent of data size. Here we present the design trade-offs which we made to implement the computation of contingency tables in parallel.We also study the parallel speedup and scalability properties of our open source implementation. In particular, we observe optimal speed-up and scalability when the contingency statistics are used in their appropriate context, namely, when the data input is not quasi-diffuse.
EPA uses high-end scientific computing, geospatial services and remote sensing/imagery analysis to support EPA's mission. The Center for Environmental Computing (CEC) assists the Agency's program offices and regions to meet staff needs in these areas.
High Performance Parallel Computational Nanotechnology
NASA Technical Reports Server (NTRS)
Saini, Subhash; Craw, James M. (Technical Monitor)
1995-01-01
At a recent press conference, NASA Administrator Dan Goldin encouraged NASA Ames Research Center to take a lead role in promoting research and development of advanced, high-performance computer technology, including nanotechnology. Manufacturers of leading-edge microprocessors currently perform large-scale simulations in the design and verification of semiconductor devices and microprocessors. Recently, the need for this intensive simulation and modeling analysis has greatly increased, due in part to the ever-increasing complexity of these devices, as well as the lessons of experiences such as the Pentium fiasco. Simulation, modeling, testing, and validation will be even more important for designing molecular computers because of the complex specification of millions of atoms, thousands of assembly steps, as well as the simulation and modeling needed to ensure reliable, robust and efficient fabrication of the molecular devices. The software for this capacity does not exist today, but it can be extrapolated from the software currently used in molecular modeling for other applications: semi-empirical methods, ab initio methods, self-consistent field methods, Hartree-Fock methods, molecular mechanics; and simulation methods for diamondoid structures. In as much as it seems clear that the application of such methods in nanotechnology will require powerful, highly powerful systems, this talk will discuss techniques and issues for performing these types of computations on parallel systems. We will describe system design issues (memory, I/O, mass storage, operating system requirements, special user interface issues, interconnects, bandwidths, and programming languages) involved in parallel methods for scalable classical, semiclassical, quantum, molecular mechanics, and continuum models; molecular nanotechnology computer-aided designs (NanoCAD) techniques; visualization using virtual reality techniques of structural models and assembly sequences; software required to
High Performance Parallel Computational Nanotechnology
NASA Technical Reports Server (NTRS)
Saini, Subhash; Craw, James M. (Technical Monitor)
1995-01-01
At a recent press conference, NASA Administrator Dan Goldin encouraged NASA Ames Research Center to take a lead role in promoting research and development of advanced, high-performance computer technology, including nanotechnology. Manufacturers of leading-edge microprocessors currently perform large-scale simulations in the design and verification of semiconductor devices and microprocessors. Recently, the need for this intensive simulation and modeling analysis has greatly increased, due in part to the ever-increasing complexity of these devices, as well as the lessons of experiences such as the Pentium fiasco. Simulation, modeling, testing, and validation will be even more important for designing molecular computers because of the complex specification of millions of atoms, thousands of assembly steps, as well as the simulation and modeling needed to ensure reliable, robust and efficient fabrication of the molecular devices. The software for this capacity does not exist today, but it can be extrapolated from the software currently used in molecular modeling for other applications: semi-empirical methods, ab initio methods, self-consistent field methods, Hartree-Fock methods, molecular mechanics; and simulation methods for diamondoid structures. In as much as it seems clear that the application of such methods in nanotechnology will require powerful, highly powerful systems, this talk will discuss techniques and issues for performing these types of computations on parallel systems. We will describe system design issues (memory, I/O, mass storage, operating system requirements, special user interface issues, interconnects, bandwidths, and programming languages) involved in parallel methods for scalable classical, semiclassical, quantum, molecular mechanics, and continuum models; molecular nanotechnology computer-aided designs (NanoCAD) techniques; visualization using virtual reality techniques of structural models and assembly sequences; software required to
Parallel computation of Gaussian processes
NASA Astrophysics Data System (ADS)
Preuss, R.; von Toussaint, U.
2017-06-01
Within the Bayesian framework we utilize Gaussian processes for parametric studies of long running computer codes. Since the simulations are expensive it is necessary to exploit the computational budget in the best possible manner. Employing the sum over variances - being indicators for the quality of the fit - as the utility function we established an optimized and automated sequential parameter selection procedure. However, often it is also desirable to utilize the parallel running capabilities of present computer technology and abandon the sequential parameter selection for a faster overall turn-around time (wall-clock time). The paper proposes to achieve this by marginalizing over the expected outcomes at optimized test points in order to set up a pool of starting values for batch execution.
ASP: a parallel computing technology
NASA Astrophysics Data System (ADS)
Lea, R. M.
1990-09-01
ASP modules constitute the basis of a parallel computing technology platform for the rapid development of a broad range of numeric and symbolic information processing systems. Based on off-the-shelf general-purpose hardware and software modules ASP technology is intended to increase productivity in the development (and competitiveness in the marketing) of cost-effective low-MIMD/high-SIMD Massively Parallel Processor (MPPs). The paper discusses ASP module philosophy and demonstrates how ASP modules can satisfy the market algorithmic architectural and engineering requirements of such MPPs. In particular two specific ASP modules based on VLSI and WSI technologies are studied as case examples of ASP technology the latter reporting 1 TOPS/fl3 1 GOPS/W and 1 MOPS/$ as ball-park figures-of-merit of cost-effectiveness.
Trajectory optimization using parallel shooting method on parallel computer
Wirthman, D.J.; Park, S.Y.; Vadali, S.R.
1995-03-01
The efficiency of a parallel shooting method on a parallel computer for solving a variety of optimal control guidance problems is studied. Several examples are considered to demonstrate that a speedup of nearly 7 to 1 is achieved with the use of 16 processors. It is suggested that further improvements in performance can be achieved by parallelizing in the state domain. 10 refs.
Parallel Pascal - An extended Pascal for parallel computers
NASA Technical Reports Server (NTRS)
Reeves, A. P.
1984-01-01
Parallel Pascal is an extended version of the conventional serial Pascal programming language which includes a convenient syntax for specifying array operations. It is upward compatible with standard Pascal and involves only a small number of carefully chosen new features. Parallel Pascal was developed to reduce the semantic gap between standard Pascal and a large range of highly parallel computers. Two important design goals of Parallel Pascal were efficiency and portability. Portability is particularly difficult to achieve since different parallel computers frequently have very different capabilities.
Parallel Pascal - An extended Pascal for parallel computers
NASA Technical Reports Server (NTRS)
Reeves, A. P.
1984-01-01
Parallel Pascal is an extended version of the conventional serial Pascal programming language which includes a convenient syntax for specifying array operations. It is upward compatible with standard Pascal and involves only a small number of carefully chosen new features. Parallel Pascal was developed to reduce the semantic gap between standard Pascal and a large range of highly parallel computers. Two important design goals of Parallel Pascal were efficiency and portability. Portability is particularly difficult to achieve since different parallel computers frequently have very different capabilities.
Parallel computing in enterprise modeling.
Goldsby, Michael E.; Armstrong, Robert C.; Shneider, Max S.; Vanderveen, Keith; Ray, Jaideep; Heath, Zach; Allan, Benjamin A.
2008-08-01
This report presents the results of our efforts to apply high-performance computing to entity-based simulations with a multi-use plugin for parallel computing. We use the term 'Entity-based simulation' to describe a class of simulation which includes both discrete event simulation and agent based simulation. What simulations of this class share, and what differs from more traditional models, is that the result sought is emergent from a large number of contributing entities. Logistic, economic and social simulations are members of this class where things or people are organized or self-organize to produce a solution. Entity-based problems never have an a priori ergodic principle that will greatly simplify calculations. Because the results of entity-based simulations can only be realized at scale, scalable computing is de rigueur for large problems. Having said that, the absence of a spatial organizing principal makes the decomposition of the problem onto processors problematic. In addition, practitioners in this domain commonly use the Java programming language which presents its own problems in a high-performance setting. The plugin we have developed, called the Parallel Particle Data Model, overcomes both of these obstacles and is now being used by two Sandia frameworks: the Decision Analysis Center, and the Seldon social simulation facility. While the ability to engage U.S.-sized problems is now available to the Decision Analysis Center, this plugin is central to the success of Seldon. Because Seldon relies on computationally intensive cognitive sub-models, this work is necessary to achieve the scale necessary for realistic results. With the recent upheavals in the financial markets, and the inscrutability of terrorist activity, this simulation domain will likely need a capability with ever greater fidelity. High-performance computing will play an important part in enabling that greater fidelity.
Parallel Environment for Quantum Computing
NASA Astrophysics Data System (ADS)
Tabakin, Frank; Diaz, Bruno Julia
2009-03-01
To facilitate numerical study of noise and decoherence in QC algorithms,and of the efficacy of error correction schemes, we have developed a Fortran 90 quantum computer simulator with parallel processing capabilities. It permits rapid evaluation of quantum algorithms for a large number of qubits and for various ``noise'' scenarios. State vectors are distributed over many processors, to employ a large number of qubits. Parallel processing is implemented by the Message-Passing Interface protocol. A description of how to spread the wave function components over many processors, along with how to efficiently describe the action of general one- and two-qubit operators on these state vectors will be delineated.Grover's search and Shor's factoring algorithms with noise will be discussed as examples. A major feature of this work is that concurrent versions of the algorithms can be evaluated with each version subject to diverse noise effects, corresponding to solving a stochastic Schrodinger equation. The density matrix for the ensemble of such noise cases is constructed using parallel distribution methods to evaluate its associated entropy. Applications of this powerful tool is made to delineate the stability and correction of QC processes using Hamiltonian based dynamics.
Exploring HPCS Languages in Scientific Computing
Barrett, Richard F; Alam, Sadaf R; de Almeida, Valmor F; Bernholdt, David E; Elwasif, Wael R; Kuehn, Jeffery A; Poole, Stephen W; Shet, Aniruddha G
2008-01-01
As computers scale up dramatically to tens and hundreds of thousands of cores, develop deeper computational and memory hierarchies, and increased heterogeneity, developers of scientific software are increasingly challenged to express complex parallel simulations effectively and efficiently. In this paper, we explore the three languages developed under the DARPA High-Productivity Computing Systems (HPCS) program to help address these concerns: Chapel, Fortress, and X10. These languages provide a variety of features not found in currently popular HPC programming environments and make it easier to express powerful computational constructs, leading to new ways of thinking about parallel programming. Though the languages and their implementations are not yet mature enough for a comprehensive evaluation, we discuss some of the important features, and provide examples of how they can be used in scientific computing. We believe that these characteristics will be important to the future of high-performance scientific computing, whether the ultimate language of choice is one of the HPCS languages or something else.
An Expert Assistant for Computer Aided Parallelization
NASA Technical Reports Server (NTRS)
Jost, Gabriele; Chun, Robert; Jin, Haoqiang; Labarta, Jesus; Gimenez, Judit
2004-01-01
The prototype implementation of an expert system was developed to assist the user in the computer aided parallelization process. The system interfaces to tools for automatic parallelization and performance analysis. By fusing static program structure information and dynamic performance analysis data the expert system can help the user to filter, correlate, and interpret the data gathered by the existing tools. Sections of the code that show poor performance and require further attention are rapidly identified and suggestions for improvements are presented to the user. In this paper we describe the components of the expert system and discuss its interface to the existing tools. We present a case study to demonstrate the successful use in full scale scientific applications.
Parallel Computing for Brain Simulation.
Pastur-Romay, L A; Porto-Pazos, A B; Cedron, F; Pazos, A
2017-01-01
The human brain is the most complex system in the known universe, it is therefore one of the greatest mysteries. It provides human beings with extraordinary abilities. However, until now it has not been understood yet how and why most of these abilities are produced. For decades, researchers have been trying to make computers reproduce these abilities, focusing on both understanding the nervous system and, on processing data in a more efficient way than before. Their aim is to make computers process information similarly to the brain. Important technological developments and vast multidisciplinary projects have allowed creating the first simulation with a number of neurons similar to that of a human brain. This paper presents an up-to-date review about the main research projects that are trying to simulate and/or emulate the human brain. They employ different types of computational models using parallel computing: digital models, analog models and hybrid models. This review includes the current applications of these works, as well as future trends. It is focused on various works that look for advanced progress in Neuroscience and still others which seek new discoveries in Computer Science (neuromorphic hardware, machine learning techniques). Their most outstanding characteristics are summarized and the latest advances and future plans are presented. In addition, this review points out the importance of considering not only neurons: Computational models of the brain should also include glial cells, given the proven importance of astrocytes in information processing. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.
Computers and Computation. Readings from Scientific American.
ERIC Educational Resources Information Center
Fenichel, Robert R.; Weizenbaum, Joseph
A collection of articles from "Scientific American" magazine has been put together at this time because the current period in computer science is one of consolidation rather than innovation. A few years ago, computer science was moving so swiftly that even the professional journals were more archival than informative; but today it is…
Accelerating Scientific Discovery Through Computation and Visualization
Sims, James S.; Hagedorn, John G.; Ketcham, Peter M.; Satterfield, Steven G.; Griffin, Terence J.; George, William L.; Fowler, Howland A.; am Ende, Barbara A.; Hung, Howard K.; Bohn, Robert B.; Koontz, John E.; Martys, Nicos S.; Bouldin, Charles E.; Warren, James A.; Feder, David L.; Clark, Charles W.; Filla, B. James; Devaney, Judith E.
2000-01-01
The rate of scientific discovery can be accelerated through computation and visualization. This acceleration results from the synergy of expertise, computing tools, and hardware for enabling high-performance computation, information science, and visualization that is provided by a team of computation and visualization scientists collaborating in a peer-to-peer effort with the research scientists. In the context of this discussion, high performance refers to capabilities beyond the current state of the art in desktop computing. To be effective in this arena, a team comprising a critical mass of talent, parallel computing techniques, visualization algorithms, advanced visualization hardware, and a recurring investment is required to stay beyond the desktop capabilities. This article describes, through examples, how the Scientific Applications and Visualization Group (SAVG) at NIST has utilized high performance parallel computing and visualization to accelerate condensate modeling, (2) fluid flow in porous materials and in other complex geometries, (3) flows in suspensions, (4) x-ray absorption, (5) dielectric breakdown modeling, and (6) dendritic growth in alloys. PMID:27551642
Accelerating Scientific Discovery Through Computation and Visualization.
Sims, J S; Hagedorn, J G; Ketcham, P M; Satterfield, S G; Griffin, T J; George, W L; Fowler, H A; Am Ende, B A; Hung, H K; Bohn, R B; Koontz, J E; Martys, N S; Bouldin, C E; Warren, J A; Feder, D L; Clark, C W; Filla, B J; Devaney, J E
2000-01-01
The rate of scientific discovery can be accelerated through computation and visualization. This acceleration results from the synergy of expertise, computing tools, and hardware for enabling high-performance computation, information science, and visualization that is provided by a team of computation and visualization scientists collaborating in a peer-to-peer effort with the research scientists. In the context of this discussion, high performance refers to capabilities beyond the current state of the art in desktop computing. To be effective in this arena, a team comprising a critical mass of talent, parallel computing techniques, visualization algorithms, advanced visualization hardware, and a recurring investment is required to stay beyond the desktop capabilities. This article describes, through examples, how the Scientific Applications and Visualization Group (SAVG) at NIST has utilized high performance parallel computing and visualization to accelerate condensate modeling, (2) fluid flow in porous materials and in other complex geometries, (3) flows in suspensions, (4) x-ray absorption, (5) dielectric breakdown modeling, and (6) dendritic growth in alloys.
File-access characteristics of parallel scientific workloads
NASA Technical Reports Server (NTRS)
Nieuwejaar, Nils; Kotz, David; Purakayastha, Apratim; Best, Michael; Ellis, Carla Schlatter
1995-01-01
Phenomenal improvements in the computational performance of multiprocessors have not been matched by comparable gains in I/O system performance. This imbalance has resulted in I/O becoming a significant bottleneck for many scientific applications. One key to overcoming this bottleneck is improving the performance of parallel file systems. The design of a high-performance parallel file system requires a comprehensive understanding of the expected workload. Unfortunately, until recently, no general workload studies of parallel file systems have been conducted. The goal of the CHARISMA project was to remedy this problem by characterizing the behavior of several production workloads, on different machines, at the level of individual reads and writes. The first set of results from the CHARISMA project describe the workloads observed on an Intel iPSC/860 and a Thinking Machines CM-5. This paper is intended to compare and contrast these two workloads for an understanding of their essential similarities and differences, isolating common trends and platform-dependent variances. Using this comparison, we are able to gain more insight into the general principles that should guide parallel file-system design.
Parallel Computing Using Web Servers and "Servlets".
ERIC Educational Resources Information Center
Lo, Alfred; Bloor, Chris; Choi, Y. K.
2000-01-01
Describes parallel computing and presents inexpensive ways to implement a virtual parallel computer with multiple Web servers. Highlights include performance measurement of parallel systems; models for using Java and intranet technology including single server, multiple clients and multiple servers, single client; and a comparison of CGI (common…
Broadcasting a message in a parallel computer
Berg, Jeremy E.; Faraj, Ahmad A.
2011-08-02
Methods, systems, and products are disclosed for broadcasting a message in a parallel computer. The parallel computer includes a plurality of compute nodes connected together using a data communications network. The data communications network optimized for point to point data communications and is characterized by at least two dimensions. The compute nodes are organized into at least one operational group of compute nodes for collective parallel operations of the parallel computer. One compute node of the operational group assigned to be a logical root. Broadcasting a message in a parallel computer includes: establishing a Hamiltonian path along all of the compute nodes in at least one plane of the data communications network and in the operational group; and broadcasting, by the logical root to the remaining compute nodes, the logical root's message along the established Hamiltonian path.
Massively parallel I/O: Building an infrastructure for parallel computing
Womble, D.E.; Greenberg, D.S.
1997-04-01
The solution of Grand Challenge Problems will require computations that are too large to fit in the memories of even the largest machines. Inevitably, new designs of I/O systems will be necessary to support them. This report describes the work in investigating I/O subsystems for massively parallel computers. Specifically, the authors investigated out-of-core algorithms for common scientific calculations present several theoretical results. They also describe several approaches to parallel I/O, including partitioned secondary storage and choreographed I/O, and the implications of each to massively parallel computing.
Enabling Computational Technologies for Terascale Scientific Simulations
Ashby, S.F.
2000-08-24
We develop scalable algorithms and object-oriented code frameworks for terascale scientific simulations on massively parallel processors (MPPs). Our research in multigrid-based linear solvers and adaptive mesh refinement enables Laboratory programs to use MPPs to explore important physical phenomena. For example, our research aids stockpile stewardship by making practical detailed 3D simulations of radiation transport. The need to solve large linear systems arises in many applications, including radiation transport, structural dynamics, combustion, and flow in porous media. These systems result from discretizations of partial differential equations on computational meshes. Our first research objective is to develop multigrid preconditioned iterative methods for such problems and to demonstrate their scalability on MPPs. Scalability describes how total computational work grows with problem size; it measures how effectively additional resources can help solve increasingly larger problems. Many factors contribute to scalability: computer architecture, parallel implementation, and choice of algorithm. Scalable algorithms have been shown to decrease simulation times by several orders of magnitude.
Parallel computation of invariant measures
Ding, J.; Liu, Y.
1995-12-01
A parallel numerical algorithm for computing invariant measures is presented. Let I{sup N} {triple_bond} [0,1]{sup N} be the unit N-cube in R N and let S : I{sup N}{r_arrow} I{sup N} be a nonsingular transformation, that is, S is Borel-measurable and m(A) = 0 implies m(S{sup -1}(A)) = 0, where m is the Lebesgue measure. The motivation of this study is the parallel computation of an absolutely continuous invariant measure {mu} under S, that is, {mu} {much_lt} m and {mu}(A) = {mu}(S{sup -1}(A)) for all Borel sets A {contained_in} I{sup N}. It is well-known that an absolutely continuous finite invariant measure {mu} can be obtained by computing a fixed density of the Frobenius-Perron operator Ps: L{sup 1} (I{sup N}) {r_arrow} L{sup 1}(I{sup N}) associated with S which is defined by (1) {integral}{sub A} P{sub S}fdm = {integral}{sub s-1(A)} fdm, {forall}f {element_of} L{sup 1} (I{sup N}). Using any suitable discretization scheme, the infinite dimensional eigenvector problem P{sub S}f = f in L{sup 1}(I{sup N}) can be approximated by an algebraic eigenvector problem P{sub l}f{sub l} = f{sub l} in {gradient}{sub l}, where P{sub l} is a finite approximation of P{sub s} associated with a finite element subspace {gradient}{sub l} of L{sup l} (I{sup N}) {intersection} L{sup {infinity}} (I{sup N}). It has been shown that for P{sub l} arising from Galerkin`s projection principle or the Markov finite approximation principle, there always exists a eigenvector f{sub l} to P{sub l}, and that a sequence of normalized eigenvectors (f{sub l}) converges to the density of an absolutely continuous probability invariant measure {mu} for a class of piecewise C{sup 2} expanding maps of I{sup N} under which the existence of {mu} is guaranteed by Gora-Boyarsky`s theorem which is reduced to Lasota-Yorke`s thoerem when N = 1.
Scientific Computing on the Grid
Allen, Gabrielle; Seidel, Edward; Shalf, John
2001-12-12
Computer simulations are becoming increasingly important as the only means for studying and interpreting the complex processes of nature. Yet the scope and accuracy of these simulations are severely limited by available computational power, even using today's most powerful supercomputers. As we endeavor to simulate the true complexity of nature, we will require much larger scale calculations than are possible at present. Such dynamic and large scale applications will require computational grids and grids require development of new latency tolerant algorithms, and sophisticated code frameworks like Cactus to carry out more complex and high fidelity simulations with a massive degree of parallelism.
Comparisons of some large scientific computers
NASA Technical Reports Server (NTRS)
Credeur, K. R.
1981-01-01
In 1975, the National Aeronautics and Space Administration (NASA) began studies to assess the technical and economic feasibility of developing a computer having sustained computational speed of one billion floating point operations per second and a working memory of at least 240 million words. Such a powerful computer would allow computational aerodynamics to play a major role in aeronautical design and advanced fluid dynamics research. Based on favorable results from these studies, NASA proceeded with developmental plans. The computer was named the Numerical Aerodynamic Simulator (NAS). To help insure that the estimated cost, schedule, and technical scope were realistic, a brief study was made of past large scientific computers. Large discrepancies between inception and operation in scope, cost, or schedule were studied so that they could be minimized with NASA's proposed new compter. The main computers studied were the ILLIAC IV, STAR 100, Parallel Element Processor Ensemble (PEPE), and Shuttle Mission Simulator (SMS) computer. Comparison data on memory and speed were also obtained on the IBM 650, 704, 7090, 360-50, 360-67, 360-91, and 370-195; the CDC 6400, 6600, 7600, CYBER 203, and CYBER 205; CRAY 1; and the Advanced Scientific Computer (ASC). A few lessons learned conclude the report.
A parallel Jacobson-Oksman optimization algorithm. [parallel processing (computers)
NASA Technical Reports Server (NTRS)
Straeter, T. A.; Markos, A. T.
1975-01-01
A gradient-dependent optimization technique which exploits the vector-streaming or parallel-computing capabilities of some modern computers is presented. The algorithm, derived by assuming that the function to be minimized is homogeneous, is a modification of the Jacobson-Oksman serial minimization method. In addition to describing the algorithm, conditions insuring the convergence of the iterates of the algorithm and the results of numerical experiments on a group of sample test functions are presented. The results of these experiments indicate that this algorithm will solve optimization problems in less computing time than conventional serial methods on machines having vector-streaming or parallel-computing capabilities.
Parallel computational fluid dynamics - Implementations and results
NASA Technical Reports Server (NTRS)
Simon, Horst D. (Editor)
1992-01-01
The present volume on parallel CFD discusses implementations on parallel machines, numerical algorithms for parallel CFD, and performance evaluation and computer science issues. Attention is given to a parallel algorithm for compressible flows through rotor-stator combinations, a massively parallel Euler solver for unstructured grids, a fast scheme to analyze 3D disk airflow on a parallel computer, and a block implicit multigrid solution of the Euler equations. Topics addressed include a 3D ADI algorithm on distributed memory multiprocessors, clustered element-by-element computations for fluid flow, hypercube FFT and the Fourier pseudospectral method, and an investigation of parallel iterative algorithms for CFD. Also discussed are fluid dynamics using interface methods on parallel processors, sorting for particle flow simulation on the connection machine, a large grain mapping method, and efforts toward a Teraflops capability for CFD.
Implementing clips on a parallel computer
NASA Technical Reports Server (NTRS)
Riley, Gary
1987-01-01
The C language integrated production system (CLIPS) is a forward chaining rule based language to provide training and delivery for expert systems. Conceptually, rule based languages have great potential for benefiting from the inherent parallelism of the algorithms that they employ. During each cycle of execution, a knowledge base of information is compared against a set of rules to determine if any rules are applicable. Parallelism also can be employed for use with multiple cooperating expert systems. To investigate the potential benefits of using a parallel computer to speed up the comparison of facts to rules in expert systems, a parallel version of CLIPS was developed for the FLEX/32, a large grain parallel computer. The FLEX implementation takes a macroscopic approach in achieving parallelism by splitting whole sets of rules among several processors rather than by splitting the components of an individual rule among processors. The parallel CLIPS prototype demonstrates the potential advantages of integrating expert system tools with parallel computers.
Not Available
1994-02-02
This report consists of three separate but related reports. They are (1) Human Resource Development, (2) Carbon-based Structural Materials Research Cluster, and (3) Data Parallel Algorithms for Scientific Computing. To meet the objectives of the Human Resource Development plan, the plan includes K--12 enrichment activities, undergraduate research opportunities for students at the state`s two Historically Black Colleges and Universities, graduate research through cluster assistantships and through a traineeship program targeted specifically to minorities, women and the disabled, and faculty development through participation in research clusters. One research cluster is the chemistry and physics of carbon-based materials. The objective of this cluster is to develop a self-sustaining group of researchers in carbon-based materials research within the institutions of higher education in the state of West Virginia. The projects will involve analysis of cokes, graphites and other carbons in order to understand the properties that provide desirable structural characteristics including resistance to oxidation, levels of anisotropy and structural characteristics of the carbons themselves. In the proposed cluster on parallel algorithms, research by four WVU faculty and three state liberal arts college faculty are: (1) modeling of self-organized critical systems by cellular automata; (2) multiprefix algorithms and fat-free embeddings; (3) offline and online partitioning of data computation; and (4) manipulating and rendering three dimensional objects. This cluster furthers the state Experimental Program to Stimulate Competitive Research plan by building on existing strengths at WVU in parallel algorithms.
Template based parallel checkpointing in a massively parallel computer system
Archer, Charles Jens; Inglett, Todd Alan
2009-01-13
A method and apparatus for a template based parallel checkpoint save for a massively parallel super computer system using a parallel variation of the rsync protocol, and network broadcast. In preferred embodiments, the checkpoint data for each node is compared to a template checkpoint file that resides in the storage and that was previously produced. Embodiments herein greatly decrease the amount of data that must be transmitted and stored for faster checkpointing and increased efficiency of the computer system. Embodiments are directed to a parallel computer system with nodes arranged in a cluster with a high speed interconnect that can perform broadcast communication. The checkpoint contains a set of actual small data blocks with their corresponding checksums from all nodes in the system. The data blocks may be compressed using conventional non-lossy data compression algorithms to further reduce the overall checkpoint size.
Reservoir Thermal Recover Simulation on Parallel Computers
NASA Astrophysics Data System (ADS)
Li, Baoyan; Ma, Yuanle
The rapid development of parallel computers has provided a hardware background for massive refine reservoir simulation. However, the lack of parallel reservoir simulation software has blocked the application of parallel computers on reservoir simulation. Although a variety of parallel methods have been studied and applied to black oil, compositional, and chemical model numerical simulations, there has been limited parallel software available for reservoir simulation. Especially, the parallelization study of reservoir thermal recovery simulation has not been fully carried out, because of the complexity of its models and algorithms. The authors make use of the message passing interface (MPI) standard communication library, the domain decomposition method, the block Jacobi iteration algorithm, and the dynamic memory allocation technique to parallelize their serial thermal recovery simulation software NUMSIP, which is being used in petroleum industry in China. The parallel software PNUMSIP was tested on both IBM SP2 and Dawn 1000A distributed-memory parallel computers. The experiment results show that the parallelization of I/O has great effects on the efficiency of parallel software PNUMSIP; the data communication bandwidth is also an important factor, which has an influence on software efficiency. Keywords: domain decomposition method, block Jacobi iteration algorithm, reservoir thermal recovery simulation, distributed-memory parallel computer
Heisenberg: Paralleling Scientific and Historical Methods
NASA Astrophysics Data System (ADS)
Cofield, Calla
2007-04-01
Werner Heisenberg is an important historical subject within the physics community partly because his actions as a human being are discussed nearly as often as his work as a physicist. But does the scientific community establish it's historical ideas with the same methods and standards as it's scientific conclusions? I interviewed Heisenberg's son, Jochen Heisenberg, a professor of physics at UNH. Despite a great amount of literature on Werner Heisenberg, only one historian has interviewed Jochen about his father and few have interviewed Werner's wife. Nature is mysterious and unpredictable, but it doesn't lie or distort like humans, and we believe it can give ``honest'' results. But are we keeping the same standards with history that we do with science? Are we holding historians to these standards and if not, is it up to scientists to not only be keepers of scientific understanding, but historical understanding as well? Shouldn't we record history by using the scientific method, by weighing the best sources of data differently than the less reliable, and are we right to be as stubborn about changing our views on history as we are about changing our views on nature?
Sequential and Parallel Matrix Computations.
1985-11-01
Theory" published by the American Math Society. (C) Jointly with A. Sameh of University of Illinois, a parallel algorithm for the single-input pole...an M.Sc. thesis at Northern Illinois University by Ava Chun and, the results were compared with parallel Q-R algorithm of Sameh and Kuck and the
The BLAZE language: A parallel language for scientific programming
NASA Technical Reports Server (NTRS)
Mehrotra, P.; Vanrosendale, J.
1985-01-01
A Pascal-like scientific programming language, Blaze, is described. Blaze contains array arithmetic, forall loops, and APL-style accumulation operators, which allow natural expression of fine grained parallelism. It also employs an applicative or functional procedure invocation mechanism, which makes it easy for compilers to extract coarse grained parallelism using machine specific program restructuring. Thus Blaze should allow one to achieve highly parallel execution on multiprocessor architectures, while still providing the user with onceptually sequential control flow. A central goal in the design of Blaze is portability across a broad range of parallel architectures. The multiple levels of parallelism present in Blaze code, in principle, allow a compiler to extract the types of parallelism appropriate for the given architecture while neglecting the remainder. The features of Blaze are described and shows how this language would be used in typical scientific programming.
The BLAZE language - A parallel language for scientific programming
NASA Technical Reports Server (NTRS)
Mehrotra, Piyush; Van Rosendale, John
1987-01-01
A Pascal-like scientific programming language, BLAZE, is described. BLAZE contains array arithmetic, forall loops, and APL-style accumulation operators, which allow natural expression of fine grained parallelism. It also employs an applicative or functional procedure invocation mechanism, which makes it easy for compilers to extract coarse grained parallelism using machine specific program restructuring. Thus BLAZE should allow one to achieve highly parallel execution on multiprocessor architectures, while still providing the user with conceptually sequential control flow. A central goal in the design of BLAZE is portability across a broad range of parallel architectures. The multiple levels of parallelism present in BLAZE code, in principle, allow a compiler to extract the types of parallelism appropriate for the given architecture while neglecting the remainder. The features of BLAZE are described and it is shown how this language would be used in typical scientific programming.
The BLAZE language - A parallel language for scientific programming
NASA Technical Reports Server (NTRS)
Mehrotra, Piyush; Van Rosendale, John
1987-01-01
A Pascal-like scientific programming language, BLAZE, is described. BLAZE contains array arithmetic, forall loops, and APL-style accumulation operators, which allow natural expression of fine grained parallelism. It also employs an applicative or functional procedure invocation mechanism, which makes it easy for compilers to extract coarse grained parallelism using machine specific program restructuring. Thus BLAZE should allow one to achieve highly parallel execution on multiprocessor architectures, while still providing the user with conceptually sequential control flow. A central goal in the design of BLAZE is portability across a broad range of parallel architectures. The multiple levels of parallelism present in BLAZE code, in principle, allow a compiler to extract the types of parallelism appropriate for the given architecture while neglecting the remainder. The features of BLAZE are described and it is shown how this language would be used in typical scientific programming.
Remarks on parallel computations in MATLAB environment
NASA Astrophysics Data System (ADS)
Opalska, Katarzyna; Opalski, Leszek
2013-10-01
The paper attempts to summarize author's investigation of parallel computation capability of MATLAB environment in solving large ordinary differential equations (ODEs). Two MATLAB versions were tested and two parallelization techniques: one used multiple processors-cores, the other - CUDA compatible Graphics Processing Units (GPUs). A set of parameterized test problems was specially designed to expose different capabilities/limitations of the different variants of the parallel computation environment tested. Presented results illustrate clearly the superiority of the newer MATLAB version and, elapsed time advantage of GPU-parallelized computations for large dimensionality problems over the multiple processor-cores (with speed-up factor strongly dependent on the problem structure).
Parallel computations and control of adaptive structures
NASA Technical Reports Server (NTRS)
Park, K. C.; Alvin, Kenneth F.; Belvin, W. Keith; Chong, K. P. (Editor); Liu, S. C. (Editor); Li, J. C. (Editor)
1991-01-01
The equations of motion for structures with adaptive elements for vibration control are presented for parallel computations to be used as a software package for real-time control of flexible space structures. A brief introduction of the state-of-the-art parallel computational capability is also presented. Time marching strategies are developed for an effective use of massive parallel mapping, partitioning, and the necessary arithmetic operations. An example is offered for the simulation of control-structure interaction on a parallel computer and the impact of the approach presented for applications in other disciplines than aerospace industry is assessed.
Data-parallel algorithms for image computing
NASA Astrophysics Data System (ADS)
Carlotto, Mark J.
1990-11-01
Data-parallel algorithms for image computing on the Connection Machine are described. After a brief review of some basic programming concepts in *Lip, a parallel extension of Common Lisp, data-parallel programming paradigms based on a local (diffusion-like) model of computation, the scan model of computation, a general interprocessor communications model, and a region-based model are introduced. Algorithms for connected component labeling, distance transformation, Voronoi diagrams, finding minimum cost paths, local means, shape-from-shading, hidden surface calculations, affine transformation, oblique parallel projection, and spatial operations over regions are presented. An new algorithm for interpolating irregularly spaced data via Voronoi diagrams is also described.
Parallel and Distributed Computing Combinatorial Algorithms
1993-10-01
FUPNDKC %2,•, PARALLEL AND DISTRIBUTED COMPUTING COMBINATORIAL ALGORITHMS 6. AUTHOR(S) 2304/DS F49620-92-J-0125 DR. LEIGHTON 7 PERFORMING ORGANIZATION NAME...on several problems involving parallel and distributed computing and combinatorial optimization. This research is reported in the numerous papers that...network decom- position. In Proceedings of the Eleventh Annual ACM Symposium on Principles of Distributed Computing , August 1992. [15] B. Awerbuch, B
Molecular dynamics on hypercube parallel computers
NASA Astrophysics Data System (ADS)
Smith, W.
1991-03-01
The implementation of molecular dynamics on parallel computers is described, with particular reference to hypercube computers. Three particular algorithms are described: replicated data (RD); systolic loop (SLS-G), and parallelised link-cells (PLC), all of which have good load balancing. The performance characteristics of each algorithm and the factors affecting their scaling properties are discussed. The article is pedagogic in intent, to introduce a novice to the main aspects of parallel computing in molecular dynamics.
A Parallel Computational Fluid Dynamics Unstructured Grid Generator
1993-12-01
Vol 11. 953-961. Philadelphia: SIAM, 1993. Holey, J. Andrew and Oscar H. Ibarra . "Triangulation, Veronoi Diagram, and Convex Hull in k-Space on Mesh...rIdhner, Rainald, Jose Camberos, and Marshall Merriam. "Parallel Unstructured Grid Generation," in Unstructured Scientific Computation on Scalable
A Parallel Quantum Computer Simulator
2016-09-01
The unique principles of quantum mechanics may one day enable computers to perform operations that would be impossible on a classical computer...Although no one knows whether it will be possible to build a large-scale, functional, and stable quantum computer, researchers can study quantum- mechanical
Sequential and Parallel Matrix Computations.
1984-10-01
value decomposition and learnt square solutions, Numer. Math. 14 (1970), 403-420. 22o J. Greer and A. Sameh , On certain parallel Toeplitz linear system...Zur Stabilitatsfrag bei Matrizen-EigenweCe-Problemn, Z. Angun. Hath. Phys. (1956). 473-500. 36. D. L. Slotnick and A. H. Sameh , Numerical calculation
Collectively loading an application in a parallel computer
Aho, Michael E.; Attinella, John E.; Gooding, Thomas M.; Miller, Samuel J.; Mundy, Michael B.
2016-01-05
Collectively loading an application in a parallel computer, the parallel computer comprising a plurality of compute nodes, including: identifying, by a parallel computer control system, a subset of compute nodes in the parallel computer to execute a job; selecting, by the parallel computer control system, one of the subset of compute nodes in the parallel computer as a job leader compute node; retrieving, by the job leader compute node from computer memory, an application for executing the job; and broadcasting, by the job leader to the subset of compute nodes in the parallel computer, the application for executing the job.
Charon Message-Passing Toolkit for Scientific Computations
NASA Technical Reports Server (NTRS)
VanderWijngarrt, Rob F.; Saini, Subhash (Technical Monitor)
1998-01-01
The Charon toolkit for piecemeal development of high-efficiency parallel programs for scientific computing is described. The portable toolkit, callable from C and Fortran, provides flexible domain decompositions and high-level distributed constructs for easy translation of serial legacy code or design to distributed environments. Gradual tuning can subsequently be applied to obtain high performance, possibly by using explicit message passing. Charon also features general structured communications that support stencil-based computations with complex recurrences. Through the separation of partitioning and distribution, the toolkit can also be used for blocking of uni-processor code, and for debugging of parallel algorithms on serial machines. An elaborate review of recent parallelization aids is presented to highlight the need for a toolkit like Charon. Some performance results of parallelizing the NAS Parallel Benchmark SP program using Charon are given, showing good scalability. Some performance results of parallelizing the NAS Parallel Benchmark SP program using Charon are given, showing good scalability.
Interconnections For Stacked Parallel Computer Modules
NASA Technical Reports Server (NTRS)
Johannesson, Richard T.
1996-01-01
Concept for interconnecting modules in parallel computers leads to cheaper, smaller, lighter, lower-power computing systems for aerospace, industrial, business, and consumer applications. Computer modules stacked and interconnected in various configurations. Connections among stacks controlled by switching within gateways and/or by addresses on buses.
Parallel Computing Strategies for Irregular Algorithms
NASA Technical Reports Server (NTRS)
Biswas, Rupak; Oliker, Leonid; Shan, Hongzhang; Biegel, Bryan (Technical Monitor)
2002-01-01
Parallel computing promises several orders of magnitude increase in our ability to solve realistic computationally-intensive problems, but relies on their efficient mapping and execution on large-scale multiprocessor architectures. Unfortunately, many important applications are irregular and dynamic in nature, making their effective parallel implementation a daunting task. Moreover, with the proliferation of parallel architectures and programming paradigms, the typical scientist is faced with a plethora of questions that must be answered in order to obtain an acceptable parallel implementation of the solution algorithm. In this paper, we consider three representative irregular applications: unstructured remeshing, sparse matrix computations, and N-body problems, and parallelize them using various popular programming paradigms on a wide spectrum of computer platforms ranging from state-of-the-art supercomputers to PC clusters. We present the underlying problems, the solution algorithms, and the parallel implementation strategies. Smart load-balancing, partitioning, and ordering techniques are used to enhance parallel performance. Overall results demonstrate the complexity of efficiently parallelizing irregular algorithms.
Parallel computation with the spectral element method
Ma, Hong
1995-12-01
Spectral element models for the shallow water equations and the Navier-Stokes equations have been successfully implemented on a data parallel supercomputer, the Connection Machine model CM-5. The nonstaggered grid formulations for both models are described, which are shown to be especially efficient in data parallel computing environment.
Parallel unstructured grid generation for computational aerosciences
NASA Technical Reports Server (NTRS)
Shephard, Mark S.
1993-01-01
The objective of this research project is to develop efficient parallel automatic grid generation procedures for use in computational aerosciences. This effort is focused on a parallel version of the Finite Octree grid generator. Progress made during the first six months is reported.
Computer architecture and parallel processing
Hwang, K.; Faye, A.
1984-01-01
The book is intended as a text to support two semesters of courses in computer architecture at the college senior and graduate levels. There are excellent problems for students at the end of each chapter. The authors have divided the use of computers into the following four levels of sophistication: data processing, information processing, knowledge processing, and intelligence processing.
Massively Parallel Computing: A Sandia Perspective
Dosanjh, Sudip S.; Greenberg, David S.; Hendrickson, Bruce; Heroux, Michael A.; Plimpton, Steve J.; Tomkins, James L.; Womble, David E.
1999-05-06
The computing power available to scientists and engineers has increased dramatically in the past decade, due in part to progress in making massively parallel computing practical and available. The expectation for these machines has been great. The reality is that progress has been slower than expected. Nevertheless, massively parallel computing is beginning to realize its potential for enabling significant break-throughs in science and engineering. This paper provides a perspective on the state of the field, colored by the authors' experiences using large scale parallel machines at Sandia National Laboratories. We address trends in hardware, system software and algorithms, and we also offer our view of the forces shaping the parallel computing industry.
Fast combinatorial optimization with parallel digital computers.
Kakeya, H; Okabe, Y
2000-01-01
This paper presents an algorithm which realizes fast search for the solutions of combinatorial optimization problems with parallel digital computers.With the standard weight matrices designed for combinatorial optimization, many iterations are required before convergence to a quasioptimal solution even when many digital processors can be used in parallel. By removing the components of the eingenvectors with eminent negative eigenvalues of the weight matrix, the proposed algorithm avoids oscillation and realizes energy reduction under synchronous discrete dynamics, which enables parallel digital computers to obtain quasi-optimal solutions with much less time than the conventional algorithm.
Parallel and Distributed Computational Fluid Dynamics: Experimental Results and Challenges
NASA Technical Reports Server (NTRS)
Djomehri, Mohammad Jahed; Biswas, R.; VanderWijngaart, R.; Yarrow, M.
2000-01-01
This paper describes several results of parallel and distributed computing using a large scale production flow solver program. A coarse grained parallelization based on clustering of discretization grids combined with partitioning of large grids for load balancing is presented. An assessment is given of its performance on distributed and distributed-shared memory platforms using large scale scientific problems. An experiment with this solver, adapted to a Wide Area Network execution environment is presented. We also give a comparative performance assessment of computation and communication times on both the tightly and loosely-coupled machines.
Adaptive Explicitly Parallel Instruction Computing
2000-12-16
1993. [17] James F. Blinn. Jim Blinn’s corner: Fugue for MMX. IEEE Computer Graphics and Applications, 17(2):88– 93, March/April 1997. Makes several...processors. IEEE Transactions on Computers, C-29(4):308–316, April 1980. [22] Doug Burger and James R. Goodman. Guest editors introduction: Billion...sequencing and scheduling: A survey. Ann. Discrete Mathematics, 5:287–326, 1979. [58] C. Ebeling D. C. Green and P. Franklin . RaPiD – reconfigurable
IPython: components for interactive and parallel computing across disciplines. (Invited)
NASA Astrophysics Data System (ADS)
Perez, F.; Bussonnier, M.; Frederic, J. D.; Froehle, B. M.; Granger, B. E.; Ivanov, P.; Kluyver, T.; Patterson, E.; Ragan-Kelley, B.; Sailer, Z.
2013-12-01
Scientific computing is an inherently exploratory activity that requires constantly cycling between code, data and results, each time adjusting the computations as new insights and questions arise. To support such a workflow, good interactive environments are critical. The IPython project (http://ipython.org) provides a rich architecture for interactive computing with: 1. Terminal-based and graphical interactive consoles. 2. A web-based Notebook system with support for code, text, mathematical expressions, inline plots and other rich media. 3. Easy to use, high performance tools for parallel computing. Despite its roots in Python, the IPython architecture is designed in a language-agnostic way to facilitate interactive computing in any language. This allows users to mix Python with Julia, R, Octave, Ruby, Perl, Bash and more, as well as to develop native clients in other languages that reuse the IPython clients. In this talk, I will show how IPython supports all stages in the lifecycle of a scientific idea: 1. Individual exploration. 2. Collaborative development. 3. Production runs with parallel resources. 4. Publication. 5. Education. In particular, the IPython Notebook provides an environment for "literate computing" with a tight integration of narrative and computation (including parallel computing). These Notebooks are stored in a JSON-based document format that provides an "executable paper": notebooks can be version controlled, exported to HTML or PDF for publication, and used for teaching.
Computer-Aided Parallelizer and Optimizer
NASA Technical Reports Server (NTRS)
Jin, Haoqiang
2011-01-01
The Computer-Aided Parallelizer and Optimizer (CAPO) automates the insertion of compiler directives (see figure) to facilitate parallel processing on Shared Memory Parallel (SMP) machines. While CAPO currently is integrated seamlessly into CAPTools (developed at the University of Greenwich, now marketed as ParaWise), CAPO was independently developed at Ames Research Center as one of the components for the Legacy Code Modernization (LCM) project. The current version takes serial FORTRAN programs, performs interprocedural data dependence analysis, and generates OpenMP directives. Due to the widely supported OpenMP standard, the generated OpenMP codes have the potential to run on a wide range of SMP machines. CAPO relies on accurate interprocedural data dependence information currently provided by CAPTools. Compiler directives are generated through identification of parallel loops in the outermost level, construction of parallel regions around parallel loops and optimization of parallel regions, and insertion of directives with automatic identification of private, reduction, induction, and shared variables. Attempts also have been made to identify potential pipeline parallelism (implemented with point-to-point synchronization). Although directives are generated automatically, user interaction with the tool is still important for producing good parallel codes. A comprehensive graphical user interface is included for users to interact with the parallelization process.
On evaluating parallel computer systems
NASA Technical Reports Server (NTRS)
Adams, George B., III; Brown, Robert L.; Denning, Peter J.
1985-01-01
A workshop was held in an attempt to program real problems on the MIT Static Data Flow Machine. Most of the architecture of the machine was specified but some parts were incomplete. The main purpose for the workshop was to explore principles for the evaluation of computer systems employing new architectures. Principles explored were: (1) evaluation must be an integral, ongoing part of a project to develop a computer of radically new architecture; (2) the evaluation should seek to measure the usability of the system as well as its performance; (3) users from the application domains must be an integral part of the evaluation process; and (4) evaluation results should be fed back into the design process. It is concluded that the general organizational principles are achievable in practice from this workshop.
Parallel Algorithms for Computer Vision
1990-04-01
Tikhonov and V. Y. Arsenin . Solutions of Ill - posed Problems . W.H.Winston, Washington, D.C., 1977 . [661 V. Torre... Ill - posed problems in early vision. Proceedings of the IEEE, 76:869-889, 1988. [4] J. Besag. Spatial interaction and the statistical analysis of lattice...Cooperative computation of stereo disparity. Science, 194:283-287, 1976. [50] J. Marroquin, S. Mitter, and T. Poggio. Probabilistic solution of ill - posed
Graph Partitioning Models for Parallel Computing
Hendrickson, B.; Kolda, T.G.
1999-03-02
Calculations can naturally be described as graphs in which vertices represent computation and edges reflect data dependencies. By partitioning the vertices of a graph, the calculation can be divided among processors of a parallel computer. However, the standard methodology for graph partitioning minimizes the wrong metric and lacks expressibility. We survey several recently proposed alternatives and discuss their relative merits.
Fast Parallel Computation Of Manipulator Inverse Dynamics
NASA Technical Reports Server (NTRS)
Fijany, Amir; Bejczy, Antal K.
1991-01-01
Method for fast parallel computation of inverse dynamics problem, essential for real-time dynamic control and simulation of robot manipulators, undergoing development. Enables exploitation of high degree of parallelism and, achievement of significant computational efficiency, while minimizing various communication and synchronization overheads as well as complexity of required computer architecture. Universal real-time robotic controller and simulator (URRCS) consists of internal host processor and several SIMD processors with ring topology. Architecture modular and expandable: more SIMD processors added to match size of problem. Operate asynchronously and in MIMD fashion.
Fast Parallel Matrix and GCD Computations.
1982-04-01
complex analysis , Vol. 2, John Wiley & Sons, 1977 0. Ibarra, S. Moran and L.E. Rosier, A note on the parallel complexity of computing che rank of order n...be computed in parallel time O(log 2n) for such a field. They also show that this is true if F is a subfield of C and one is allowed to use complex ...identity and multistep integer- preserving Gaussian elimination, Mlath. Comp. 22(1968), S65-$78. W. Baur, V. Strassen, The computational complexity of
Link failure detection in a parallel computer
Archer, Charles J.; Blocksome, Michael A.; Megerian, Mark G.; Smith, Brian E.
2010-11-09
Methods, apparatus, and products are disclosed for link failure detection in a parallel computer including compute nodes connected in a rectangular mesh network, each pair of adjacent compute nodes in the rectangular mesh network connected together using a pair of links, that includes: assigning each compute node to either a first group or a second group such that adjacent compute nodes in the rectangular mesh network are assigned to different groups; sending, by each of the compute nodes assigned to the first group, a first test message to each adjacent compute node assigned to the second group; determining, by each of the compute nodes assigned to the second group, whether the first test message was received from each adjacent compute node assigned to the first group; and notifying a user, by each of the compute nodes assigned to the second group, whether the first test message was received.
Internode data communications in a parallel computer
Archer, Charles J; Blocksome, Michael A; Miller, Douglas R; Parker, Jeffrey J; Ratterman, Joseph D; Smith, Brian E
2014-02-11
Internode data communications in a parallel computer that includes compute nodes that each include main memory and a messaging unit, the messaging unit including computer memory and coupling compute nodes for data communications, in which, for each compute node at compute node boot time: a messaging unit allocates, in the messaging unit's computer memory, a predefined number of message buffers, each message buffer associated with a process to be initialized on the compute node; receives, prior to initialization of a particular process on the compute node, a data communications message intended for the particular process; and stores the data communications message in the message buffer associated with the particular process. Upon initialization of the particular process, the process establishes a messaging buffer in main memory of the compute node and copies the data communications message from the message buffer of the messaging unit into the message buffer of main memory.
Internode data communications in a parallel computer
Archer, Charles J.; Blocksome, Michael A.; Miller, Douglas R.; Parker, Jeffrey J.; Ratterman, Joseph D.; Smith, Brian E.
2013-09-03
Internode data communications in a parallel computer that includes compute nodes that each include main memory and a messaging unit, the messaging unit including computer memory and coupling compute nodes for data communications, in which, for each compute node at compute node boot time: a messaging unit allocates, in the messaging unit's computer memory, a predefined number of message buffers, each message buffer associated with a process to be initialized on the compute node; receives, prior to initialization of a particular process on the compute node, a data communications message intended for the particular process; and stores the data communications message in the message buffer associated with the particular process. Upon initialization of the particular process, the process establishes a messaging buffer in main memory of the compute node and copies the data communications message from the message buffer of the messaging unit into the message buffer of main memory.
Locating hardware faults in a parallel computer
Archer, Charles J.; Megerian, Mark G.; Ratterman, Joseph D.; Smith, Brian E.
2010-04-13
Locating hardware faults in a parallel computer, including defining within a tree network of the parallel computer two or more sets of non-overlapping test levels of compute nodes of the network that together include all the data communications links of the network, each non-overlapping test level comprising two or more adjacent tiers of the tree; defining test cells within each non-overlapping test level, each test cell comprising a subtree of the tree including a subtree root compute node and all descendant compute nodes of the subtree root compute node within a non-overlapping test level; performing, separately on each set of non-overlapping test levels, an uplink test on all test cells in a set of non-overlapping test levels; and performing, separately from the uplink tests and separately on each set of non-overlapping test levels, a downlink test on all test cells in a set of non-overlapping test levels.
Dynamic Load Balancing for Computational Plasticity on Parallel Computers
NASA Technical Reports Server (NTRS)
Pramono, Eddy; Simon, Horst
1994-01-01
The simulation of the computational plasticity on a complex structure remains a formidable computational task, especially when a highly nonlinear, complex material model was used. It appears that the computational requirements for a such problem can only be satisfied by massively parallel architectures. In order to effectively harness the tremendous computational power provided by such architectures, it is imperative to investigate and to study the algorithmic and implementation issues pertaining to dynamic load balancing for computational plasticity on a highly parallel, distributed-memory, multiple-instruction, multiple-data computers. This paper will measure the effectiveness of the algorithms developed in handling the dynamic load balancing.
Parallel Processing for Computational Continuum Dynamics,
1985-01-01
Instruction stream, Multiple Data stream ( MIMD ). An example of a machine of this type is the HEP HIOO computer manu- factured by the Denelcor...parallel architecture in general and for the HEP H1O00 computer in partic- ular. The approach is a step-by-step procedure based on a progression from the...Element Processor) by Denelcor has MIMD architecture. The HEP computer is designed to combine from one up to 16 Process Execu- tion Modules (PEM’s
Parallel computer methods for eigenvalue extraction
NASA Technical Reports Server (NTRS)
Akl, Fred
1988-01-01
A new numerical algorithm for the solution of large-order eigenproblems typically encountered in linear elastic finite element systems is presented. The architecture of parallel processing is used in the algorithm to achieve increased speed and efficiency of calculations. The algorithm is based on the frontal technique for the solution of linear simultaneous equations and the modified subspace eigenanalysis method for the solution of the eigenproblem. The advantages of this new algorithm in parallel computer architecture are discussed.
Fast Parallel Computation Of Multibody Dynamics
NASA Technical Reports Server (NTRS)
Fijany, Amir; Kwan, Gregory L.; Bagherzadeh, Nader
1996-01-01
Constraint-force algorithm fast, efficient, parallel-computation algorithm for solving forward dynamics problem of multibody system like robot arm or vehicle. Solves problem in minimum time proportional to log(N) by use of optimal number of processors proportional to N, where N is number of dynamical degrees of freedom: in this sense, constraint-force algorithm both time-optimal and processor-optimal parallel-processing algorithm.
Toward a science of parallel computation
Worlton, W.J.
1986-01-01
The evolution of parallel processing over the past several decades can be viewed as the development of a new scientific discipline. Parallel processing has been, and is, undergoing the same evolutionary stages that are common to the development of scientific disciplines in general: exploration, focusing, and maturity. That parallel processing is not yet a science can readily be appreciated by its lack of some of the characteristics typical of mature sciences, such as prescriptive terminology, comprehensive taxonomies, and authoritative fundamental principles. A great deal of outstanding work has been done and the field is experiencing the beginnings of its ''focusing'' phase, i.e., support is being concentrated in a set of the more promising approaches selected from among the larger set of exploratory projects. However, the possible set of parallel-processing concepts is so extensive that exploratory work will probably continue for one or two more decades. In the meantime, the growing maturity of the field will be reflected in the increasing clarity and precision of the terminology, the development of systematic classification of the domain of discourse, the development of basic principles, and the growing number of commercial products that are the outcome of the research and development projects on which support is being focused. In this paper we develop some generalizations of taxonomies and use basic principles to draw conclusions about the extensibility of parallel processor architectures. 7 refs., 5 figs., 2 tabs.
High performance parallel computing of flows in complex geometries
NASA Astrophysics Data System (ADS)
Gicquel, Laurent Y. M.; Gourdain, N.; Boussuge, J.-F.; Deniau, H.; Staffelbach, G.; Wolf, P.; Poinsot, Thierry
2011-02-01
Efficient numerical tools taking advantage of the ever increasing power of high-performance computers, become key elements in the fields of energy supply and transportation, not only from a purely scientific point of view, but also at the design stage in industry. Indeed, flow phenomena that occur in or around the industrial applications such as gas turbines or aircraft are still not mastered. In fact, most Computational Fluid Dynamics (CFD) predictions produced today focus on reduced or simplified versions of the real systems and are usually solved with a steady state assumption. This article shows how recent developments of CFD codes and parallel computer architectures can help overcoming this barrier. With this new environment, new scientific and technological challenges can be addressed provided that thousands of computing cores are efficiently used in parallel. Strategies of modern flow solvers are discussed with particular emphases on mesh-partitioning, load balancing and communication. These concepts are used in two CFD codes developed by CERFACS: a multi-block structured code dedicated to aircrafts and turbo-machinery as well as an unstructured code for gas turbine flow predictions. Leading edge computations obtained with these high-end massively parallel CFD codes are illustrated and discussed in the context of aircrafts, turbo-machinery and gas turbine applications. Finally, future developments of CFD and high-end computers are proposed to provide leading edge tools and end applications with strong industrial implications at the design stage of the next generation of aircraft and gas turbines.
Wing-Body Aeroelasticity on Parallel Computers
NASA Technical Reports Server (NTRS)
Guruswamy, Guru P.; Byun, Chansup
1996-01-01
This article presents a procedure for computing the aeroelasticity of wing-body configurations on multiple-instruction, multiple-data parallel computers. In this procedure, fluids are modeled using Euler equations discretized by a finite difference method, and structures are modeled using finite element equations. The procedure is designed in such a way that each discipline can be developed and maintained independently by using a domain decomposition approach. A parallel integration scheme is used to compute aeroelastic responses by solving the coupled fluid and structural equations concurrently while keeping modularity of each discipline. The present procedure is validated by computing the aeroelastic response of a wing and comparing with experiment. Aeroelastic computations are illustrated for a high speed civil transport type wing-body configuration.
Parallel processing for computer vision and display
Dew, P.M. . Dept. of Computer Studies); Earnshaw, R.A. ); Heywood, T.R. )
1989-01-01
The widespread availability of high performance computers has led to an increased awareness of the importance of visualization techniques particularly in engineering and science. However, many visualization tasks involve processing large amounts of data or manipulating complex computer models of 3D objects. For example, in the field of computer aided engineering it is often necessary to display an edit solid object (see Plate 1) which can take many minutes even on the fastest serial processors. Another example of a computationally intensive problem, this time from computer vision, is the recognition of objects in a 3D scene from a stereo image pair. To perform visualization tasks of this type in real and reasonable time it is necessary to exploit the advances in parallel processing that have taken place over the last decade. This book uniquely provides a collection of papers from leading visualization researchers with a common interest in the application and exploitation of parallel processing techniques.
Interfacing Computer Aided Parallelization and Performance Analysis
NASA Technical Reports Server (NTRS)
Jost, Gabriele; Jin, Haoqiang; Labarta, Jesus; Gimenez, Judit; Biegel, Bryan A. (Technical Monitor)
2003-01-01
When porting sequential applications to parallel computer architectures, the program developer will typically go through several cycles of source code optimization and performance analysis. We have started a project to develop an environment where the user can jointly navigate through program structure and performance data information in order to make efficient optimization decisions. In a prototype implementation we have interfaced the CAPO computer aided parallelization tool with the Paraver performance analysis tool. We describe both tools and their interface and give an example for how the interface helps within the program development cycle of a benchmark code.
Parallel computing using a Lagrangian formulation
NASA Technical Reports Server (NTRS)
Liou, May-Fun; Loh, Ching Yuen
1991-01-01
A new Lagrangian formulation of the Euler equation is adopted for the calculation of 2-D supersonic steady flow. The Lagrangian formulation represents the inherent parallelism of the flow field better than the common Eulerian formulation and offers a competitive alternative on parallel computers. The implementation of the Lagrangian formulation on the Thinking Machines Corporation CM-2 Computer is described. The program uses a finite volume, first-order Godunov scheme and exhibits high accuracy in dealing with multidimensional discontinuities (slip-line and shock). By using this formulation, a better than six times speed-up was achieved on a 8192-processor CM-2 over a single processor of a CRAY-2.
A Computing Platform for Parallel Sparse Matrix Computations
2016-01-05
infiniband. Each node contains 24 cores. This parallel computing platform has been used by my research group in the early stages of developing large...sparse linear system and symmetric eigenvalue problem solvers (ARO grant W911NF-07-R-0003-04) that are suitable for parallel architectures containing...hundreds of multicore nodes (thousands of cores). Once our parallel solvers obtain the correct solutions, and perform properly on this 8-node platform
Highly parallel computer architecture for robotic computation
NASA Technical Reports Server (NTRS)
Fijany, Amir (Inventor); Bejczy, Anta K. (Inventor)
1991-01-01
In a computer having a large number of single instruction multiple data (SIMD) processors, each of the SIMD processors has two sets of three individual processor elements controlled by a master control unit and interconnected among a plurality of register file units where data is stored. The register files input and output data in synchronism with a minor cycle clock under control of two slave control units controlling the register file units connected to respective ones of the two sets of processor elements. Depending upon which ones of the register file units are enabled to store or transmit data during a particular minor clock cycle, the processor elements within an SIMD processor are connected in rings or in pipeline arrays, and may exchange data with the internal bus or with neighboring SIMD processors through interface units controlled by respective ones of the two slave control units.
Charon Message-Passing Toolkit for Scientific Computations
NASA Technical Reports Server (NTRS)
VanderWijngaart, Rob F.; Saini, Subhash (Technical Monitor)
1998-01-01
The Charon toolkit for piecemeal development of high-efficiency parallel programs for scientific computing is described. The portable toolkit, callable from C and Fortran, provides flexible domain decompositions and high-level distributed constructs for easy translation of serial legacy code or design to distributed environments. Gradual tuning can subsequently be applied to obtain high performance, possibly by using explicit message passing. Charon also features general structured communications that support stencil-based computations with complex recurrences. Through the separation of partitioning and distribution, the toolkit can also be used for blocking of uni-processor code, and for debugging of parallel algorithms on serial machines. An elaborate review of recent parallelization aids is presented to highlight the need for a toolkit like Charon. Some performance results of parallelizing the NAS Parallel Benchmark SP program using Charon are given, showing good scalability.
Tpetra, and the use of generic programming in scientific computing
Baker, Christopher G; Heroux, Dr. Michael A
2012-01-01
We present Tpetra, a Trilinos package for parallel linear algebra primitives implementing the Petra object model. We describe Tpetra s design, based on generic programming via C++ templated types and template metaprogramming. We discuss some benefits of this approach in the context of scientific computing, with illustrations consisting of code and notable empirical results.
Archer, Charles J; Blocksome, Michael E; Ratterman, Joseph D; Smith, Brian E
2014-02-11
Endpoint-based parallel data processing in a parallel active messaging interface ('PAMI') of a parallel computer, the PAMI composed of data communications endpoints, each endpoint including a specification of data communications parameters for a thread of execution on a compute node, including specifications of a client, a context, and a task, the compute nodes coupled for data communications through the PAMI, including establishing a data communications geometry, the geometry specifying, for tasks representing processes of execution of the parallel application, a set of endpoints that are used in collective operations of the PAMI including a plurality of endpoints for one of the tasks; receiving in endpoints of the geometry an instruction for a collective operation; and executing the instruction for a collective opeartion through the endpoints in dependence upon the geometry, including dividing data communications operations among the plurality of endpoints for one of the tasks.
Archer, Charles J.; Blocksome, Michael A.; Ratterman, Joseph D.; Smith, Brian E.
2014-08-12
Endpoint-based parallel data processing in a parallel active messaging interface (`PAMI`) of a parallel computer, the PAMI composed of data communications endpoints, each endpoint including a specification of data communications parameters for a thread of execution on a compute node, including specifications of a client, a context, and a task, the compute nodes coupled for data communications through the PAMI, including establishing a data communications geometry, the geometry specifying, for tasks representing processes of execution of the parallel application, a set of endpoints that are used in collective operations of the PAMI including a plurality of endpoints for one of the tasks; receiving in endpoints of the geometry an instruction for a collective operation; and executing the instruction for a collective operation through the endpoints in dependence upon the geometry, including dividing data communications operations among the plurality of endpoints for one of the tasks.
Parallel Analysis and Visualization on Cray Compute Node Linux
Pugmire, Dave; Ahern, Sean
2008-01-01
Capability computer systems are deployed to give researchers the computational power required to investigate and solve key challenges facing the scientific community. As the power of these computer systems increases, the computational problem domain typically increases in size, complexity and scope. These increases strain the ability of commodity analysis and visualization clusters to effectively perform post-processing tasks and provide critical insight and understanding to the computed results. An alternative to purchasing increasingly larger, separate analysis and visualization commodity clusters is to use the computational system itself to perform post-processing tasks. In this paper, the recent successful port of VisIt, a parallel, open source analysis and visualization tool, to compute node linux running on the Cray is detailed. Additionally, the unprecedented ability of this resource for analysis and visualization is discussed and a report on obtained results is presented.
Impact of Parallel Computing on Large Scale Aeroelastic Computations
NASA Technical Reports Server (NTRS)
Guruswamy, Guru P.; Kwak, Dochan (Technical Monitor)
2000-01-01
Aeroelasticity is computationally one of the most intensive fields in aerospace engineering. Though over the last three decades the computational speed of supercomputers have substantially increased, they are still inadequate for large scale aeroelastic computations using high fidelity flow and structural equations. In addition to reaching a saturation in computational speed because of changes in economics, computer manufactures are stopping the manufacturing of mainframe type supercomputers. This has led computational aeroelasticians to face the gigantic task of finding alternate approaches for fulfilling their needs. The alternate path to over come speed and availability limitations of mainframe type supercomputers is to use parallel computers. During this decade several different architectures have evolved. In FY92 the US Government started the High Performance Computing and Communication (HPCC) program. As a participant in this program NASA developed several parallel computational tools for aeroelastic applications. This talk describes the impact of those application tools on high fidelity based multidisciplinary analysis.
Parallel Object-Oriented Computation Applied to a Finite Element Problem
NASA Technical Reports Server (NTRS)
Weissman, Jon B.; Grimshaw, Andrew S.; Ferraro, Robert
1993-01-01
The conventional wisdom in the scientific computing community is that the best way to solve large-scale numerically intensive scientific problems on today's parallel MIMD computers is to use Fortran or C programmed in a data-parallel style using low-level message-passing primitives. This approach inevitably leads to nonportable codes, extensive development time, and restricts parallel programming to the domain of the expert programmer. We believe that these problems are not inherent to parallel computing but are the result of the tools used. We will show that comparable performance can be achieved with little effort if better tools that present higher level abstractions are used.
Measuring performance of parallel computers. Final report
Sullivan, F.
1994-07-01
Performance Measurement - the authors have developed a taxonomy of parallel algorithms based on data motion and example applications have been coded for each class of the taxonomy. Computational benchmark kernels have been extracted for several applications, and detailed measurements have been performed. Algorithms for Massively Parallel SIMD machines - measurement results and computational experiences indicate that top performance will be achieved by `iteration` type algorithms running on massively parallel SIMD machines. Reformulation as iteration may entail unorthodox approaches based on probabilistic methods. The authors have developed such methods for some applications. Here they discuss their approach to performance measurement, describe the taxonomy and measurements which have been made, and report on some general conclusions which can be drawn from the results of the measurements.
Computing association probabilities using parallel Boltzmann machines.
Iltis, R A; Ting, P Y
1993-01-01
A new computational method is presented for solving the data association problem using parallel Boltzmann machines. It is shown that the association probabilities can be computed with arbitrarily small errors if a sufficient number of parallel Boltzmann machines are available. The probability beta(i)(j) that the i th measurement emanated from the jth target can be obtained simply by observing the relative frequency with which neuron v(i,j) in a two-dimensional network is on throughout the layers. Some simple tracking examples comparing the performance of the Boltzmann algorithm to the exact data association solution and with the performance of an alternative parallel method using the Hopfield neural network are also presented.
Software development strategies for parallel computer architectures
NASA Astrophysics Data System (ADS)
Gruber, Ralf; Cooper, W. Anthony; Beniston, Martin; Gengler, Marc; Merazzi, Silvio
1991-09-01
As pragmatic users of high performance supercomputers, we believe that nowadays parallel computer architectures with disturbed memories are not yet mature to be used by a wide range of application engineers. A big effort should be made to bring these very promising computers closer to the users. One major flaw of massively parallel machines is that the programmer has to take care himself of the data flow which is often different on different parallel computers. To overcome this problem, we propose that data structures be standardized. The data base then can become an integrated part of the system and the data flow for a given algorithm can be easily prescribed. Fixing data structures forces the computer manufacturer to rather adapt his machine to user's demands and not, as it happens now, the user has to adapt to the innovative computer science approach of the computer manufacturer. In this paper, we present data standards chosen for our ASTRID programming platform for research scientist and engineers, as well as a plasma physics application which won the Cray Gigaflop Performance Awards 1989 and 1990 and which was succesfully ported on an INTEL iPSC/2 hypercube.
FastQuery: A Parallel Indexing System for Scientific Data
Chou, Jerry; Wu, Kesheng; Prabhat,
2011-07-29
Modern scientific datasets present numerous data management and analysis challenges. State-of-the- art index and query technologies such as FastBit can significantly improve accesses to these datasets by augmenting the user data with indexes and other secondary information. However, a challenge is that the indexes assume the relational data model but the scientific data generally follows the array data model. To match the two data models, we design a generic mapping mechanism and implement an efficient input and output interface for reading and writing the data and their corresponding indexes. To take advantage of the emerging many-core architectures, we also develop a parallel strategy for indexing using threading technology. This approach complements our on-going MPI-based parallelization efforts. We demonstrate the flexibility of our software by applying it to two of the most commonly used scientific data formats, HDF5 and NetCDF. We present two case studies using data from a particle accelerator model and a global climate model. We also conducted a detailed performance study using these scientific datasets. The results show that FastQuery speeds up the query time by a factor of 2.5x to 50x, and it reduces the indexing time by a factor of 16 on 24 cores.
Rectilinear partitioning of irregular data parallel computations
NASA Technical Reports Server (NTRS)
Nicol, David M.
1991-01-01
New mapping algorithms for domain oriented data-parallel computations, where the workload is distributed irregularly throughout the domain, but exhibits localized communication patterns are described. Researchers consider the problem of partitioning the domain for parallel processing in such a way that the workload on the most heavily loaded processor is minimized, subject to the constraint that the partition be perfectly rectilinear. Rectilinear partitions are useful on architectures that have a fast local mesh network. Discussed here is an improved algorithm for finding the optimal partitioning in one dimension, new algorithms for partitioning in two dimensions, and optimal partitioning in three dimensions. The application of these algorithms to real problems are discussed.
Unsteady flow simulation on a parallel computer
NASA Astrophysics Data System (ADS)
Faden, M.; Pokorny, S.; Engel, K.
For the simulation of the flow through compressor stages, an interactive flow simulation system is set up on an MIMD-type parallel computer. An explicit scheme is used in order to resolve the time-dependent interaction between the blades. The 2D Navier-Stokes equations are transformed into their general moving coordinates. The parallelization of the solver is based on the idea of domain decomposition. Results are presented for a problem of fixed size (4096 grid nodes for the Hakkinen case).
Intranode data communications in a parallel computer
Archer, Charles J; Blocksome, Michael A; Miller, Douglas R; Ratterman, Joseph D; Smith, Brian E
2013-07-23
Intranode data communications in a parallel computer that includes compute nodes configured to execute processes, where the data communications include: allocating, upon initialization of a first process of a compute node, a region of shared memory; establishing, by the first process, a predefined number of message buffers, each message buffer associated with a process to be initialized on the compute node; sending, to a second process on the same compute node, a data communications message without determining whether the second process has been initialized, including storing the data communications message in the message buffer of the second process; and upon initialization of the second process: retrieving, by the second process, a pointer to the second process's message buffer; and retrieving, by the second process from the second process's message buffer in dependence upon the pointer, the data communications message sent by the first process.
Intranode data communications in a parallel computer
Archer, Charles J; Blocksome, Michael A; Miller, Douglas R; Ratterman, Joseph D; Smith, Brian E
2014-01-07
Intranode data communications in a parallel computer that includes compute nodes configured to execute processes, where the data communications include: allocating, upon initialization of a first process of a computer node, a region of shared memory; establishing, by the first process, a predefined number of message buffers, each message buffer associated with a process to be initialized on the compute node; sending, to a second process on the same compute node, a data communications message without determining whether the second process has been initialized, including storing the data communications message in the message buffer of the second process; and upon initialization of the second process: retrieving, by the second process, a pointer to the second process's message buffer; and retrieving, by the second process from the second process's message buffer in dependence upon the pointer, the data communications message sent by the first process.
NASA Astrophysics Data System (ADS)
Decker, K. M.; Jayewardena, C.; Rehmann, R.
We describe the library lgtlib, and lgttool, the corresponding development environment for Monte Carlo simulations of lattice gauge theory on multiprocessor vector computers with shared memory. We explain why distributed memory parallel processor (DMPP) architectures are particularly appealing for compute-intensive scientific applications, and introduce the design of a general application and program development environment system for scientific applications on DMPP architectures.
Synchronizing compute node time bases in a parallel computer
Chen, Dong; Faraj, Daniel A; Gooding, Thomas M; Heidelberger, Philip
2014-12-30
Synchronizing time bases in a parallel computer that includes compute nodes organized for data communications in a tree network, where one compute node is designated as a root, and, for each compute node: calculating data transmission latency from the root to the compute node; configuring a thread as a pulse waiter; initializing a wakeup unit; and performing a local barrier operation; upon each node completing the local barrier operation, entering, by all compute nodes, a global barrier operation; upon all nodes entering the global barrier operation, sending, to all the compute nodes, a pulse signal; and for each compute node upon receiving the pulse signal: waking, by the wakeup unit, the pulse waiter; setting a time base for the compute node equal to the data transmission latency between the root node and the compute node; and exiting the global barrier operation.
Synchronizing compute node time bases in a parallel computer
Chen, Dong; Faraj, Daniel A; Gooding, Thomas M; Heidelberger, Philip
2015-01-27
Synchronizing time bases in a parallel computer that includes compute nodes organized for data communications in a tree network, where one compute node is designated as a root, and, for each compute node: calculating data transmission latency from the root to the compute node; configuring a thread as a pulse waiter; initializing a wakeup unit; and performing a local barrier operation; upon each node completing the local barrier operation, entering, by all compute nodes, a global barrier operation; upon all nodes entering the global barrier operation, sending, to all the compute nodes, a pulse signal; and for each compute node upon receiving the pulse signal: waking, by the wakeup unit, the pulse waiter; setting a time base for the compute node equal to the data transmission latency between the root node and the compute node; and exiting the global barrier operation.
Parallel software support for computational structural mechanics
NASA Technical Reports Server (NTRS)
Jordan, Harry F.
1987-01-01
The application of the parallel programming methodology known as the Force was conducted. Two application issues were addressed. The first involves the efficiency of the implementation and its completeness in terms of satisfying the needs of other researchers implementing parallel algorithms. Support for, and interaction with, other Computational Structural Mechanics (CSM) researchers using the Force was the main issue, but some independent investigation of the Barrier construct, which is extremely important to overall performance, was also undertaken. Another efficiency issue which was addressed was that of relaxing the strong synchronization condition imposed on the self-scheduled parallel DO loop. The Force was extended by the addition of logical conditions to the cases of a parallel case construct and by the inclusion of a self-scheduled version of this construct. The second issue involved applying the Force to the parallelization of finite element codes such as those found in the NICE/SPAR testbed system. One of the more difficult problems encountered is the determination of what information in COMMON blocks is actually used outside of a subroutine and when a subroutine uses a COMMON block merely as scratch storage for internal temporary results.
Parallel Computation of the Regional Ocean Modeling System (ROMS)
Wang, P; Song, Y T; Chao, Y; Zhang, H
2005-04-05
The Regional Ocean Modeling System (ROMS) is a regional ocean general circulation modeling system solving the free surface, hydrostatic, primitive equations over varying topography. It is free software distributed world-wide for studying both complex coastal ocean problems and the basin-to-global scale ocean circulation. The original ROMS code could only be run on shared-memory systems. With the increasing need to simulate larger model domains with finer resolutions and on a variety of computer platforms, there is a need in the ocean-modeling community to have a ROMS code that can be run on any parallel computer ranging from 10 to hundreds of processors. Recently, we have explored parallelization for ROMS using the MPI programming model. In this paper, an efficient parallelization strategy for such a large-scale scientific software package, based on an existing shared-memory computing model, is presented. In addition, scientific applications and data-performance issues on a couple of SGI systems, including Columbia, the world's third-fastest supercomputer, are discussed.
Opportunities in computational mechanics: Advances in parallel computing
Lesar, R.A.
1999-02-01
In this paper, the authors will discuss recent advances in computing power and the prospects for using these new capabilities for studying plasticity and failure. They will first review the new capabilities made available with parallel computing. They will discuss how these machines perform and how well their architecture might work on materials issues. Finally, they will give some estimates on the size of problems possible using these computers.
Efficient Parallel Engineering Computing on Linux Workstations
NASA Technical Reports Server (NTRS)
Lou, John Z.
2010-01-01
A C software module has been developed that creates lightweight processes (LWPs) dynamically to achieve parallel computing performance in a variety of engineering simulation and analysis applications to support NASA and DoD project tasks. The required interface between the module and the application it supports is simple, minimal and almost completely transparent to the user applications, and it can achieve nearly ideal computing speed-up on multi-CPU engineering workstations of all operating system platforms. The module can be integrated into an existing application (C, C++, Fortran and others) either as part of a compiled module or as a dynamically linked library (DLL).
A design methodology for portable software on parallel computers
NASA Technical Reports Server (NTRS)
Nicol, David M.; Miller, Keith W.; Chrisman, Dan A.
1993-01-01
This final report for research that was supported by grant number NAG-1-995 documents our progress in addressing two difficulties in parallel programming. The first difficulty is developing software that will execute quickly on a parallel computer. The second difficulty is transporting software between dissimilar parallel computers. In general, we expect that more hardware-specific information will be included in software designs for parallel computers than in designs for sequential computers. This inclusion is an instance of portability being sacrificed for high performance. New parallel computers are being introduced frequently. Trying to keep one's software on the current high performance hardware, a software developer almost continually faces yet another expensive software transportation. The problem of the proposed research is to create a design methodology that helps designers to more precisely control both portability and hardware-specific programming details. The proposed research emphasizes programming for scientific applications. We completed our study of the parallelizability of a subsystem of the NASA Earth Radiation Budget Experiment (ERBE) data processing system. This work is summarized in section two. A more detailed description is provided in Appendix A ('Programming Practices to Support Eventual Parallelism'). Mr. Chrisman, a graduate student, wrote and successfully defended a Ph.D. dissertation proposal which describes our research associated with the issues of software portability and high performance. The list of research tasks are specified in the proposal. The proposal 'A Design Methodology for Portable Software on Parallel Computers' is summarized in section three and is provided in its entirety in Appendix B. We are currently studying a proposed subsystem of the NASA Clouds and the Earth's Radiant Energy System (CERES) data processing system. This software is the proof-of-concept for the Ph.D. dissertation. We have implemented and measured
Seismic imaging on massively parallel computers
Ober, C.C.; Oldfield, R.A.; Womble, D.E.; Mosher, C.C.
1997-07-01
A key to reducing the risks and costs associated with oil and gas exploration is the fast, accurate imaging of complex geologies, such as salt domes in the Gulf of Mexico and overthrust regions in US onshore regions. Pre-stack depth migration generally yields the most accurate images, and one approach to this is to solve the scalar-wave equation using finite differences. Current industry computational capabilities are insufficient for the application of finite-difference, 3-D, prestack, depth-migration algorithms. High performance computers and state-of-the-art algorithms and software are required to meet this need. As part of an ongoing ACTI project funded by the US Department of Energy, the authors have developed a finite-difference, 3-D prestack, depth-migration code for massively parallel computer systems. The goal of this work is to demonstrate that massively parallel computers (thousands of processors) can be used efficiently for seismic imaging, and that sufficient computing power exists (or soon will exist) to make finite-difference, prestack, depth migration practical for oil and gas exploration.
Parallel architectures for computing cyclic convolutions
NASA Technical Reports Server (NTRS)
Yeh, C.-S.; Reed, I. S.; Truong, T. K.
1983-01-01
In the paper two parallel architectural structures are developed to compute one-dimensional cyclic convolutions. The first structure is based on the Chinese remainder theorem and Kung's pipelined array. The second structure is a direct mapping from the mathematical definition of a cyclic convolution to a computational architecture. To compute a d-point cyclic convolution the first structure needs d/2 inner product cells, while the second structure and Kung's linear array require d cells. However, to compute a cyclic convolution, the second structure requires less time than both the first structure and Kung's linear array. Another application of the second structure is to multiply a Toeplitz matrix by a vector. A table is listed to compare these two structures and Kung's linear array. Both structures are simple and regular and are therefore suitable for VLSI implementation.
Parallel architectures for computing cyclic convolutions
NASA Technical Reports Server (NTRS)
Yeh, C.-S.; Reed, I. S.; Truong, T. K.
1983-01-01
In the paper two parallel architectural structures are developed to compute one-dimensional cyclic convolutions. The first structure is based on the Chinese remainder theorem and Kung's pipelined array. The second structure is a direct mapping from the mathematical definition of a cyclic convolution to a computational architecture. To compute a d-point cyclic convolution the first structure needs d/2 inner product cells, while the second structure and Kung's linear array require d cells. However, to compute a cyclic convolution, the second structure requires less time than both the first structure and Kung's linear array. Another application of the second structure is to multiply a Toeplitz matrix by a vector. A table is listed to compare these two structures and Kung's linear array. Both structures are simple and regular and are therefore suitable for VLSI implementation.
Scientific computing environment for the 1980s
NASA Technical Reports Server (NTRS)
Bailey, F. R.
1986-01-01
An emerging scientific computing environment in which computers are used not only to solve large-scale models, but are also integrated into the daily activities of scientists and engineers, is discussed. The requirements of the scientific user in this environment are reviewed, and the hardware environment is described, including supercomputers, work stations, mass storage, and communications. Significant increases in memory capacity to keep pace with performance increases, the introduction of powerful graphics displays into the work station, and networking to integrate many computers are stressed. The emerging system software environment is considered, including the operating systems, communications software, and languages. New scientific user tools and utilities that will become available are described.
Dynamic file-access characteristics of a production parallel scientific workload
NASA Technical Reports Server (NTRS)
Kotz, David; Nieuwejaar, Nils
1994-01-01
Multiprocessors have permitted astounding increases in computational performance, but many cannot meet the intense I/O requirements of some scientific applications. An important component of any solution to this I/O bottleneck is a parallel file system that can provide high-bandwidth access to tremendous amounts of data in parallel to hundreds or thousands of processors. Most successful systems are based on a solid understanding of the expected workload, but thus far there have been no comprehensive workload characterizations of multiprocessor file systems. This paper presents the results of a three week tracing study in which all file-related activity on a massively parallel computer was recorded. Our instrumentation differs from previous efforts in that it collects information about every I/O request and about the mix of jobs running in a production environment. We also present the results of a trace-driven caching simulation and recommendations for designers of multiprocessor file systems.
Parallelized reliability estimation of reconfigurable computer networks
NASA Technical Reports Server (NTRS)
Nicol, David M.; Das, Subhendu; Palumbo, Dan
1990-01-01
A parallelized system, ASSURE, for computing the reliability of embedded avionics flight control systems which are able to reconfigure themselves in the event of failure is described. ASSURE accepts a grammar that describes a reliability semi-Markov state-space. From this it creates a parallel program that simultaneously generates and analyzes the state-space, placing upper and lower bounds on the probability of system failure. ASSURE is implemented on a 32-node Intel iPSC/860, and has achieved high processor efficiencies on real problems. Through a combination of improved algorithms, exploitation of parallelism, and use of an advanced microprocessor architecture, ASSURE has reduced the execution time on substantial problems by a factor of one thousand over previous workstation implementations. Furthermore, ASSURE's parallel execution rate on the iPSC/860 is an order of magnitude faster than its serial execution rate on a Cray-2 supercomputer. While dynamic load balancing is necessary for ASSURE's good performance, it is needed only infrequently; the particular method of load balancing used does not substantially affect performance.
Parallel Computational Environment for Substructure Optimization
NASA Technical Reports Server (NTRS)
Gendy, Atef S.; Patnaik, Surya N.; Hopkins, Dale A.; Berke, Laszlo
1995-01-01
Design optimization of large structural systems can be attempted through a substructure strategy when convergence difficulties are encountered. When this strategy is used, the large structure is divided into several smaller substructures and a subproblem is defined for each substructure. The solution of the large optimization problem can be obtained iteratively through repeated solutions of the modest subproblems. Substructure strategies, in sequential as well as in parallel computational modes on a Cray YMP multiprocessor computer, have been incorporated in the optimization test bed CometBoards. CometBoards is an acronym for Comparative Evaluation Test Bed of Optimization and Analysis Routines for Design of Structures. Three issues, intensive computation, convergence of the iterative process, and analytically superior optimum, were addressed in the implementation of substructure optimization into CometBoards. Coupling between subproblems as well as local and global constraint grouping are essential for convergence of the iterative process. The substructure strategy can produce an analytically superior optimum different from what can be obtained by regular optimization. For the problems solved, substructure optimization in a parallel computational mode made effective use of all assigned processors.
An improved spectral graph partitioning algorithm for mapping parallel computations
Hendrickson, B.; Leland, R.
1992-09-01
Efficient use of a distributed memory parallel computer requires that the computational load be balanced across processors in a way that minimizes interprocessor communication. We present a new domain mapping algorithm that extends recent work in which ideas from spectral graph theory have been applied to this problem. Our generalization of spectral graph bisection involves a novel use of multiple eigenvectors to allow for division of a computation into four or eight parts at each stage of a recursive decomposition. The resulting method is suitable for scientific computations like irregular finite elements or differences performed on hypercube or mesh architecture machines. Experimental results confirm that the new method provides better decompositions arrived at more economically and robustly than with previous spectral methods. We have also improved upon the known spectral lower bound for graph bisection.
Computational chaos in massively parallel neural networks
NASA Technical Reports Server (NTRS)
Barhen, Jacob; Gulati, Sandeep
1989-01-01
A fundamental issue which directly impacts the scalability of current theoretical neural network models to massively parallel embodiments, in both software as well as hardware, is the inherent and unavoidable concurrent asynchronicity of emerging fine-grained computational ensembles and the possible emergence of chaotic manifestations. Previous analyses attributed dynamical instability to the topology of the interconnection matrix, to parasitic components or to propagation delays. However, researchers have observed the existence of emergent computational chaos in a concurrently asynchronous framework, independent of the network topology. Researcher present a methodology enabling the effective asynchronous operation of large-scale neural networks. Necessary and sufficient conditions guaranteeing concurrent asynchronous convergence are established in terms of contracting operators. Lyapunov exponents are computed formally to characterize the underlying nonlinear dynamics. Simulation results are presented to illustrate network convergence to the correct results, even in the presence of large delays.
Electromagnetic Physics Models for Parallel Computing Architectures
NASA Astrophysics Data System (ADS)
Amadio, G.; Ananya, A.; Apostolakis, J.; Aurora, A.; Bandieramonte, M.; Bhattacharyya, A.; Bianchini, C.; Brun, R.; Canal, P.; Carminati, F.; Duhem, L.; Elvira, D.; Gheata, A.; Gheata, M.; Goulas, I.; Iope, R.; Jun, S. Y.; Lima, G.; Mohanty, A.; Nikitina, T.; Novak, M.; Pokorski, W.; Ribon, A.; Seghal, R.; Shadura, O.; Vallecorsa, S.; Wenzel, S.; Zhang, Y.
2016-10-01
The recent emergence of hardware architectures characterized by many-core or accelerated processors has opened new opportunities for concurrent programming models taking advantage of both SIMD and SIMT architectures. GeantV, a next generation detector simulation, has been designed to exploit both the vector capability of mainstream CPUs and multi-threading capabilities of coprocessors including NVidia GPUs and Intel Xeon Phi. The characteristics of these architectures are very different in terms of the vectorization depth and type of parallelization needed to achieve optimal performance. In this paper we describe implementation of electromagnetic physics models developed for parallel computing architectures as a part of the GeantV project. Results of preliminary performance evaluation and physics validation are presented as well.
Electromagnetic physics models for parallel computing architectures
Amadio, G.; Ananya, A.; Apostolakis, J.; ...
2016-11-21
The recent emergence of hardware architectures characterized by many-core or accelerated processors has opened new opportunities for concurrent programming models taking advantage of both SIMD and SIMT architectures. GeantV, a next generation detector simulation, has been designed to exploit both the vector capability of mainstream CPUs and multi-threading capabilities of coprocessors including NVidia GPUs and Intel Xeon Phi. The characteristics of these architectures are very different in terms of the vectorization depth and type of parallelization needed to achieve optimal performance. In this paper we describe implementation of electromagnetic physics models developed for parallel computing architectures as a part ofmore » the GeantV project. Finally, the results of preliminary performance evaluation and physics validation are presented as well.« less
Electromagnetic physics models for parallel computing architectures
Amadio, G.; Ananya, A.; Apostolakis, J.; Aurora, A.; Bandieramonte, M.; Bhattacharyya, A.; Bianchini, C.; Brun, R.; Canal, P.; Carminati, F.; Duhem, L.; Elvira, D.; Gheata, A.; Gheata, M.; Goulas, I.; Iope, R.; Jun, S. Y.; Lima, G.; Mohanty, A.; Nikitina, T.; Novak, M.; Pokorski, W.; Ribon, A.; Seghal, R.; Shadura, O.; Vallecorsa, S.; Wenzel, S.; Zhang, Y.
2016-11-21
The recent emergence of hardware architectures characterized by many-core or accelerated processors has opened new opportunities for concurrent programming models taking advantage of both SIMD and SIMT architectures. GeantV, a next generation detector simulation, has been designed to exploit both the vector capability of mainstream CPUs and multi-threading capabilities of coprocessors including NVidia GPUs and Intel Xeon Phi. The characteristics of these architectures are very different in terms of the vectorization depth and type of parallelization needed to achieve optimal performance. In this paper we describe implementation of electromagnetic physics models developed for parallel computing architectures as a part of the GeantV project. Finally, the results of preliminary performance evaluation and physics validation are presented as well.
Computation and parallel implementation for early vision
NASA Technical Reports Server (NTRS)
Gualtieri, J. Anthony
1990-01-01
The problem of early vision is to transform one or more retinal illuminance images-pixel arrays-to image representations built out of such primitive visual features such as edges, regions, disparities, and clusters. These transformed representations form the input to later vision stages that perform higher level vision tasks including matching and recognition. Researchers developed algorithms for: (1) edge finding in the scale space formulation; (2) correlation methods for computing matches between pairs of images; and (3) clustering of data by neural networks. These algorithms are formulated for parallel implementation of SIMD machines, such as the Massively Parallel Processor, a 128 x 128 array processor with 1024 bits of local memory per processor. For some cases, researchers can show speedups of three orders of magnitude over serial implementations.
Parallel computing techniques for rotorcraft aerodynamics
NASA Astrophysics Data System (ADS)
Ekici, Kivanc
The modification of unsteady three-dimensional Navier-Stokes codes for application on massively parallel and distributed computing environments is investigated. The Euler/Navier-Stokes code TURNS (Transonic Unsteady Rotor Navier-Stokes) was chosen as a test bed because of its wide use by universities and industry. For the efficient implementation of TURNS on parallel computing systems, two algorithmic changes are developed. First, main modifications to the implicit operator, Lower-Upper Symmetric Gauss Seidel (LU-SGS) originally used in TURNS, is performed. Second, application of an inexact Newton method, coupled with a Krylov subspace iterative method (Newton-Krylov method) is carried out. Both techniques have been tried previously for the Euler equations mode of the code. In this work, we have extended the methods to the Navier-Stokes mode. Several new implicit operators were tried because of convergence problems of traditional operators with the high cell aspect ratio (CAR) grids needed for viscous calculations on structured grids. Promising results for both Euler and Navier-Stokes cases are presented for these operators. For the efficient implementation of Newton-Krylov methods to the Navier-Stokes mode of TURNS, efficient preconditioners must be used. The parallel implicit operators used in the previous step are employed as preconditioners and the results are compared. The Message Passing Interface (MPI) protocol has been used because of its portability to various parallel architectures. It should be noted that the proposed methodology is general and can be applied to several other CFD codes (e.g. OVERFLOW).
Hypercluster - Parallel processing for computational mechanics
NASA Technical Reports Server (NTRS)
Blech, Richard A.
1988-01-01
An account is given of the development status, performance capabilities and implications for further development of NASA-Lewis' testbed 'hypercluster' parallel computer network, in which multiple processors communicate through a shared memory. Processors have local as well as shared memory; the hypercluster is expanded in the same manner as the hypercube, with processor clusters replacing the normal single processor node. The NASA-Lewis machine has three nodes with a vector personality and one node with a scalar personality. Each of the vector nodes uses four board-level vector processors, while the scalar node uses four general-purpose microcomputer boards.
Associative Networks on a Massively Parallel Computer.
1985-10-01
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Parallel Processing for Computational Continuum Dynamics.
1985-05-10
F49620-84-C-0111In I PARALLEL PROCESSING FOR COMPUTATIONAL CONTINUUM DYNAMICS: A FINAL REPORT Accession For Joseph F. McGrath DTIc TAB KMS Fusion, Inc...Uiarmouncod 0P . . B O X 1 5 6 7 J u s t tic a t io - --- - - Ann Arbor, MI 48106 A v ar_ _ la b il it¥ C o d e a 10 May 1985 nF , Final Report ... REPORT (Yr., Mo. a) 15 PAGE COUNT * Final IFROM 5S4i..4r.5 .. Mar. 10 May 1985 42 * 16. SUPPLEMENTARY NOTATION 17. COSATI CODES IB. SUBJECT TERMS
Parallel algorithm for computing points on a computation front hyperplane
NASA Astrophysics Data System (ADS)
Krasnov, M. M.
2015-01-01
A parallel algorithm for computing points on a computation front hyperplane is described. This task arises in the computation of a quantity defined on a multidimensional rectangular domain. Three-dimensional domains are usually discussed, but the material is given in the general form when the number of measurements is at least two. When the values of a quantity at different points are internally independent (which is frequently the case), the corresponding computations are independent as well and can be performed in parallel. However, if there are internal dependences (as, for example, in the Gauss-Seidel method for systems of linear equations), then the order of scanning points of the domain is an important issue. A conventional approach in this case is to form a computation front hyperplane (a usual plane in the three-dimensional case and a line in the two-dimensional case) that moves linearly across the domain at a certain angle. At every step in the course of motion of this hyperplane, its intersection points with the domain can be treated independently and, hence, in parallel, but the steps themselves are executed sequentially. At different steps, the intersection of the hyperplane with the entire domain can have a rather complex geometry and the search for all points of the domain lying on the hyperplane at a given step is a nontrivial problem. This problem (i.e., the computation of the coordinates of points lying in the intersection of the domain with the hyperplane at a given step in the course of hyperplane motion) is addressed below. The computations over the points of the hyperplane can be executed in parallel.
Utilizing parallel optimization in computational fluid dynamics
NASA Astrophysics Data System (ADS)
Kokkolaras, Michael
1998-12-01
General problems of interest in computational fluid dynamics are investigated by means of optimization. Specifically, in the first part of the dissertation, a method of optimal incremental function approximation is developed for the adaptive solution of differential equations. Various concepts and ideas utilized by numerical techniques employed in computational mechanics and artificial neural networks (e.g. function approximation and error minimization, variational principles and weighted residuals, and adaptive grid optimization) are combined to formulate the proposed method. The basis functions and associated coefficients of a series expansion, representing the solution, are optimally selected by a parallel direct search technique at each step of the algorithm according to appropriate criteria; the solution is built sequentially. In this manner, the proposed method is adaptive in nature, although a grid is neither built nor adapted in the traditional sense using a-posteriori error estimates. Variational principles are utilized for the definition of the objective function to be extremized in the associated optimization problems, ensuring that the problem is well-posed. Complicated data structures and expensive remeshing algorithms and systems solvers are avoided. Computational efficiency is increased by using low-order basis functions and concurrent computing. Numerical results and convergence rates are reported for a range of steady-state problems, including linear and nonlinear differential equations associated with general boundary conditions, and illustrate the potential of the proposed method. Fluid dynamics applications are emphasized. Conclusions are drawn by discussing the method's limitations, advantages, and possible extensions. The second part of the dissertation is concerned with the optimization of the viscous-inviscid-interaction (VII) mechanism in an airfoil flow analysis code. The VII mechanism is based on the concept of a transpiration velocity
Optimal dynamic remapping of parallel computations
NASA Technical Reports Server (NTRS)
Nicol, David M.; Reynolds, Paul F., Jr.
1987-01-01
A large class of computations are characterized by a sequence of phases, with phase changes occurring unpredictably. The decision problem was considered regarding the remapping of workload to processors in a parallel computation when the utility of remapping and the future behavior of the workload is uncertain, and phases exhibit stable execution requirements during a given phase, but requirements may change radically between phases. For these problems a workload assignment generated for one phase may hinder performance during the next phase. This problem is treated formally for a probabilistic model of computation with at most two phases. The fundamental problem of balancing the expected remapping performance gain against the delay cost was addressed. Stochastic dynamic programming is used to show that the remapping decision policy minimizing the expected running time of the computation has an extremely simple structure. Because the gain may not be predictable, the performance of a heuristic policy that does not require estimnation of the gain is examined. The heuristic method's feasibility is demonstrated by its use on an adaptive fluid dynamics code on a multiprocessor. The results suggest that except in extreme cases, the remapping decision problem is essentially that of dynamically determining whether gain can be achieved by remapping after a phase change. The results also suggest that this heuristic is applicable to computations with more than two phases.
CFD Research, Parallel Computation and Aerodynamic Optimization
NASA Technical Reports Server (NTRS)
Ryan, James S.
1995-01-01
During the last five years, CFD has matured substantially. Pure CFD research remains to be done, but much of the focus has shifted to integration of CFD into the design process. The work under these cooperative agreements reflects this trend. The recent work, and work which is planned, is designed to enhance the competitiveness of the US aerospace industry. CFD and optimization approaches are being developed and tested, so that the industry can better choose which methods to adopt in their design processes. The range of computer architectures has been dramatically broadened, as the assumption that only huge vector supercomputers could be useful has faded. Today, researchers and industry can trade off time, cost, and availability, choosing vector supercomputers, scalable parallel architectures, networked workstations, or heterogenous combinations of these to complete required computations efficiently.
Optimized data communications in a parallel computer
Faraj, Daniel A.
2014-08-19
A parallel computer includes nodes that include a network adapter that couples the node in a point-to-point network and supports communications in opposite directions of each dimension. Optimized communications include: receiving, by a network adapter of a receiving compute node, a packet--from a source direction--that specifies a destination node and deposit hints. Each hint is associated with a direction within which the packet is to be deposited. If a hint indicates the packet to be deposited in the opposite direction: the adapter delivers the packet to an application on the receiving node; forwards the packet to a next node in the opposite direction if the receiving node is not the destination; and forwards the packet to a node in a direction of a subsequent dimension if the hints indicate that the packet is to be deposited in the direction of the subsequent dimension.
Optimized data communications in a parallel computer
Faraj, Daniel A
2014-10-21
A parallel computer includes nodes that include a network adapter that couples the node in a point-to-point network and supports communications in opposite directions of each dimension. Optimized communications include: receiving, by a network adapter of a receiving compute node, a packet--from a source direction--that specifies a destination node and deposit hints. Each hint is associated with a direction within which the packet is to be deposited. If a hint indicates the packet to be deposited in the opposite direction: the adapter delivers the packet to an application on the receiving node; forwards the packet to a next node in the opposite direction if the receiving node is not the destination; and forwards the packet to a node in a direction of a subsequent dimension if the hints indicate that the packet is to be deposited in the direction of the subsequent dimension.
Parallel Proximity Detection for Computer Simulations
NASA Technical Reports Server (NTRS)
Steinman, Jeffrey S. (Inventor); Wieland, Frederick P. (Inventor)
1998-01-01
The present invention discloses a system for performing proximity detection in computer simulations on parallel processing architectures utilizing a distribution list which includes movers and sensor coverages which check in and out of grids. Each mover maintains a list of sensors that detect the mover's motion as the mover and sensor coverages check in and out of the grids. Fuzzy grids are included by fuzzy resolution parameters to allow movers and sensor coverages to check in and out of grids without computing exact grid crossings. The movers check in and out of grids while moving sensors periodically inform the grids of their coverage. In addition, a lookahead function is also included for providing a generalized capability without making any limiting assumptions about the particular application to which it is applied. The lookahead function is initiated so that risk-free synchronization strategies never roll back grid events. The lookahead function adds fixed delays as events are scheduled for objects on other nodes.
Parallel Proximity Detection for Computer Simulation
NASA Technical Reports Server (NTRS)
Steinman, Jeffrey S. (Inventor); Wieland, Frederick P. (Inventor)
1997-01-01
The present invention discloses a system for performing proximity detection in computer simulations on parallel processing architectures utilizing a distribution list which includes movers and sensor coverages which check in and out of grids. Each mover maintains a list of sensors that detect the mover's motion as the mover and sensor coverages check in and out of the grids. Fuzzy grids are includes by fuzzy resolution parameters to allow movers and sensor coverages to check in and out of grids without computing exact grid crossings. The movers check in and out of grids while moving sensors periodically inform the grids of their coverage. In addition, a lookahead function is also included for providing a generalized capability without making any limiting assumptions about the particular application to which it is applied. The lookahead function is initiated so that risk-free synchronization strategies never roll back grid events. The lookahead function adds fixed delays as events are scheduled for objects on other nodes.
A Component Architecture for High-Performance Scientific Computing
Bernholdt, D E; Allan, B A; Armstrong, R; Bertrand, F; Chiu, K; Dahlgren, T L; Damevski, K; Elwasif, W R; Epperly, T W; Govindaraju, M; Katz, D S; Kohl, J A; Krishnan, M; Kumfert, G; Larson, J W; Lefantzi, S; Lewis, M J; Malony, A D; McInnes, L C; Nieplocha, J; Norris, B; Parker, S G; Ray, J; Shende, S; Windus, T L; Zhou, S
2004-12-14
The Common Component Architecture (CCA) provides a means for software developers to manage the complexity of large-scale scientific simulations and to move toward a plug-and-play environment for high-performance computing. In the scientific computing context, component models also promote collaboration using independently developed software, thereby allowing particular individuals or groups to focus on the aspects of greatest interest to them. The CCA supports parallel and distributed computing as well as local high-performance connections between components in a language-independent manner. The design places minimal requirements on components and thus facilitates the integration of existing code into the CCA environment. The CCA model imposes minimal overhead to minimize the impact on application performance. The focus on high performance distinguishes the CCA from most other component models. The CCA is being applied within an increasing range of disciplines, including combustion research, global climate simulation, and computational chemistry.
A Component Architecture for High-Performance Scientific Computing
Bernholdt, David E; Allan, Benjamin A; Armstrong, Robert C; Bertrand, Felipe; Chiu, Kenneth; Dahlgren, Tamara L; Damevski, Kostadin; Elwasif, Wael R; Epperly, Thomas G; Govindaraju, Madhusudhan; Katz, Daniel S; Kohl, James A; Krishnan, Manoj Kumar; Kumfert, Gary K; Larson, J Walter; Lefantzi, Sophia; Lewis, Michael J; Malony, Allen D; McInnes, Lois C; Nieplocha, Jarek; Norris, Boyana; Parker, Steven G; Ray, Jaideep; Shende, Sameer; Windus, Theresa L; Zhou, Shujia
2006-07-03
The Common Component Architecture (CCA) provides a means for software developers to manage the complexity of large-scale scientific simulations and to move toward a plug-and-play environment for high-performance computing. In the scientific computing context, component models also promote collaboration using independently developed software, thereby allowing particular individuals or groups to focus on the aspects of greatest interest to them. The CCA supports parallel and distributed computing as well as local high-performance connections between components in a language-independent manner. The design places minimal requirements on components and thus facilitates the integration of existing code into the CCA environment. The CCA model imposes minimal overhead to minimize the impact on application performance. The focus on high performance distinguishes the CCA from most other component models. The CCA is being applied within an increasing range of disciplines, including combustion research, global climate simulation, and computational chemistry.
Final Report: Super Instruction Architecture for Scalable Parallel Computations
Sanders, Beverly Ann; Bartlett, Rodney; Deumens, Erik
2013-12-23
The most advanced methods for reliable and accurate computation of the electronic structure of molecular and nano systems are the coupled-cluster techniques. These high-accuracy methods help us to understand, for example, how biological enzymes operate and contribute to the design of new organic explosives. The ACES III software provides a modern, high-performance implementation of these methods optimized for high performance parallel computer systems, ranging from small clusters typical in individual research groups, through larger clusters available in campus and regional computer centers, all the way to high-end petascale systems at national labs, including exploiting GPUs if available. This project enhanced the ACESIII software package and used it to study interesting scientific problems.
Techniques and Tools for Performance Tuning of Parallel and Distributed Scientific Applications
NASA Technical Reports Server (NTRS)
Sarukkai, Sekhar R.; VanderWijngaart, Rob F.; Castagnera, Karen (Technical Monitor)
1994-01-01
Performance degradation in scientific computing on parallel and distributed computer systems can be caused by numerous factors. In this half-day tutorial we explain what are the important methodological issues involved in obtaining codes that have good performance potential. Then we discuss what are the possible obstacles in realizing that potential on contemporary hardware platforms, and give an overview of the software tools currently available for identifying the performance bottlenecks. Finally, some realistic examples are used to illustrate the actual use and utility of such tools.
Accelerating Scientific Discovery Through Computation and Visualization II
Sims, James S.; George, William L.; Satterfield, Steven G.; Hung, Howard K.; Hagedorn, John G.; Ketcham, Peter M.; Griffin, Terence J.; Hagstrom, Stanley A.; Franiatte, Julien C.; Bryant, Garnett W.; Jaskólski, W.; Martys, Nicos S.; Bouldin, Charles E.; Simmons, Vernon; Nicolas, Oliver P.; Warren, James A.; am Ende, Barbara A.; Koontz, John E.; Filla, B. James; Pourprix, Vital G.; Copley, Stefanie R.; Bohn, Robert B.; Peskin, Adele P.; Parker, Yolanda M.; Devaney, Judith E.
2002-01-01
This is the second in a series of articles describing a wide variety of projects at NIST that synergistically combine physical science and information science. It describes, through examples, how the Scientific Applications and Visualization Group (SAVG) at NIST has utilized high performance parallel computing, visualization, and machine learning to accelerate research. The examples include scientific collaborations in the following areas: (1) High Precision Energies for few electron atomic systems, (2) Flows of suspensions, (3) X-ray absorption, (4) Molecular dynamics of fluids, (5) Nanostructures, (6) Dendritic growth in alloys, (7) Screen saver science, (8) genetic programming. PMID:27446728
Swift : fast, reliable, loosely coupled parallel computation.
Zhao, Y.; Hategan, M.; Clifford, B.; Foster, I.; von Laszewski, G.; Nefedova, V.; Raicu, I.; Stef-Praun, T.; Wilde, M.; Mathematics and Computer Science; Univ. of Chicago
2007-01-01
A common pattern in scientific computing involves the execution of many tasks that are coupled only in the sense that the output of one may be passed as input to one or more others - for example, as a file, or via a Web Services invocation. While such 'loosely coupled' computations can involve large amounts of computation and communication, the concerns of the programmer tend to be different than in traditional high performance computing, being focused on management issues relating to the large numbers of datasets and tasks (and often, the complexities inherent in 'messy' data organizations) rather than the optimization of interprocessor communication. To address these concerns, we have developed Swift, a system that combines a novel scripting language called SwiftScript with a powerful runtime system based on CoG Karajan and Falkon to allow for the concise specification, and reliable and efficient execution, of large loosely coupled computations. Swift adopts and adapts ideas first explored in the GriPhyN virtual data system, improving on that system in many regards. We describe the SwiftScript language and its use of XDTM to describe the logical structure of complex file system structures. We also present the Swift system and its use of CoG Karajan, Falkon, and Globus services to dispatch and manage the execution of many tasks in different execution environments. We summarize application experiences and detail performance experiments that quantify the cost of Swift operations.
Simple, parallel virtual machines for extreme computations
NASA Astrophysics Data System (ADS)
Chokoufe Nejad, Bijan; Ohl, Thorsten; Reuter, Jürgen
2015-11-01
We introduce a virtual machine (VM) written in a numerically fast language like Fortran or C for evaluating very large expressions. We discuss the general concept of how to perform computations in terms of a VM and present specifically a VM that is able to compute tree-level cross sections for any number of external legs, given the corresponding byte-code from the optimal matrix element generator, O'MEGA. Furthermore, this approach allows to formulate the parallel computation of a single phase space point in a simple and obvious way. We analyze hereby the scaling behavior with multiple threads as well as the benefits and drawbacks that are introduced with this method. Our implementation of a VM can run faster than the corresponding native, compiled code for certain processes and compilers, especially for very high multiplicities, and has in general runtimes in the same order of magnitude. By avoiding the tedious compile and link steps, which may fail for source code files of gigabyte sizes, new processes or complex higher order corrections that are currently out of reach could be evaluated with a VM given enough computing power.
A Computational Fluid Dynamics Algorithm on a Massively Parallel Computer
NASA Technical Reports Server (NTRS)
Jespersen, Dennis C.; Levit, Creon
1989-01-01
The discipline of computational fluid dynamics is demanding ever-increasing computational power to deal with complex fluid flow problems. We investigate the performance of a finite-difference computational fluid dynamics algorithm on a massively parallel computer, the Connection Machine. Of special interest is an implicit time-stepping algorithm; to obtain maximum performance from the Connection Machine, it is necessary to use a nonstandard algorithm to solve the linear systems that arise in the implicit algorithm. We find that the Connection Machine ran achieve very high computation rates on both explicit and implicit algorithms. The performance of the Connection Machine puts it in the same class as today's most powerful conventional supercomputers.
Research initiatives for plug-and-play scientific computing.
McInnes, L. C.; Dahlgren, T.; Nieplocha, J.; Bernholdt, D.; Allan, B.; Armstrong, R.; Chavarria, D.; Elwasif, W.; Gorton, I.; Krishan, M.; Malony, A.; Norris, B.; Ray, J.; Shende, S.; Mathematics and Computer Science; LLNL; PNNL; ORNL; SNL; Univ. of Oregon
2007-01-01
This paper introduces three component technology initiatives within the SciDAC Center for Technology for Advanced Scientific Component Software (TASCS) that address ever-increasing productivity challenges in creating, managing, and applying simulation software to scientific discovery. By leveraging the Common Component Architecture (CCA), a new component standard for high-performance scientific computing, these initiatives tackle difficulties at different but related levels in the development of component-based scientific software: (1) deploying applications on massively parallel and heterogeneous architectures, (2) investigating new approaches to the runtime enforcement of behavioral semantics, and (3) developing tools to facilitate dynamic composition, substitution, and reconfiguration of component implementations and parameters, so that application scientists can explore tradeoffs among factors such as accuracy, reliability, and performance.
Research initiatives for plug-and-play scientific computing
NASA Astrophysics Data System (ADS)
Curfman McInnes, Lois; Dahlgren, Tamara; Nieplocha, Jarek; Bernholdt, David; Allan, Ben; Armstrong, Rob; Chavarria, Daniel; Elwasif, Wael; Gorton, Ian; Kenny, Joe; Krishan, Manoj; Malony, Allen; Norris, Boyana; Ray, Jaideep; Shende, Sameer
2007-07-01
This paper introduces three component technology initiatives within the SciDAC Center for Technology for Advanced Scientific Component Software (TASCS) that address ever-increasing productivity challenges in creating, managing, and applying simulation software to scientific discovery. By leveraging the Common Component Architecture (CCA), a new component standard for high-performance scientific computing, these initiatives tackle difficulties at different but related levels in the development of component-based scientific software: (1) deploying applications on massively parallel and heterogeneous architectures, (2) investigating new approaches to the runtime enforcement of behavioral semantics, and (3) developing tools to facilitate dynamic composition, substitution, and reconfiguration of component implementations and parameters, so that application scientists can explore tradeoffs among factors such as accuracy, reliability, and performance.
Sims, James S; George, William L; Griffin, Terence J; Hagedorn, John G; Hung, Howard K; Kelso, John T; Olano, Marc; Peskin, Adele P; Satterfield, Steven G; Terrill, Judith Devaney; Bryant, Garnett W; Diaz, Jose G
2008-01-01
This is the third in a series of articles that describe, through examples, how the Scientific Applications and Visualization Group (SAVG) at NIST has utilized high performance parallel computing, visualization, and machine learning to accelerate scientific discovery. In this article we focus on the use of high performance computing and visualization for simulations of nanotechnology.
Evaluation of leading scalar and vector architectures for scientific computations
Simon, Horst D.; Oliker, Leonid; Canning, Andrew; Carter, Jonathan; Ethier, Stephane; Shalf, John
2004-04-20
The growing gap between sustained and peak performance for scientific applications is a well-known problem in high performance computing. The recent development of parallel vector systems offers the potential to reduce this gap for many computational science codes and deliver a substantial increase in computing capabilities. This project examines the performance of the cacheless vector Earth Simulator (ES) and compares it to superscalar cache-based IBM Power3 system. Results demonstrate that the ES is significantly faster than the Power3 architecture, highlighting the tremendous potential advantage of the ES for numerical simulation. However, vectorization of a particle-in-cell application (GTC) greatly increased the memory footprint preventing loop-level parallelism and limiting scalability potential.
NASA Astrophysics Data System (ADS)
Moon, Hongsik
What is the impact of multicore and associated advanced technologies on computational software for science? Most researchers and students have multicore laptops or desktops for their research and they need computing power to run computational software packages. Computing power was initially derived from Central Processing Unit (CPU) clock speed. That changed when increases in clock speed became constrained by power requirements. Chip manufacturers turned to multicore CPU architectures and associated technological advancements to create the CPUs for the future. Most software applications benefited by the increased computing power the same way that increases in clock speed helped applications run faster. However, for Computational ElectroMagnetics (CEM) software developers, this change was not an obvious benefit - it appeared to be a detriment. Developers were challenged to find a way to correctly utilize the advancements in hardware so that their codes could benefit. The solution was parallelization and this dissertation details the investigation to address these challenges. Prior to multicore CPUs, advanced computer technologies were compared with the performance using benchmark software and the metric was FLoting-point Operations Per Seconds (FLOPS) which indicates system performance for scientific applications that make heavy use of floating-point calculations. Is FLOPS an effective metric for parallelized CEM simulation tools on new multicore system? Parallel CEM software needs to be benchmarked not only by FLOPS but also by the performance of other parameters related to type and utilization of the hardware, such as CPU, Random Access Memory (RAM), hard disk, network, etc. The codes need to be optimized for more than just FLOPs and new parameters must be included in benchmarking. In this dissertation, the parallel CEM software named High Order Basis Based Integral Equation Solver (HOBBIES) is introduced. This code was developed to address the needs of the
QCMPI: A parallel environment for quantum computing
NASA Astrophysics Data System (ADS)
Tabakin, Frank; Juliá-Díaz, Bruno
2009-06-01
QCMPI is a quantum computer (QC) simulation package written in Fortran 90 with parallel processing capabilities. It is an accessible research tool that permits rapid evaluation of quantum algorithms for a large number of qubits and for various "noise" scenarios. The prime motivation for developing QCMPI is to facilitate numerical examination of not only how QC algorithms work, but also to include noise, decoherence, and attenuation effects and to evaluate the efficacy of error correction schemes. The present work builds on an earlier Mathematica code QDENSITY, which is mainly a pedagogic tool. In that earlier work, although the density matrix formulation was featured, the description using state vectors was also provided. In QCMPI, the stress is on state vectors, in order to employ a large number of qubits. The parallel processing feature is implemented by using the Message-Passing Interface (MPI) protocol. A description of how to spread the wave function components over many processors is provided, along with how to efficiently describe the action of general one- and two-qubit operators on these state vectors. These operators include the standard Pauli, Hadamard, CNOT and CPHASE gates and also Quantum Fourier transformation. These operators make up the actions needed in QC. Codes for Grover's search and Shor's factoring algorithms are provided as examples. A major feature of this work is that concurrent versions of the algorithms can be evaluated with each version subject to alternate noise effects, which corresponds to the idea of solving a stochastic Schrödinger equation. The density matrix for the ensemble of such noise cases is constructed using parallel distribution methods to evaluate its eigenvalues and associated entropy. Potential applications of this powerful tool include studies of the stability and correction of QC processes using Hamiltonian based dynamics. Program summaryProgram title: QCMPI Catalogue identifier: AECS_v1_0 Program summary URL
Scientific data analysis on data-parallel platforms.
Ulmer, Craig D.; Bayer, Gregory W.; Choe, Yung Ryn; Roe, Diana C.
2010-09-01
As scientific computing users migrate to petaflop platforms that promise to generate multi-terabyte datasets, there is a growing need in the community to be able to embed sophisticated analysis algorithms in the computing platforms' storage systems. Data Warehouse Appliances (DWAs) are attractive for this work, due to their ability to store and process massive datasets efficiently. While DWAs have been utilized effectively in data-mining and informatics applications, they remain largely unproven in scientific workloads. In this paper we present our experiences in adapting two mesh analysis algorithms to function on five different DWA architectures: two Netezza database appliances, an XtremeData dbX database, a LexisNexis DAS, and multiple Hadoop MapReduce clusters. The main contribution of this work is insight into the differences between these DWAs from a user's perspective. In addition, we present performance measurements for ten DWA systems to help understand the impact of different architectural trade-offs in these systems.
CFD research, parallel computation and aerodynamic optimization
NASA Technical Reports Server (NTRS)
Ryan, James S.
1995-01-01
Over five years of research in Computational Fluid Dynamics and its applications are covered in this report. Using CFD as an established tool, aerodynamic optimization on parallel architectures is explored. The objective of this work is to provide better tools to vehicle designers. Submarine design requires accurate force and moment calculations in flow with thick boundary layers and large separated vortices. Low noise production is critical, so flow into the propulsor region must be predicted accurately. The High Speed Civil Transport (HSCT) has been the subject of recent work. This vehicle is to be a passenger vehicle with the capability of cutting overseas flight times by more than half. A successful design must surpass the performance of comparable planes. Fuel economy, other operational costs, environmental impact, and range must all be improved substantially. For all these reasons, improved design tools are required, and these tools must eventually integrate optimization, external aerodynamics, propulsion, structures, heat transfer and other disciplines.
Broadcasting a message in a parallel computer
Archer, Charles J; Faraj, Ahmad A
2013-04-16
Methods, systems, and products are disclosed for broadcasting a message in a parallel computer that includes: transmitting, by the logical root to all of the nodes directly connected to the logical root, a message; and for each node except the logical root: receiving the message; if that node is the physical root, then transmitting the message to all of the child nodes except the child node from which the message was received; if that node received the message from a parent node and if that node is not a leaf node, then transmitting the message to all of the child nodes; and if that node received the message from a child node and if that node is not the physical root, then transmitting the message to all of the child nodes except the child node from which the message was received and transmitting the message to the parent node.
Broadcasting a message in a parallel computer
Archer, Charles J; Faraj, Daniel A
2014-11-18
Methods, systems, and products are disclosed for broadcasting a message in a parallel computer that includes: transmitting, by the logical root to all of the nodes directly connected to the logical root, a message; and for each node except the logical root: receiving the message; if that node is the physical root, then transmitting the message to all of the child nodes except the child node from which the message was received; if that node received the message from a parent node and if that node is not a leaf node, then transmitting the message to all of the child nodes; and if that node received the message from a child node and if that node is not the physical root, then transmitting the message to all of the child nodes except the child node from which the message was received and transmitting the message to the parent node.
Broadcasting collective operation contributions throughout a parallel computer
Faraj, Ahmad [Rochester, MN
2012-02-21
Methods, systems, and products are disclosed for broadcasting collective operation contributions throughout a parallel computer. The parallel computer includes a plurality of compute nodes connected together through a data communications network. Each compute node has a plurality of processors for use in collective parallel operations on the parallel computer. Broadcasting collective operation contributions throughout a parallel computer according to embodiments of the present invention includes: transmitting, by each processor on each compute node, that processor's collective operation contribution to the other processors on that compute node using intra-node communications; and transmitting on a designated network link, by each processor on each compute node according to a serial processor transmission sequence, that processor's collective operation contribution to the other processors on the other compute nodes using inter-node communications.
Application Specific Performance Technology for Productive Parallel Computing
Malony, Allen D.; Shende, Sameer
2008-09-30
Our accomplishments over the last three years of the DOE project Application- Specific Performance Technology for Productive Parallel Computing (DOE Agreement: DE-FG02-05ER25680) are described below. The project will have met all of its objectives by the time of its completion at the end of September, 2008. Two extensive yearly progress reports were produced in in March 2006 and 2007 and were previously submitted to the DOE Office of Advanced Scientific Computing Research (OASCR). Following an overview of the objectives of the project, we summarize for each of the project areas the achievements in the first two years, and then describe in some more detail the project accomplishments this past year. At the end, we discuss the relationship of the proposed renewal application to the work done on the current project.
Intel Woodcrest: An Evaluation for Scientific Computing
Roth, Philip C; Vetter, Jeffrey S
2007-01-01
Intel recently began shipping its Xeon 5100 series processors, formerly known by their 'Woodcrest' code name. To evaluate the suitability of the Woodcrest processor for high-end scientific computing, we obtained access to a Woodcrest-based system at Intel and measured its performance first using computation and memory micro-benchmarks, followed by full applications from the areas of climate modeling and molecular dynamics. For computational benchmarks, the Woodcrest showed excellent performance compared to a test system that uses Opteron processors from Advanced Micro Devices (AMD), though its performance advantage for full applications was less definitive. Nevertheless, our evaluation suggests the Woodcrest to be a compelling foundation for future leadership class systems for scientific computing.
SCE: Grid Environment for Scientific Computing
NASA Astrophysics Data System (ADS)
Xiao, Haili; Wu, Hong; Chi, Xuebin
Over the last few years Grid computing has evolved into an innovating technology and gotten increased commercial adoption. However, existing Grids do not have enough users as for sustainable development in the long term. This paper proposes several suggestions to this problem on the basis of long-term experience and careful analysis. The Scientific Computing Environment (SCE) in the Chinese Academy of Sciences is introduced as a completely new model and a feasible solution to this problem.
Parallel reservoir computing using optical amplifiers.
Vandoorne, Kristof; Dambre, Joni; Verstraeten, David; Schrauwen, Benjamin; Bienstman, Peter
2011-09-01
Reservoir computing (RC), a computational paradigm inspired on neural systems, has become increasingly popular in recent years for solving a variety of complex recognition and classification problems. Thus far, most implementations have been software-based, limiting their speed and power efficiency. Integrated photonics offers the potential for a fast, power efficient and massively parallel hardware implementation. We have previously proposed a network of coupled semiconductor optical amplifiers as an interesting test case for such a hardware implementation. In this paper, we investigate the important design parameters and the consequences of process variations through simulations. We use an isolated word recognition task with babble noise to evaluate the performance of the photonic reservoirs with respect to traditional software reservoir implementations, which are based on leaky hyperbolic tangent functions. Our results show that the use of coherent light in a well-tuned reservoir architecture offers significant performance benefits. The most important design parameters are the delay and the phase shift in the system's physical connections. With optimized values for these parameters, coherent semiconductor optical amplifier (SOA) reservoirs can achieve better results than traditional simulated reservoirs. We also show that process variations hardly degrade the performance, but amplifier noise can be detrimental. This effect must therefore be taken into account when designing SOA-based RC implementations.
I/O-Efficient Scientific Computation Using TPIE
NASA Technical Reports Server (NTRS)
Vengroff, Darren Erik; Vitter, Jeffrey Scott
1996-01-01
In recent years, input/output (I/O)-efficient algorithms for a wide variety of problems have appeared in the literature. However, systems specifically designed to assist programmers in implementing such algorithms have remained scarce. TPIE is a system designed to support I/O-efficient paradigms for problems from a variety of domains, including computational geometry, graph algorithms, and scientific computation. The TPIE interface frees programmers from having to deal not only with explicit read and write calls, but also the complex memory management that must be performed for I/O-efficient computation. In this paper we discuss applications of TPIE to problems in scientific computation. We discuss algorithmic issues underlying the design and implementation of the relevant components of TPIE and present performance results of programs written to solve a series of benchmark problems using our current TPIE prototype. Some of the benchmarks we present are based on the NAS parallel benchmarks while others are of our own creation. We demonstrate that the central processing unit (CPU) overhead required to manage I/O is small and that even with just a single disk, the I/O overhead of I/O-efficient computation ranges from negligible to the same order of magnitude as CPU time. We conjecture that if we use a number of disks in parallel this overhead can be all but eliminated.
Parallel CE/SE Computations via Domain Decomposition
NASA Technical Reports Server (NTRS)
Himansu, Ananda; Jorgenson, Philip C. E.; Wang, Xiao-Yen; Chang, Sin-Chung
2000-01-01
This paper describes the parallelization strategy and achieved parallel efficiency of an explicit time-marching algorithm for solving conservation laws. The Space-Time Conservation Element and Solution Element (CE/SE) algorithm for solving the 2D and 3D Euler equations is parallelized with the aid of domain decomposition. The parallel efficiency of the resultant algorithm on a Silicon Graphics Origin 2000 parallel computer is checked.
Parallel multi-computers and artificial intelligence
Uhr, L.
1986-01-01
This book examines the present state and future direction of multicomputer parallel architectures for artificial intelligence research and development of artificial intelligence applications. The book provides a survey of the large variety of parallel architectures, describing the current state of the art and suggesting promising architectures to produce artificial intelligence systems such as intelligence systems such as intelligent robots. This book integrates artificial intelligence and parallel processing research areas and discusses parallel processing from the viewpoint of artificial intelligence.
A Scientific Cloud Computing Platform for Condensed Matter Physics
NASA Astrophysics Data System (ADS)
Jorissen, K.; Johnson, W.; Vila, F. D.; Rehr, J. J.
2013-03-01
Scientific Cloud Computing (SCC) makes possible calculations with high performance computational tools, without the need to purchase or maintain sophisticated hardware and software. We have recently developed an interface dubbed SC2IT that controls on-demand virtual Linux clusters within the Amazon EC2 cloud platform. Using this interface we have developed a more advanced, user-friendly SCC Platform configured especially for condensed matter calculations. This platform contains a GUI, based on a new Java version of SC2IT, that permits calculations of various materials properties. The cloud platform includes Virtual Machines preconfigured for parallel calculations and several precompiled and optimized materials science codes for electronic structure and x-ray and electron spectroscopy. Consequently this SCC makes state-of-the-art condensed matter calculations easy to access for general users. Proof-of-principle performance benchmarks show excellent parallelization and communication performance. Supported by NSF grant OCI-1048052
A Simple Physical Optics Algorithm Perfect for Parallel Computing Architecture
NASA Technical Reports Server (NTRS)
Imbriale, W. A.; Cwik, T.
1994-01-01
A reflector antenna computer program based upon a simple discreet approximation of the radiation integral has proven to be extremely easy to adapt to the parallel computing architecture of the modest number of large-gain computing elements such as are used in the Intel iPSC and Touchstone Delta parallel machines.
A Simple Physical Optics Algorithm Perfect for Parallel Computing Architecture
NASA Technical Reports Server (NTRS)
Imbriale, W. A.; Cwik, T.
1994-01-01
A reflector antenna computer program based upon a simple discreet approximation of the radiation integral has proven to be extremely easy to adapt to the parallel computing architecture of the modest number of large-gain computing elements such as are used in the Intel iPSC and Touchstone Delta parallel machines.
Parallel CFD design on network-based computer
NASA Technical Reports Server (NTRS)
Cheung, Samson
1995-01-01
Combining multiple engineering workstations into a network-based heterogeneous parallel computer allows application of aerodynamic optimization with advanced computational fluid dynamics codes, which can be computationally expensive on mainframe supercomputers. This paper introduces a nonlinear quasi-Newton optimizer designed for this network-based heterogeneous parallel computing environment utilizing a software called Parallel Virtual Machine. This paper will introduce the methodology behind coupling a Parabolized Navier-Stokes flow solver to the nonlinear optimizer. This parallel optimization package is applied to reduce the wave drag of a body of revolution and a wing/body configuration with results of 5% to 6% drag reduction.
CFD Optimization on Network-Based Parallel Computer System
NASA Technical Reports Server (NTRS)
Cheung, Samson H.; VanDalsem, William (Technical Monitor)
1994-01-01
Combining multiple engineering workstations into a network-based heterogeneous parallel computer allows application of aerodynamic optimization with advance computational fluid dynamics codes, which is computationally expensive in mainframe supercomputer. This paper introduces a nonlinear quasi-Newton optimizer designed for this network-based heterogeneous parallel computer on a software called Parallel Virtual Machine. This paper will introduce the methodology behind coupling a Parabolized Navier-Stokes flow solver to the nonlinear optimizer. This parallel optimization package has been applied to reduce the wave drag of a body of revolution and a wing/body configuration with results of 5% to 6% drag reduction.
Parallel CFD design on network-based computer
NASA Technical Reports Server (NTRS)
Cheung, Samson
1995-01-01
Combining multiple engineering workstations into a network-based heterogeneous parallel computer allows application of aerodynamic optimization with advanced computational fluid dynamics codes, which can be computationally expensive on mainframe supercomputers. This paper introduces a nonlinear quasi-Newton optimizer designed for this network-based heterogeneous parallel computing environment utilizing a software called Parallel Virtual Machine. This paper will introduce the methodology behind coupling a Parabolized Navier-Stokes flow solver to the nonlinear optimizer. This parallel optimization package is applied to reduce the wave drag of a body of revolution and a wing/body configuration with results of 5% to 6% drag reduction.
CFD Optimization on Network-Based Parallel Computer System
NASA Technical Reports Server (NTRS)
Cheung, Samson H.; Holst, Terry L. (Technical Monitor)
1994-01-01
Combining multiple engineering workstations into a network-based heterogeneous parallel computer allows application of aerodynamic optimization with advance computational fluid dynamics codes, which is computationally expensive in mainframe supercomputer. This paper introduces a nonlinear quasi-Newton optimizer designed for this network-based heterogeneous parallel computer on a software called Parallel Virtual Machine. This paper will introduce the methodology behind coupling a Parabolized Navier-Stokes flow solver to the nonlinear optimizer. This parallel optimization package has been applied to reduce the wave drag of a body of revolution and a wing/body configuration with results of 5% to 6% drag reduction.
NASA Astrophysics Data System (ADS)
Ford, Eric B.; Dindar, Saleh; Peters, Jorg
2015-08-01
The realism of astrophysical simulations and statistical analyses of astronomical data are set by the available computational resources. Thus, astronomers and astrophysicists are constantly pushing the limits of computational capabilities. For decades, astronomers benefited from massive improvements in computational power that were driven primarily by increasing clock speeds and required relatively little attention to details of the computational hardware. For nearly a decade, increases in computational capabilities have come primarily from increasing the degree of parallelism, rather than increasing clock speeds. Further increases in computational capabilities will likely be led by many-core architectures such as Graphical Processing Units (GPUs) and Intel Xeon Phi. Successfully harnessing these new architectures, requires significantly more understanding of the hardware architecture, cache hierarchy, compiler capabilities and network network characteristics.I will provide an astronomer's overview of the opportunities and challenges provided by modern many-core architectures and elastic cloud computing. The primary goal is to help an astronomical audience understand what types of problems are likely to yield more than order of magnitude speed-ups and which problems are unlikely to parallelize sufficiently efficiently to be worth the development time and/or costs.I will draw on my experience leading a team in developing the Swarm-NG library for parallel integration of large ensembles of small n-body systems on GPUs, as well as several smaller software projects. I will share lessons learned from collaborating with computer scientists, including both technical and soft skills. Finally, I will discuss the challenges of training the next generation of astronomers to be proficient in this new era of high-performance computing, drawing on experience teaching a graduate class on High-Performance Scientific Computing for Astrophysics and organizing a 2014 advanced summer
Malony, Allen D; Shende, Sameer
2011-08-15
The primary goal of the University of Oregon's DOE "ÃÂcompetitiveness" project was to create performance technology that embodies and supports knowledge of performance data, analysis, and diagnosis in parallel performance problem solving. The target of our development activities was the TAU Performance System and the technology accomplishments reported in this and prior reports have all been incorporated in the TAU open software distribution. In addition, the project has been committed to maintaining strong interactions with the DOE SciDAC Performance Engineering Research Institute (PERI) and Center for Technology for Advanced Scientific Component Software (TASCS). This collaboration has proved valuable for translation of our knowledge-based performance techniques to parallel application development and performance engineering practice. Our outreach has also extended to the DOE Advanced CompuTational Software (ACTS) collection and project. Throughout the project we have participated in the PERI and TASCS meetings, as well as the ACTS annual workshops.
Scientific Computing Kernels on the Cell Processor
Williams, Samuel W.; Shalf, John; Oliker, Leonid; Kamil, Shoaib; Husbands, Parry; Yelick, Katherine
2007-04-04
The slowing pace of commodity microprocessor performance improvements combined with ever-increasing chip power demands has become of utmost concern to computational scientists. As a result, the high performance computing community is examining alternative architectures that address the limitations of modern cache-based designs. In this work, we examine the potential of using the recently-released STI Cell processor as a building block for future high-end computing systems. Our work contains several novel contributions. First, we introduce a performance model for Cell and apply it to several key scientific computing kernels: dense matrix multiply, sparse matrix vector multiply, stencil computations, and 1D/2D FFTs. The difficulty of programming Cell, which requires assembly level intrinsics for the best performance, makes this model useful as an initial step in algorithm design and evaluation. Next, we validate the accuracy of our model by comparing results against published hardware results, as well as our own implementations on a 3.2GHz Cell blade. Additionally, we compare Cell performance to benchmarks run on leading superscalar (AMD Opteron), VLIW (Intel Itanium2), and vector (Cray X1E) architectures. Our work also explores several different mappings of the kernels and demonstrates a simple and effective programming model for Cell's unique architecture. Finally, we propose modest microarchitectural modifications that could significantly increase the efficiency of double-precision calculations. Overall results demonstrate the tremendous potential of the Cell architecture for scientific computations in terms of both raw performance and power efficiency.
Parallel computing for probabilistic fatigue analysis
NASA Technical Reports Server (NTRS)
Sues, Robert H.; Lua, Yuan J.; Smith, Mark D.
1993-01-01
This paper presents the results of Phase I research to investigate the most effective parallel processing software strategies and hardware configurations for probabilistic structural analysis. We investigate the efficiency of both shared and distributed-memory architectures via a probabilistic fatigue life analysis problem. We also present a parallel programming approach, the virtual shared-memory paradigm, that is applicable across both types of hardware. Using this approach, problems can be solved on a variety of parallel configurations, including networks of single or multiprocessor workstations. We conclude that it is possible to effectively parallelize probabilistic fatigue analysis codes; however, special strategies will be needed to achieve large-scale parallelism to keep large number of processors busy and to treat problems with the large memory requirements encountered in practice. We also conclude that distributed-memory architecture is preferable to shared-memory for achieving large scale parallelism; however, in the future, the currently emerging hybrid-memory architectures will likely be optimal.
Performance Evaluation in Network-Based Parallel Computing
NASA Technical Reports Server (NTRS)
Dezhgosha, Kamyar
1996-01-01
Network-based parallel computing is emerging as a cost-effective alternative for solving many problems which require use of supercomputers or massively parallel computers. The primary objective of this project has been to conduct experimental research on performance evaluation for clustered parallel computing. First, a testbed was established by augmenting our existing SUNSPARCs' network with PVM (Parallel Virtual Machine) which is a software system for linking clusters of machines. Second, a set of three basic applications were selected. The applications consist of a parallel search, a parallel sort, a parallel matrix multiplication. These application programs were implemented in C programming language under PVM. Third, we conducted performance evaluation under various configurations and problem sizes. Alternative parallel computing models and workload allocations for application programs were explored. The performance metric was limited to elapsed time or response time which in the context of parallel computing can be expressed in terms of speedup. The results reveal that the overhead of communication latency between processes in many cases is the restricting factor to performance. That is, coarse-grain parallelism which requires less frequent communication between processes will result in higher performance in network-based computing. Finally, we are in the final stages of installing an Asynchronous Transfer Mode (ATM) switch and four ATM interfaces (each 155 Mbps) which will allow us to extend our study to newer applications, performance metrics, and configurations.
Performance Evaluation in Network-Based Parallel Computing
NASA Technical Reports Server (NTRS)
Dezhgosha, Kamyar
1996-01-01
Network-based parallel computing is emerging as a cost-effective alternative for solving many problems which require use of supercomputers or massively parallel computers. The primary objective of this project has been to conduct experimental research on performance evaluation for clustered parallel computing. First, a testbed was established by augmenting our existing SUNSPARCs' network with PVM (Parallel Virtual Machine) which is a software system for linking clusters of machines. Second, a set of three basic applications were selected. The applications consist of a parallel search, a parallel sort, a parallel matrix multiplication. These application programs were implemented in C programming language under PVM. Third, we conducted performance evaluation under various configurations and problem sizes. Alternative parallel computing models and workload allocations for application programs were explored. The performance metric was limited to elapsed time or response time which in the context of parallel computing can be expressed in terms of speedup. The results reveal that the overhead of communication latency between processes in many cases is the restricting factor to performance. That is, coarse-grain parallelism which requires less frequent communication between processes will result in higher performance in network-based computing. Finally, we are in the final stages of installing an Asynchronous Transfer Mode (ATM) switch and four ATM interfaces (each 155 Mbps) which will allow us to extend our study to newer applications, performance metrics, and configurations.
Singularity: Scientific containers for mobility of compute.
Kurtzer, Gregory M; Sochat, Vanessa; Bauer, Michael W
2017-01-01
Here we present Singularity, software developed to bring containers and reproducibility to scientific computing. Using Singularity containers, developers can work in reproducible environments of their choosing and design, and these complete environments can easily be copied and executed on other platforms. Singularity is an open source initiative that harnesses the expertise of system and software engineers and researchers alike, and integrates seamlessly into common workflows for both of these groups. As its primary use case, Singularity brings mobility of computing to both users and HPC centers, providing a secure means to capture and distribute software and compute environments. This ability to create and deploy reproducible environments across these centers, a previously unmet need, makes Singularity a game changing development for computational science.
Review of parallel computing methods and tools for FPGA technology
NASA Astrophysics Data System (ADS)
Cieszewski, Radosław; Linczuk, Maciej; Pozniak, Krzysztof; Romaniuk, Ryszard
2013-10-01
Parallel computing is emerging as an important area of research in computer architectures and software systems. Many algorithms can be greatly accelerated using parallel computing techniques. Specialized parallel computer architectures are used for accelerating speci c tasks. High-Energy Physics Experiments measuring systems often use FPGAs for ne-grained computation. FPGA combines many bene ts of both software and ASIC implementations. Like software, the mapped circuit is exible, and can be recon gured over the lifetime of the system. FPGAs therefore have the potential to achieve far greater performance than software as a result of bypassing the fetch-decode-execute operations of traditional processors, and possibly exploiting a greater level of parallelism. Creating parallel programs implemented in FPGAs is not trivial. This paper presents existing methods and tools for ne-grained computation implemented in FPGA using Behavioral Description and High Level Programming Languages.
Numerical recipes, The art of scientific computing
Press, W.H.; Flannery, B.P.; Teukolsky, S.; Vetterling, W.T.
1986-01-01
Seventeen chapter are divided into 130 sections provide a self-contained treatment that derives, critically discusses, and actually implements over 200 of the most important numerical algorithms for scientific work. Each algorithm is presented both in FORTRAN and Pascal, with the source programs printed in the book itself. The scope of Numerical Recipes ranges from standard areas of numerical analysis (linear algebra, differential equations, roots) through subjects useful to signal processing (Fourier methods, filtering), data analysis (least squares, robust fitting, statistical functions), simulation (random deviates and Monte Carlo). The routines themselves are available for a wide variety of different computers, from personal computers to mainframes, and are largely portable among different machines.
Parallel Computing and the IRAS Galaxy Atlas
NASA Astrophysics Data System (ADS)
Cao, Yu
1997-09-01
The Infrared Astronomical Satellite carried out a nearly complete survey of the infrared sky, and the survey data are important for the study of many astrophysical phenomena. However, many data sets at other wavelengths have higher resolutions than that of the co-added IRAS maps (4'-5'), and high resolution IRAS images are strongly desired both for their own information content and their usefulness in correlation studies. The HIRES program was developed by the Infrared Processing and Analysis Center (IPAC) to produce high resolution (~1') images from IRAS data using the maximum correlation method (MCM). We describe the port of HIRES to the Intel Paragon, a massively parallel supercomputer, and other software tools developed for mass production of HIRES images. striping and ringing artifacts. Correcting detector gain offsets in the reconstruction scheme was found to be effective in suppressing the striping artifacts. A variation of the destriping algorithm was used to subtract zodiacal emission. Using a Burg entropy metric in the image space gave good ringing suppression results for some test cases, but was found to have difficulties with photometry and resolution enhancement and hence not used in subsequent image production. A different ringing suppression algorithm was later developed, which aims to maximize cross log entropy between measured and modeled data. The algorithm suppresses point source ringing, and gave scientifically superior image for the α Ori test field. A partial convergence proof for the log entropy algorithm was achieved. HIRES images in the 60 and 100 μm wavelength bands were produced for the Galactic plane (-4.7o
Data communications in a parallel active messaging interface of a parallel computer
Archer, Charles J; Blocksome, Michael A; Ratterman, Joseph D; Smith, Brian E
2013-11-12
Data communications in a parallel active messaging interface (`PAMI`) of a parallel computer composed of compute nodes that execute a parallel application, each compute node including application processors that execute the parallel application and at least one management processor dedicated to gathering information regarding data communications. The PAMI is composed of data communications endpoints, each endpoint composed of a specification of data communications parameters for a thread of execution on a compute node, including specifications of a client, a context, and a task, the compute nodes and the endpoints coupled for data communications through the PAMI and through data communications resources. Embodiments function by gathering call site statistics describing data communications resulting from execution of data communications instructions and identifying in dependence upon the call cite statistics a data communications algorithm for use in executing a data communications instruction at a call site in the parallel application.
Parallel In Situ Indexing for Data-intensive Computing
Kim, Jinoh; Abbasi, Hasan; Chacon, Luis; Docan, Ciprian; Klasky, Scott; Liu, Qing; Podhorszki, Norbert; Shoshani, Arie; Wu, Kesheng
2011-09-09
As computing power increases exponentially, vast amount of data is created by many scientific re- search activities. However, the bandwidth for storing the data to disks and reading the data from disks has been improving at a much slower pace. These two trends produce an ever-widening data access gap. Our work brings together two distinct technologies to address this data access issue: indexing and in situ processing. From decades of database research literature, we know that indexing is an effective way to address the data access issue, particularly for accessing relatively small fraction of data records. As data sets increase in sizes, more and more analysts need to use selective data access, which makes indexing an even more important for improving data access. The challenge is that most implementations of in- dexing technology are embedded in large database management systems (DBMS), but most scientific datasets are not managed by any DBMS. In this work, we choose to include indexes with the scientific data instead of requiring the data to be loaded into a DBMS. We use compressed bitmap indexes from the FastBit software which are known to be highly effective for query-intensive workloads common to scientific data analysis. To use the indexes, we need to build them first. The index building procedure needs to access the whole data set and may also require a significant amount of compute time. In this work, we adapt the in situ processing technology to generate the indexes, thus removing the need of read- ing data from disks and to build indexes in parallel. The in situ data processing system used is ADIOS, a middleware for high-performance I/O. Our experimental results show that the indexes can improve the data access time up to 200 times depending on the fraction of data selected, and using in situ data processing system can effectively reduce the time needed to create the indexes, up to 10 times with our in situ technique when using identical parallel settings.
Access and visualization using clusters and other parallel computers
NASA Technical Reports Server (NTRS)
Katz, D. S.; Bergou, A.; Berriman, B.; Block, G.; Collier, J.; Curkendall, D.; Good, J.; Husman, L.; Jacob, J.; Laity, A.; Li, P.; Miller, C.; Plesea, L.; Prince, T.; Siegel, H.; Williams, R.
2003-01-01
JPL's Parallel Applications Technologies Group has been exploring the issues of data access and visualization of very large data sets over the past 10 years. this work has used a number of types of parallel computers, and today includes the use of commodity clusters. This talk will highlight some of the applications and tools we have developed, including how they use parallel computing resources, and specifically how we are using modern clusters.
Access and visualization using clusters and other parallel computers
NASA Technical Reports Server (NTRS)
Katz, D. S.; Bergou, A.; Berriman, B.; Block, G.; Collier, J.; Curkendall, D.; Good, J.; Husman, L.; Jacob, J.; Laity, A.;
2003-01-01
JPL's Parallel Applications Technologies Group has been exploring the issues of data access and visualization of very large data sets over the past 10 years. this work has used a number of types of parallel computers, and today includes the use of commodity clusters. This talk will highlight some of the applications and tools we have developed, including how they use parallel computing resources, and specifically how we are using modern clusters.
Scientific Visualization and Computational Science: Natural Partners
NASA Technical Reports Server (NTRS)
Uselton, Samuel P.; Lasinski, T. A. (Technical Monitor)
1995-01-01
Scientific visualization is developing rapidly, stimulated by computational science, which is gaining acceptance as a third alternative to theory and experiment. Computational science is based on numerical simulations of mathematical models derived from theory. But each individual simulation is like a hypothetical experiment; initial conditions are specified, and the result is a record of the observed conditions. Experiments can be simulated for situations that can not really be created or controlled. Results impossible to measure can be computed.. Even for observable values, computed samples are typically much denser. Numerical simulations also extend scientific exploration where the mathematics is analytically intractable. Numerical simulations are used to study phenomena from subatomic to intergalactic scales and from abstract mathematical structures to pragmatic engineering of everyday objects. But computational science methods would be almost useless without visualization. The obvious reason is that the huge amounts of data produced require the high bandwidth of the human visual system, and interactivity adds to the power. Visualization systems also provide a single context for all the activities involved from debugging the simulations, to exploring the data, to communicating the results. Most of the presentations today have their roots in image processing, where the fundamental task is: Given an image, extract information about the scene. Visualization has developed from computer graphics, and the inverse task: Given a scene description, make an image. Visualization extends the graphics paradigm by expanding the possible input. The goal is still to produce images; the difficulty is that the input is not a scene description displayable by standard graphics methods. Visualization techniques must either transform the data into a scene description or extend graphics techniques to display this odd input. Computational science is a fertile field for visualization
Scientific Visualization and Computational Science: Natural Partners
NASA Technical Reports Server (NTRS)
Uselton, Samuel P.; Lasinski, T. A. (Technical Monitor)
1995-01-01
Scientific visualization is developing rapidly, stimulated by computational science, which is gaining acceptance as a third alternative to theory and experiment. Computational science is based on numerical simulations of mathematical models derived from theory. But each individual simulation is like a hypothetical experiment; initial conditions are specified, and the result is a record of the observed conditions. Experiments can be simulated for situations that can not really be created or controlled. Results impossible to measure can be computed.. Even for observable values, computed samples are typically much denser. Numerical simulations also extend scientific exploration where the mathematics is analytically intractable. Numerical simulations are used to study phenomena from subatomic to intergalactic scales and from abstract mathematical structures to pragmatic engineering of everyday objects. But computational science methods would be almost useless without visualization. The obvious reason is that the huge amounts of data produced require the high bandwidth of the human visual system, and interactivity adds to the power. Visualization systems also provide a single context for all the activities involved from debugging the simulations, to exploring the data, to communicating the results. Most of the presentations today have their roots in image processing, where the fundamental task is: Given an image, extract information about the scene. Visualization has developed from computer graphics, and the inverse task: Given a scene description, make an image. Visualization extends the graphics paradigm by expanding the possible input. The goal is still to produce images; the difficulty is that the input is not a scene description displayable by standard graphics methods. Visualization techniques must either transform the data into a scene description or extend graphics techniques to display this odd input. Computational science is a fertile field for visualization
Parallel image computation in clusters with task-distributor.
Baun, Christian
2016-01-01
Distributed systems, especially clusters, can be used to execute ray tracing tasks in parallel for speeding up the image computation. Because ray tracing is a computational expensive and memory consuming task, ray tracing can also be used to benchmark clusters. This paper introduces task-distributor, a free software solution for the parallel execution of ray tracing tasks in distributed systems. The ray tracing solution used for this work is the Persistence Of Vision Raytracer (POV-Ray). Task-distributor does not require any modification of the POV-Ray source code or the installation of an additional message passing library like the Message Passing Interface or Parallel Virtual Machine to allow parallel image computation, in contrast to various other projects. By analyzing the runtime of the sequential and parallel program parts of task-distributor, it becomes clear how the problem size and available hardware resources influence the scaling of the parallel application.
Distributing an executable job load file to compute nodes in a parallel computer
Gooding, Thomas M.
2016-09-13
Distributing an executable job load file to compute nodes in a parallel computer, the parallel computer comprising a plurality of compute nodes, including: determining, by a compute node in the parallel computer, whether the compute node is participating in a job; determining, by the compute node in the parallel computer, whether a descendant compute node is participating in the job; responsive to determining that the compute node is participating in the job or that the descendant compute node is participating in the job, communicating, by the compute node to a parent compute node, an identification of a data communications link over which the compute node receives data from the parent compute node; constructing a class route for the job, wherein the class route identifies all compute nodes participating in the job; and broadcasting the executable load file for the job along the class route for the job.
Distributing an executable job load file to compute nodes in a parallel computer
Gooding, Thomas M.
2016-08-09
Distributing an executable job load file to compute nodes in a parallel computer, the parallel computer comprising a plurality of compute nodes, including: determining, by a compute node in the parallel computer, whether the compute node is participating in a job; determining, by the compute node in the parallel computer, whether a descendant compute node is participating in the job; responsive to determining that the compute node is participating in the job or that the descendant compute node is participating in the job, communicating, by the compute node to a parent compute node, an identification of a data communications link over which the compute node receives data from the parent compute node; constructing a class route for the job, wherein the class route identifies all compute nodes participating in the job; and broadcasting the executable load file for the job along the class route for the job.
Data communications in a parallel active messaging interface of a parallel computer
Archer, Charles J; Blocksome, Michael A; Ratterman, Joseph D; Smith, Brian E
2013-10-29
Data communications in a parallel active messaging interface (`PAMI`) of a parallel computer, the parallel computer including a plurality of compute nodes that execute a parallel application, the PAMI composed of data communications endpoints, each endpoint including a specification of data communications parameters for a thread of execution on a compute node, including specifications of a client, a context, and a task, the compute nodes and the endpoints coupled for data communications through the PAMI and through data communications resources, including receiving in an origin endpoint of the PAMI a data communications instruction, the instruction characterized by an instruction type, the instruction specifying a transmission of transfer data from the origin endpoint to a target endpoint and transmitting, in accordance with the instruction type, the transfer data from the origin endpoint to the target endpoint.
Data communications in a parallel active messaging interface of a parallel computer
Archer, Charles J; Blocksome, Michael A; Ratterman, Joseph D; Smith, Brian E
2014-02-11
Data communications in a parallel active messaging interface ('PAMI') or a parallel computer, the parallel computer including a plurality of compute nodes that execute a parallel application, the PAMI composed of data communications endpoints, each endpoint including a specification of data communications parameters for a thread of execution of a compute node, including specification of a client, a context, and a task, the compute nodes and the endpoints coupled for data communications instruction, the instruction characterized by instruction type, the instruction specifying a transmission of transfer data from the origin endpoint to a target endpoint and transmitting, in accordance witht the instruction type, the transfer data from the origin endpoin to the target endpoint.
Development of Message Passing Routines for High Performance Parallel Computations
NASA Technical Reports Server (NTRS)
Summers, Edward K.
2004-01-01
Computational Fluid Dynamics (CFD) calculations require a great deal of computing power for completing the detailed computations involved. In an effort shorten the time it takes to complete such calculations they are implemented on a parallel computer. In the case of a parallel computer some sort of message passing structure must be used to communicate between the computers because, unlike a single machine, each computer in a parallel computing cluster does not have access to all the data or run all the parts of the total program. Thus, message passing is used to divide up the data and send instructions to each machine. The nature of my work this summer involves programming the "message passing" aspect of the parallel computer. I am working on modifying an existing program, which was written with OpenMP, and does not use a multi-machine parallel computing structure, to work with Message Passing Interface (MPI) routines. The actual code is being written in the FORTRAN 90 programming language. My goal is to write a parameterized message passing structure that could be used for a variety of individual applications and implement it on Silicon Graphics Incorporated s (SGI) IRIX operating system. With this new parameterized structure engineers would be able to speed up computations for a wide variety of purposes without having to use larger and more expensive computing equipment from another division or another NASA center.
Development of Message Passing Routines for High Performance Parallel Computations
NASA Technical Reports Server (NTRS)
Summers, Edward K.
2004-01-01
Computational Fluid Dynamics (CFD) calculations require a great deal of computing power for completing the detailed computations involved. In an effort shorten the time it takes to complete such calculations they are implemented on a parallel computer. In the case of a parallel computer some sort of message passing structure must be used to communicate between the computers because, unlike a single machine, each computer in a parallel computing cluster does not have access to all the data or run all the parts of the total program. Thus, message passing is used to divide up the data and send instructions to each machine. The nature of my work this summer involves programming the "message passing" aspect of the parallel computer. I am working on modifying an existing program, which was written with OpenMP, and does not use a multi-machine parallel computing structure, to work with Message Passing Interface (MPI) routines. The actual code is being written in the FORTRAN 90 programming language. My goal is to write a parameterized message passing structure that could be used for a variety of individual applications and implement it on Silicon Graphics Incorporated s (SGI) IRIX operating system. With this new parameterized structure engineers would be able to speed up computations for a wide variety of purposes without having to use larger and more expensive computing equipment from another division or another NASA center.
Parallel, distributed and GPU computing technologies in single-particle electron microscopy
Schmeisser, Martin; Heisen, Burkhard C.; Luettich, Mario; Busche, Boris; Hauer, Florian; Koske, Tobias; Knauber, Karl-Heinz; Stark, Holger
2009-01-01
Most known methods for the determination of the structure of macromolecular complexes are limited or at least restricted at some point by their computational demands. Recent developments in information technology such as multicore, parallel and GPU processing can be used to overcome these limitations. In particular, graphics processing units (GPUs), which were originally developed for rendering real-time effects in computer games, are now ubiquitous and provide unprecedented computational power for scientific applications. Each parallel-processing paradigm alone can improve overall performance; the increased computational performance obtained by combining all paradigms, unleashing the full power of today’s technology, makes certain applications feasible that were previously virtually impossible. In this article, state-of-the-art paradigms are introduced, the tools and infrastructure needed to apply these paradigms are presented and a state-of-the-art infrastructure and solution strategy for moving scientific applications to the next generation of computer hardware is outlined. PMID:19564686
Parallel, distributed and GPU computing technologies in single-particle electron microscopy.
Schmeisser, Martin; Heisen, Burkhard C; Luettich, Mario; Busche, Boris; Hauer, Florian; Koske, Tobias; Knauber, Karl-Heinz; Stark, Holger
2009-07-01
Most known methods for the determination of the structure of macromolecular complexes are limited or at least restricted at some point by their computational demands. Recent developments in information technology such as multicore, parallel and GPU processing can be used to overcome these limitations. In particular, graphics processing units (GPUs), which were originally developed for rendering real-time effects in computer games, are now ubiquitous and provide unprecedented computational power for scientific applications. Each parallel-processing paradigm alone can improve overall performance; the increased computational performance obtained by combining all paradigms, unleashing the full power of today's technology, makes certain applications feasible that were previously virtually impossible. In this article, state-of-the-art paradigms are introduced, the tools and infrastructure needed to apply these paradigms are presented and a state-of-the-art infrastructure and solution strategy for moving scientific applications to the next generation of computer hardware is outlined.
Symplectic molecular dynamics simulations on specially designed parallel computers.
Borstnik, Urban; Janezic, Dusanka
2005-01-01
We have developed a computer program for molecular dynamics (MD) simulation that implements the Split Integration Symplectic Method (SISM) and is designed to run on specialized parallel computers. The MD integration is performed by the SISM, which analytically treats high-frequency vibrational motion and thus enables the use of longer simulation time steps. The low-frequency motion is treated numerically on specially designed parallel computers, which decreases the computational time of each simulation time step. The combination of these approaches means that less time is required and fewer steps are needed and so enables fast MD simulations. We study the computational performance of MD simulation of molecular systems on specialized computers and provide a comparison to standard personal computers. The combination of the SISM with two specialized parallel computers is an effective way to increase the speed of MD simulations up to 16-fold over a single PC processor.
AVES: A Computer Cluster System approach for INTEGRAL Scientific Analysis
NASA Astrophysics Data System (ADS)
Federici, M.; Martino, B. L.; Natalucci, L.; Umbertini, P.
The AVES computing system, based on an "Cluster" architecture is a fully integrated, low cost computing facility dedicated to the archiving and analysis of the INTEGRAL data. AVES is a modular system that uses the software resource manager (SLURM) and allows almost unlimited expandibility (65,536 nodes and hundreds of thousands of processors); actually is composed by 30 Personal Computers with Quad-Cores CPU able to reach the computing power of 300 Giga Flops (300x10{9} Floating point Operations Per Second), with 120 GB of RAM and 7.5 Tera Bytes (TB) of storage memory in UFS configuration plus 6 TB for users area. AVES was designed and built to solve growing problems raised from the analysis of the large data amount accumulated by the INTEGRAL mission (actually about 9 TB) and due to increase every year. The used analysis software is the OSA package, distributed by the ISDC in Geneva. This is a very complex package consisting of dozens of programs that can not be converted to parallel computing. To overcome this limitation we developed a series of programs to distribute the workload analysis on the various nodes making AVES automatically divide the analysis in N jobs sent to N cores. This solution thus produces a result similar to that obtained by the parallel computing configuration. In support of this we have developed tools that allow a flexible use of the scientific software and quality control of on-line data storing. The AVES software package is constituted by about 50 specific programs. Thus the whole computing time, compared to that provided by a Personal Computer with single processor, has been enhanced up to a factor 70.
NASA Astrophysics Data System (ADS)
Murni, Bustamam, A.; Ernastuti, Handhika, T.; Kerami, D.
2017-07-01
Calculation of the matrix-vector multiplication in the real-world problems often involves large matrix with arbitrary size. Therefore, parallelization is needed to speed up the calculation process that usually takes a long time. Graph partitioning techniques that have been discussed in the previous studies cannot be used to complete the parallelized calculation of matrix-vector multiplication with arbitrary size. This is due to the assumption of graph partitioning techniques that can only solve the square and symmetric matrix. Hypergraph partitioning techniques will overcome the shortcomings of the graph partitioning technique. This paper addresses the efficient parallelization of matrix-vector multiplication through hypergraph partitioning techniques using CUDA GPU-based parallel computing. CUDA (compute unified device architecture) is a parallel computing platform and programming model that was created by NVIDIA and implemented by the GPU (graphics processing unit).
A scalable parallel black oil simulator on distributed memory parallel computers
NASA Astrophysics Data System (ADS)
Wang, Kun; Liu, Hui; Chen, Zhangxin
2015-11-01
This paper presents our work on developing a parallel black oil simulator for distributed memory computers based on our in-house parallel platform. The parallel simulator is designed to overcome the performance issues of common simulators that are implemented for personal computers and workstations. The finite difference method is applied to discretize the black oil model. In addition, some advanced techniques are employed to strengthen the robustness and parallel scalability of the simulator, including an inexact Newton method, matrix decoupling methods, and algebraic multigrid methods. A new multi-stage preconditioner is proposed to accelerate the solution of linear systems from the Newton methods. Numerical experiments show that our simulator is scalable and efficient, and is capable of simulating extremely large-scale black oil problems with tens of millions of grid blocks using thousands of MPI processes on parallel computers.
Algorithms for parallel and vector computations
NASA Technical Reports Server (NTRS)
Ortega, James M.
1995-01-01
This is a final report on work performed under NASA grant NAG-1-1112-FOP during the period March, 1990 through February 1995. Four major topics are covered: (1) solution of nonlinear poisson-type equations; (2) parallel reduced system conjugate gradient method; (3) orderings for conjugate gradient preconditioners, and (4) SOR as a preconditioner.
Dynamic traffic assignment on parallel computers
Nagel, K.; Frye, R.; Jakob, R.; Rickert, M.; Stretz, P.
1998-12-01
The authors describe part of the current framework of the TRANSIMS traffic research project at the Los Alamos National Laboratory. It includes parallel implementations of a route planner and a microscopic traffic simulation model. They present performance figures and results of an offline load-balancing scheme used in one of the iterative re-planning runs required for dynamic route assignment.
Research on Web-based Scientific Computing Legacy Application Sharing
NASA Astrophysics Data System (ADS)
Zhang, Yong; Cui, Bin-Ge
With the development of Internet technology, A legion of scientific computing legacy programs with rich domain knowledge and expertise were distributed across various disciplines. As the program implementations or interfaces and so on, scientific computing legacy programs can not be shared through the Internet. This paper proposes a method of packaging scientific computing legacy programs into DLL(Dynamic Link Library), and packaging them into Web services through the C# reflection, making the scientific computing legacy programs successfully share on the Internet.
Large scale scientific computing - future directions
NASA Astrophysics Data System (ADS)
Patterson, G. S.
1982-06-01
Every new generation of scientific computers has opened up new areas of science for exploration through the use of more realistic numerical models or the ability to process ever larger amounts of data. Concomitantly, scientists, because of the success of past models and the wide range of physical phenomena left unexplored, have pressed computer designers to strive for the maximum performance that current technology will permit. This encompasses not only increased processor speed, but also substantial improvements in processor memory, I/O bandwidth, secondary storage and facilities to augment the scientist's ability both to program and to understand the results of a computation. Over the past decade, performance improvements for scientific calculations have come from algoeithm development and a major change in the underlying architecture of the hardware, not from significantly faster circuitry. It appears that this trend will continue for another decade. A future archetectural change for improved performance will most likely be multiple processors coupled together in some fashion. Because the demand for a significantly more powerful computer system comes from users with single large applications, it is essential that an application be efficiently partitionable over a set of processors; otherwise, a multiprocessor system will not be effective. This paper explores some of the constraints on multiple processor architecture posed by these large applications. In particular, the trade-offs between large numbers of slow processors and small numbers of fast processors is examined. Strategies for partitioning range from partitioning at the language statement level (in-the-small) and at the program module level (in-the-large). Some examples of partitioning in-the-large are given and a strategy for efficiently executing a partitioned program is explored.
Parallel algorithms and architecture for computation of manipulator forward dynamics
NASA Technical Reports Server (NTRS)
Fijany, Amir; Bejczy, Antal K.
1989-01-01
Parallel computation of manipulator forward dynamics is investigated. Considering three classes of algorithms for the solution of the problem, that is, the O(n), the O(n exp 2), and the O(n exp 3) algorithms, parallelism in the problem is analyzed. It is shown that the problem belongs to the class of NC and that the time and processors bounds are of O(log2/2n) and O(n exp 4), respectively. However, the fastest stable parallel algorithms achieve the computation time of O(n) and can be derived by parallelization of the O(n exp 3) serial algorithms. Parallel computation of the O(n exp 3) algorithms requires the development of parallel algorithms for a set of fundamentally different problems, that is, the Newton-Euler formulation, the computation of the inertia matrix, decomposition of the symmetric, positive definite matrix, and the solution of triangular systems. Parallel algorithms for this set of problems are developed which can be efficiently implemented on a unique architecture, a triangular array of n(n+2)/2 processors with a simple nearest-neighbor interconnection. This architecture is particularly suitable for VLSI and WSI implementations. The developed parallel algorithm, compared to the best serial O(n) algorithm, achieves an asymptotic speedup of more than two orders-of-magnitude in the computation the forward dynamics.
Performance issues for engineering analysis on MIMD parallel computers
Fang, H.E.; Vaughan, C.T.; Gardner, D.R.
1994-08-01
We discuss how engineering analysts can obtain greater computational resolution in a more timely manner from applications codes running on MIMD parallel computers. Both processor speed and memory capacity are important to achieving better performance than a serial vector supercomputer. To obtain good performance, a parallel applications code must be scalable. In addition, the aspect ratios of the subdomains in the decomposition of the simulation domain onto the parallel computer should be of order 1. We demonstrate these conclusions using simulations conducted with the PCTH shock wave physics code running on a Cray Y-MP, a 1024-node nCUBE 2, and an 1840-node Paragon.
Massively parallel computing at Sandia and its application to national defense
Dosanjh, S.S.
1991-01-01
Two years ago, researchers at Sandia National Laboratories showed that a massively parallel computer with 1024 processors could solve scientific problems more than 1000 times faster than a single processor. Since then, interest in massively parallel processing has increased dramatically. This review paper discusses some of the applications of this emerging technology to important problems at Sandia. Particular attention is given here to the impact of massively parallel systems on applications related to national defense. New concepts in heterogenous programming and load balancing for MIMD computers are drastically increasing synthetic aperture radar (SAR) and SDI modeling capabilities. Also, researchers are showing that the current generation of massively parallel MIMD and SIMD computers are highly competitive with a CRAY on hydrodynamic and structural mechanics codes that are optimized for vector processors. 9 refs., 5 figs., 1 tab.
75 FR 64720 - DOE/Advanced Scientific Computing Advisory Committee
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Computational Biology, Advanced Scientific Computing, and Emerging Computational Architectures
2007-06-27
This CRADA was established at the start of FY02 with $200 K from IBM and matching funds from DOE to support post-doctoral fellows in collaborative research between International Business Machines and Oak Ridge National Laboratory to explore effective use of emerging petascale computational architectures for the solution of computational biology problems. 'No cost' extensions of the CRADA were negotiated with IBM for FY03 and FY04.
Mathematical model partitioning and packing for parallel computer calculation
NASA Technical Reports Server (NTRS)
Arpasi, Dale J.; Milner, Edward J.
1986-01-01
This paper deals with the development of multiprocessor simulations from a serial set of ordinary differential equations describing a physical system. The identification of computational parallelism within the model equations is discussed. A technique is presented for identifying this parallelism and for partitioning the equations for parallel solution on a multiprocessor. Next, an algorithm which packs the equations into a minimum number of processors is described. The results of applying the packing algorithm to a turboshaft engine model are presented.
Mathematical model partitioning and packing for parallel computer calculation
NASA Technical Reports Server (NTRS)
Arpasi, Dale J.; Milner, Edward J.
1986-01-01
This paper deals with the development of multiprocessor simulations from a serial set of ordinary differential equations describing a physical system. The identification of computational parallelism within the model equations is discussed. A technique is presented for identifying this parallelism and for partitioning the equations for parallel solution on a multiprocessor. Next, an algorithm which packs the equations into a minimum number of processors is described. The results of applying the packing algorithm to a turboshaft engine model are presented.
Java Performance for Scientific Applications on LLNL Computer Systems
Kapfer, C; Wissink, A
2002-05-10
Languages in use for high performance computing at the laboratory--Fortran (f77 and f90), C, and C++--have many years of development behind them and are generally considered the fastest available. However, Fortran and C do not readily extend to object-oriented programming models, limiting their capability for very complex simulation software. C++ facilitates object-oriented programming but is a very complex and error-prone language. Java offers a number of capabilities that these other languages do not. For instance it implements cleaner (i.e., easier to use and less prone to errors) object-oriented models than C++. It also offers networking and security as part of the language standard, and cross-platform executables that make it architecture neutral, to name a few. These features have made Java very popular for industrial computing applications. The aim of this paper is to explain the trade-offs in using Java for large-scale scientific applications at LLNL. Despite its advantages, the computational science community has been reluctant to write large-scale computationally intensive applications in Java due to concerns over its poor performance. However, considerable progress has been made over the last several years. The Java Grande Forum [1] has been promoting the use of Java for large-scale computing. Members have introduced efficient array libraries, developed fast just-in-time (JIT) compilers, and built links to existing packages used in high performance parallel computing.
Super and parallel computers and their impact on civil engineering
Kamat, M.P.
1986-01-01
This book presents the papers given at a conference on the use of supercomputers in civil engineering. Topics considered at the conference included solving nonlinear equations on a hypercube, a custom architectured parallel processing system, distributed data processing, algorithms, computer architecture, parallel processing, vector processing, computerized simulation, and cost benefit analysis.
Research in Parallel Algorithms and Software for Computational Aerosciences
NASA Technical Reports Server (NTRS)
Domel, Neal D.
1996-01-01
Phase 1 is complete for the development of a computational fluid dynamics CFD) parallel code with automatic grid generation and adaptation for the Euler analysis of flow over complex geometries. SPLITFLOW, an unstructured Cartesian grid code developed at Lockheed Martin Tactical Aircraft Systems, has been modified for a distributed memory/massively parallel computing environment. The parallel code is operational on an SGI network, Cray J90 and C90 vector machines, SGI Power Challenge, and Cray T3D and IBM SP2 massively parallel machines. Parallel Virtual Machine (PVM) is the message passing protocol for portability to various architectures. A domain decomposition technique was developed which enforces dynamic load balancing to improve solution speed and memory requirements. A host/node algorithm distributes the tasks. The solver parallelizes very well, and scales with the number of processors. Partially parallelized and non-parallelized tasks consume most of the wall clock time in a very fine grain environment. Timing comparisons on a Cray C90 demonstrate that Parallel SPLITFLOW runs 2.4 times faster on 8 processors than its non-parallel counterpart autotasked over 8 processors.
Research in Parallel Algorithms and Software for Computational Aerosciences
NASA Technical Reports Server (NTRS)
Domel, Neal D.
1996-01-01
Phase I is complete for the development of a Computational Fluid Dynamics parallel code with automatic grid generation and adaptation for the Euler analysis of flow over complex geometries. SPLITFLOW, an unstructured Cartesian grid code developed at Lockheed Martin Tactical Aircraft Systems, has been modified for a distributed memory/massively parallel computing environment. The parallel code is operational on an SGI network, Cray J90 and C90 vector machines, SGI Power Challenge, and Cray T3D and IBM SP2 massively parallel machines. Parallel Virtual Machine (PVM) is the message passing protocol for portability to various architectures. A domain decomposition technique was developed which enforces dynamic load balancing to improve solution speed and memory requirements. A host/node algorithm distributes the tasks. The solver parallelizes very well, and scales with the number of processors. Partially parallelized and non-parallelized tasks consume most of the wall clock time in a very fine grain environment. Timing comparisons on a Cray C90 demonstrate that Parallel SPLITFLOW runs 2.4 times faster on 8 processors than its non-parallel counterpart autotasked over 8 processors.
QCD on the Massively Parallel Computer AP1000
NASA Astrophysics Data System (ADS)
Akemi, K.; Fujisaki, M.; Okuda, M.; Tago, Y.; Hashimoto, T.; Hioki, S.; Miyamura, O.; Takaishi, T.; Nakamura, A.; de Forcrand, Ph.; Hege, C.; Stamatescu, I. O.
We present the QCD-TARO program of calculations which uses the parallel computer AP1000 of Fujitsu. We discuss the results on scaling, correlation times and hadronic spectrum, some aspects of the implementation and the future prospects.
Fluid dynamics parallel computer development at NASA Langley Research Center
NASA Technical Reports Server (NTRS)
Townsend, James C.; Zang, Thomas A.; Dwoyer, Douglas L.
1987-01-01
To accomplish more detailed simulations of highly complex flows, such as the transition to turbulence, fluid dynamics research requires computers much more powerful than any available today. Only parallel processing on multiple-processor computers offers hope for achieving the required effective speeds. Looking ahead to the use of these machines, the fluid dynamicist faces three issues: algorithm development for near-term parallel computers, architecture development for future computer power increases, and assessment of possible advantages of special purpose designs. Two projects at NASA Langley address these issues. Software development and algorithm exploration is being done on the FLEX/32 Parallel Processing Research Computer. New architecture features are being explored in the special purpose hardware design of the Navier-Stokes Computer. These projects are complementary and are producing promising results.
Fluid dynamics parallel computer development at NASA Langley Research Center
NASA Technical Reports Server (NTRS)
Townsend, James C.; Zang, Thomas A.; Dwoyer, Douglas L.
1987-01-01
To accomplish more detailed simulations of highly complex flows, such as the transition to turbulence, fluid dynamics research requires computers much more powerful than any available today. Only parallel processing on multiple-processor computers offers hope for achieving the required effective speeds. Looking ahead to the use of these machines, the fluid dynamicist faces three issues: algorithm development for near-term parallel computers, architecture development for future computer power increases, and assessment of possible advantages of special purpose designs. Two projects at NASA Langley address these issues. Software development and algorithm exploration is being done on the FLEX/32 Parallel Processing Research Computer. New architecture features are being explored in the special purpose hardware design of the Navier-Stokes Computer. These projects are complementary and are producing promising results.
A Testbed of Parallel Kernels for Computer Science Research
Bailey, David; Demmel, James; Ibrahim, Khaled; Kaiser, Alex; Koniges, Alice; Madduri, Kamesh; Shalf, John; Strohmaier, Erich; Williams, Samuel
2010-04-30
For several decades, computer scientists have sought guidance on how to evolve architectures, languages, and programming models for optimal performance, efficiency, and productivity. Unfortunately, this guidance is most often taken from the existing software/hardware ecosystem. Architects attempt to provide micro-architectural solutions to improve performance on fixed binaries. Researchers tweak compilers to improve code generation for existing architectures and implementations, and they may invent new programming models for fixed processor and memory architectures and computational algorithms. In today's rapidly evolving world of on-chip parallelism, these isolated and iterative improvements to performance may miss superior solutions in the same way gradient descent optimization techniques may get stuck in local minima. In an initial study, we have developed an alternate approach that, rather than starting with an existing hardware/software solution laced with hidden assumptions, defines the computational problems of interest and invites architects, researchers and programmers to implement novel hardware/ software co-designed solutions. Our work builds on the previous ideas of computational dwarfs, motifs, and parallel patterns by selecting a representative set of essential problems for which we provide: An algorithmic description; scalable problem definition; illustrative reference implementations; verification schemes. For simplicity, we focus initially on the computational problems of interest to the scientific computing community but proclaim the methodology (and perhaps a subset of the problems) as applicable to other communities. We intend to broaden the coverage of this problem space through stronger community involvement. Previous work has established a broad categorization of numerical methods of interest to the scientific computing, in the spirit of the NAS Benchmarks, which pioneered the basic idea of a 'pencil and paper benchmark' in the 1990s. The
Integrating multiple scientific computing needs via a Private Cloud infrastructure
NASA Astrophysics Data System (ADS)
Bagnasco, S.; Berzano, D.; Brunetti, R.; Lusso, S.; Vallero, S.
2014-06-01
In a typical scientific computing centre, diverse applications coexist and share a single physical infrastructure. An underlying Private Cloud facility eases the management and maintenance of heterogeneous use cases such as multipurpose or application-specific batch farms, Grid sites catering to different communities, parallel interactive data analysis facilities and others. It allows to dynamically and efficiently allocate resources to any application and to tailor the virtual machines according to the applications' requirements. Furthermore, the maintenance of large deployments of complex and rapidly evolving middleware and application software is eased by the use of virtual images and contextualization techniques; for example, rolling updates can be performed easily and minimizing the downtime. In this contribution we describe the Private Cloud infrastructure at the INFN-Torino Computer Centre, that hosts a full-fledged WLCG Tier-2 site and a dynamically expandable PROOF-based Interactive Analysis Facility for the ALICE experiment at the CERN LHC and several smaller scientific computing applications. The Private Cloud building blocks include the OpenNebula software stack, the GlusterFS filesystem (used in two different configurations for worker- and service-class hypervisors) and the OpenWRT Linux distribution (used for network virtualization). A future integration into a federated higher-level infrastructure is made possible by exposing commonly used APIs like EC2 and by using mainstream contextualization tools like CloudInit.
Parallel Computation for Developing Nonlinear Control Procedures.
1981-07-01
optimal and subcptimal control systems. The early ’crk in the area cf zara :-eter :ientification can be attributed to Nyquist [i and 3ode [27 in which...line in an adaptive fashion . It should be emnhasized that the goal of this chapter is tz .evelo.p algorith--ms hich possess a high degree of -zrzale.ism...control in an adaptive fashion . Note that the major goal is to utilize these parallel algorithms in an explicit adaptive controller of the type shown in
Serial multiplier arrays for parallel computation
NASA Technical Reports Server (NTRS)
Winters, Kel
1990-01-01
Arrays of systolic serial-parallel multiplier elements are proposed as an alternative to conventional SIMD mesh serial adder arrays for applications that are multiplication intensive and require few stored operands. The design and operation of a number of multiplier and array configurations featuring locality of connection, modularity, and regularity of structure are discussed. A design methodology combining top-down and bottom-up techniques is described to facilitate development of custom high-performance CMOS multiplier element arrays as well as rapid synthesis of simulation models and semicustom prototype CMOS components. Finally, a differential version of NORA dynamic circuits requiring a single-phase uncomplemented clock signal introduced for this application.
Climate Ocean Modeling on Parallel Computers
NASA Technical Reports Server (NTRS)
Wang, P.; Cheng, B. N.; Chao, Y.
1998-01-01
Ocean modeling plays an important role in both understanding the current climatic conditions and predicting future climate change. However, modeling the ocean circulation at various spatial and temporal scales is a very challenging computational task.
The Design and Evaluation of "CAPTools"--A Computer Aided Parallelization Toolkit
NASA Technical Reports Server (NTRS)
Yan, Jerry; Frumkin, Michael; Hribar, Michelle; Jin, Haoqiang; Waheed, Abdul; Johnson, Steve; Cross, Jark; Evans, Emyr; Ierotheou, Constantinos; Leggett, Pete;
1998-01-01
Writing applications for high performance computers is a challenging task. Although writing code by hand still offers the best performance, it is extremely costly and often not very portable. The Computer Aided Parallelization Tools (CAPTools) are a toolkit designed to help automate the mapping of sequential FORTRAN scientific applications onto multiprocessors. CAPTools consists of the following major components: an inter-procedural dependence analysis module that incorporates user knowledge; a 'self-propagating' data partitioning module driven via user guidance; an execution control mask generation and optimization module for the user to fine tune parallel processing of individual partitions; a program transformation/restructuring facility for source code clean up and optimization; a set of browsers through which the user interacts with CAPTools at each stage of the parallelization process; and a code generator supporting multiple programming paradigms on various multiprocessors. Besides describing the rationale behind the architecture of CAPTools, the parallelization process is illustrated via case studies involving structured and unstructured meshes. The programming process and the performance of the generated parallel programs are compared against other programming alternatives based on the NAS Parallel Benchmarks, ARC3D and other scientific applications. Based on these results, a discussion on the feasibility of constructing architectural independent parallel applications is presented.
Routing Application for Parallel computatIon of Discharge
NASA Astrophysics Data System (ADS)
David, C. H.; Maidment, D. R.; Yang, Z.
2008-12-01
Today, meteorological models can predict storm events and climate patterns, but there are few models that connect atmospheric models to the hydraulics of rivers. Such a model is necessary to advance the prediction of events such as floods or droughts. Furthermore, the increasingly available Geographic Information System-based hydrographic datasets offer ways to use actual mapped river for routing. Such GIS-based datasets include NHDPlus at the continental-scale [USEPA and USGS, 2007]and HydroSHEDS at the global- scale [Lehner, et al., 2006]. The objective of ongoing work is to develop RAPID (Routing Application for Parallel computatIon of Discharge), a large-scale river routing model that: - has physical representation of river flow - allows for coupling with both land surface models and groundwater models. In particular enabling bi-directional exchanges between rivers and aquifers through a computation of water volume and flow on a reach-to-reach basis - has some specific treatment for man-made infrastructures and anthropogenic actions (dams, pumping) - benefits from the latest scientific computing developments (supercomputing facilities and high performance parallel computing libraries) - will benefit from the increasingly available Geographic Information System hydrographic databases. RAPID has already been tested over France through a ten-year coupling with SIM-France [Habets, et al., 2008, using a river network made of 24,264 river reaches. RAPID has been adapted to run on the Lonestar supercomputer (http://www.tacc.utexas.edu/resources/hpcsystems/) and to allow the use of the NHDPlus dataset. RAPID is now being tested with the 74,615 river reaches of the entire Texas Gulf. This type of innovative model has strong implications for the future of hydrology. Such a tool can help improve the understanding of the effect of climate change on water resources as well as provide information on how many gages are needed and where are they needed the most. More broadly
Use of parallel computing in mass processing of laser data
NASA Astrophysics Data System (ADS)
Będkowski, J.; Bratuś, R.; Prochaska, M.; Rzonca, A.
2015-12-01
The first part of the paper includes a description of the rules used to generate the algorithm needed for the purpose of parallel computing and also discusses the origins of the idea of research on the use of graphics processors in large scale processing of laser scanning data. The next part of the paper includes the results of an efficiency assessment performed for an array of different processing options, all of which were substantially accelerated with parallel computing. The processing options were divided into the generation of orthophotos using point clouds, coloring of point clouds, transformations, and the generation of a regular grid, as well as advanced processes such as the detection of planes and edges, point cloud classification, and the analysis of data for the purpose of quality control. Most algorithms had to be formulated from scratch in the context of the requirements of parallel computing. A few of the algorithms were based on existing technology developed by the Dephos Software Company and then adapted to parallel computing in the course of this research study. Processing time was determined for each process employed for a typical quantity of data processed, which helped confirm the high efficiency of the solutions proposed and the applicability of parallel computing to the processing of laser scanning data. The high efficiency of parallel computing yields new opportunities in the creation and organization of processing methods for laser scanning data.
Programming Probabilistic Structural Analysis for Parallel Processing Computer
NASA Technical Reports Server (NTRS)
Sues, Robert H.; Chen, Heh-Chyun; Twisdale, Lawrence A.; Chamis, Christos C.; Murthy, Pappu L. N.
1991-01-01
The ultimate goal of this research program is to make Probabilistic Structural Analysis (PSA) computationally efficient and hence practical for the design environment by achieving large scale parallelism. The paper identifies the multiple levels of parallelism in PSA, identifies methodologies for exploiting this parallelism, describes the development of a parallel stochastic finite element code, and presents results of two example applications. It is demonstrated that speeds within five percent of those theoretically possible can be achieved. A special-purpose numerical technique, the stochastic preconditioned conjugate gradient method, is also presented and demonstrated to be extremely efficient for certain classes of PSA problems.
Parallel Computing for Probabilistic Response Analysis of High Temperature Composites
NASA Technical Reports Server (NTRS)
Sues, R. H.; Lua, Y. J.; Smith, M. D.
1994-01-01
The objective of this Phase I research was to establish the required software and hardware strategies to achieve large scale parallelism in solving PCM problems. To meet this objective, several investigations were conducted. First, we identified the multiple levels of parallelism in PCM and the computational strategies to exploit these parallelisms. Next, several software and hardware efficiency investigations were conducted. These involved the use of three different parallel programming paradigms and solution of two example problems on both a shared-memory multiprocessor and a distributed-memory network of workstations.
Parallel aeroelastic computations for wing and wing-body configurations
NASA Technical Reports Server (NTRS)
Byun, Chansup
1994-01-01
The objective of this research is to develop computationally efficient methods for solving fluid-structural interaction problems by directly coupling finite difference Euler/Navier-Stokes equations for fluids and finite element dynamics equations for structures on parallel computers. This capability will significantly impact many aerospace projects of national importance such as Advanced Subsonic Civil Transport (ASCT), where the structural stability margin becomes very critical at the transonic region. This research effort will have direct impact on the High Performance Computing and Communication (HPCC) Program of NASA in the area of parallel computing.
History Matching in Parallel Computational Environments
Steven Bryant; Sanjay Srinivasan; Alvaro Barrera; Sharad Yadav
2004-08-31
In the probabilistic approach for history matching, the information from the dynamic data is merged with the prior geologic information in order to generate permeability models consistent with the observed dynamic data as well as the prior geology. The relationship between dynamic response data and reservoir attributes may vary in different regions of the reservoir due to spatial variations in reservoir attributes, fluid properties, well configuration, flow constrains on wells etc. This implies probabilistic approach should then update different regions of the reservoir in different ways. This necessitates delineation of multiple reservoir domains in order to increase the accuracy of the approach. The research focuses on a probabilistic approach to integrate dynamic data that ensures consistency between reservoir models developed from one stage to the next. The algorithm relies on efficient parameterization of the dynamic data integration problem and permits rapid assessment of the updated reservoir model at each stage. The report also outlines various domain decomposition schemes from the perspective of increasing the accuracy of probabilistic approach of history matching. Research progress in three important areas of the project are discussed: {lg_bullet}Validation and testing the probabilistic approach to incorporating production data in reservoir models. {lg_bullet}Development of a robust scheme for identifying reservoir regions that will result in a more robust parameterization of the history matching process. {lg_bullet}Testing commercial simulators for parallel capability and development of a parallel algorithm for history matching.
Parallel Computation of the Topology of Level Sets
Pascucci, V; Cole-McLaughlin, K
2004-12-16
This paper introduces two efficient algorithms that compute the Contour Tree of a 3D scalar field F and its augmented version with the Betti numbers of each isosurface. The Contour Tree is a fundamental data structure in scientific visualization that is used to preprocess the domain mesh to allow optimal computation of isosurfaces with minimal overhead storage. The Contour Tree can also be used to build user interfaces reporting the complete topological characterization of a scalar field, as shown in Figure 1. Data exploration time is reduced since the user understands the evolution of level set components with changing isovalue. The Augmented Contour Tree provides even more accurate information segmenting the range space of the scalar field in portion of invariant topology. The exploration time for a single isosurface is also improved since its genus is known in advance. Our first new algorithm augments any given Contour Tree with the Betti numbers of all possible corresponding isocontours in linear time with the size of the tree. Moreover we show how to extend the scheme introduced in [3] with the Betti number computation without increasing its complexity. Thus, we improve on the time complexity from our previous approach [10] from O(m log m) to O(n log n + m), where m is the number of cells and n is the number of vertices in the domain of F. Our second contribution is a new divide-and-conquer algorithm that computes the Augmented Contour Tree with improved efficiency. The approach computes the output Contour Tree by merging two intermediate Contour Trees and is independent of the interpolant. In this way we confine any knowledge regarding a specific interpolant to an independent function that computes the tree for a single cell. We have implemented this function for the trilinear interpolant and plan to replace it with higher order interpolants when needed. The time complexity is O(n + t log n), where t is the number of critical points of F. For the first time
Access and visualization using clusters and other parallel computers
NASA Technical Reports Server (NTRS)
Katz, Daniel S.; Bergou, Attila; Berriman, Bruce; Block, Gary; Collier, Jim; Curkendall, Dave; Good, John; Husman, Laura; Jacob, Joe; Laity, Anastasia;
2003-01-01
JPL's Parallel Applications Technologies Group has been exploring the issues of data access and visualization of very large data sets over the past 10 or so years. this work has used a number of types of parallel computers, and today includes the use of commodity clusters. This talk will highlight some of the applications and tools we have developed, including how they use parallel computing resources, and specifically how we are using modern clusters. Our applications focus on NASA's needs; thus our data sets are usually related to Earth and Space Science, including data delivered from instruments in space, and data produced by telescopes on the ground.
InSAR Scientific Computing Environment
NASA Astrophysics Data System (ADS)
Gurrola, E. M.; Rosen, P. A.; Sacco, G.; Zebker, H. A.; Simons, M.; Sandwell, D. T.
2010-12-01
The InSAR Scientific Computing Environment (ISCE) is a software development effort in its second year within the NASA Advanced Information Systems and Technology program. The ISCE will provide a new computing environment for geodetic image processing for InSAR sensors that will enable scientists to reduce measurements directly from radar satellites and aircraft to new geophysical products without first requiring them to develop detailed expertise in radar processing methods. The environment can serve as the core of a centralized processing center to bring Level-0 raw radar data up to Level-3 data products, but is adaptable to alternative processing approaches for science users interested in new and different ways to exploit mission data. The NRC Decadal Survey-recommended DESDynI mission will deliver data of unprecedented quantity and quality, making possible global-scale studies in climate research, natural hazards, and Earth's ecosystem. The InSAR Scientific Computing Environment is planned to become a key element in processing DESDynI data into higher level data products and it is expected to enable a new class of analyses that take greater advantage of the long time and large spatial scales of these new data, than current approaches. At the core of ISCE is both legacy processing software from the JPL/Caltech ROI_PAC repeat-pass interferometry package as well as a new InSAR processing package containing more efficient and more accurate processing algorithms being developed at Stanford for this project that is based on experience gained in developing processors for missions such as SRTM and UAVSAR. Around the core InSAR processing programs we are building object-oriented wrappers to enable their incorporation into a more modern, flexible, extensible software package that is informed by modern programming methods, including rigorous componentization of processing codes, abstraction and generalization of data models, and a robust, intuitive user interface with
Partitioning problems in parallel, pipelined and distributed computing
NASA Technical Reports Server (NTRS)
Bokhari, S.
1985-01-01
The problem of optimally assigning the modules of a parallel program over the processors of a multiple computer system is addressed. A Sum-Bottleneck path algorithm is developed that permits the efficient solution of many variants of this problem under some constraints on the structure of the partitions. In particular, the following problems are solved optimally for a single-host, multiple satellite system: partitioning multiple chain structured parallel programs, multiple arbitrarily structured serial programs and single tree structured parallel programs. In addition, the problems of partitioning chain structured parallel programs across chain connected systems and across shared memory (or shared bus) systems are also solved under certain constraints. All solutions for parallel programs are equally applicable to pipelined programs. These results extend prior research in this area by explicitly taking concurrency into account and permit the efficient utilization of multiple computer architectures for a wide range of problems of practical interest.
Partitioning problems in parallel, pipelined, and distributed computing
NASA Technical Reports Server (NTRS)
Bokhari, Shahid H.
1988-01-01
The problem of optimally assigning the modules of a parallel program over the processors of a multiple-computer system is addressed. A sum-bottleneck path algorithm is developed that permits the efficient solution of many variants of this problem under some constraints on the structure of the partitions. In particular, the following problems are solved optimally for a single-host, multiple-satellite system: partitioning multiple chain-structured parallel programs, multiple arbitrarily structured serial programs, and single-tree structured parallel programs. In addition, the problem of partitioning chain-structured parallel programs across chain-connected systems is solved under certain constraints. All solutions for parallel programs are equally applicable to pipelined programs. These results extend prior research in this area by explicitly taking concurrency into account and permit the efficient utilization of multiple-computer architectures for a wide range of problems of practical interest.
Performing a global barrier operation in a parallel computer
Archer, Charles J; Blocksome, Michael A; Ratterman, Joseph D; Smith, Brian E
2014-12-09
Executing computing tasks on a parallel computer that includes compute nodes coupled for data communications, where each compute node executes tasks, with one task on each compute node designated as a master task, including: for each task on each compute node until all master tasks have joined a global barrier: determining whether the task is a master task; if the task is not a master task, joining a single local barrier; if the task is a master task, joining the global barrier and the single local barrier only after all other tasks on the compute node have joined the single local barrier.
Scientific Writing & the Scientific Method: Parallel "Hourglass" Structure in Form & Content
ERIC Educational Resources Information Center
Schulte, Bruce A.
2003-01-01
The writing of a scientific article offers a sharp divergence from the standard essay paper familiar to most students from English class. Supporting documentation is embedded in the scientific prose, not footnoted along the bottom of each page. Instructing students in the art of scientific writing can be truly challenging. After years of…
Scientific Writing & the Scientific Method: Parallel "Hourglass" Structure in Form & Content
ERIC Educational Resources Information Center
Schulte, Bruce A.
2003-01-01
The writing of a scientific article offers a sharp divergence from the standard essay paper familiar to most students from English class. Supporting documentation is embedded in the scientific prose, not footnoted along the bottom of each page. Instructing students in the art of scientific writing can be truly challenging. After years of…
Parallel structures in human and computer memory
NASA Technical Reports Server (NTRS)
Kanerva, P.
1986-01-01
If one thinks of our experiences as being recorded continuously on film, then human memory can be compared to a film library that is indexed by the contents of the film strips stored in it. Moreover, approximate retrieval cues suffice to retrieve information stored in this library. One recognizes a familiar person in a fuzzy photograph or a familiar tune played on a strange instrument. A computer memory that would allow a computer to recognize patterns and to recall sequences the way humans do is constructed. Such a memory is remarkably similiar in structure to a conventional computer memory and also to the neural circuits in the cortex of the cerebellum of the human brain. It is concluded that the frame problem of artificial intelligence could be solved by the use of such a memory if one were able to encode information about the world properly.
Parallel structures in human and computer memory
NASA Astrophysics Data System (ADS)
Kanerva, Pentti
1986-08-01
If we think of our experiences as being recorded continuously on film, then human memory can be compared to a film library that is indexed by the contents of the film strips stored in it. Moreover, approximate retrieval cues suffice to retrieve information stored in this library: We recognize a familiar person in a fuzzy photograph or a familiar tune played on a strange instrument. This paper is about how to construct a computer memory that would allow a computer to recognize patterns and to recall sequences the way humans do. Such a memory is remarkably similar in structure to a conventional computer memory and also to the neural circuits in the cortex of the cerebellum of the human brain. The paper concludes that the frame problem of artificial intelligence could be solved by the use of such a memory if we were able to encode information about the world properly.
Models and Measurements of Parallelism for a Distributed Computer System.
1982-01-01
that parallel execution of the processes comprising an application program will defray U the overhead costs of distributed computing . This...of Different Approaches to Distributed Computing ", Proceedings of the Ist International Conference on Distributed Comput er Systems, Huntsville, AL...Oct. 1-5, 1979), pp. 222-232. [20] Liskov, B., "Primitives for Distributed Computing ", Froceedings of the 7--th Symposium on Operating System
Parallel algorithms and archtectures for computational structural mechanics
NASA Technical Reports Server (NTRS)
Patrick, Merrell; Ma, Shing; Mahajan, Umesh
1989-01-01
The determination of the fundamental (lowest) natural vibration frequencies and associated mode shapes is a key step used to uncover and correct potential failures or problem areas in most complex structures. However, the computation time taken by finite element codes to evaluate these natural frequencies is significant, often the most computationally intensive part of structural analysis calculations. There is continuing need to reduce this computation time. This study addresses this need by developing methods for parallel computation.
Semtner, A.J. Jr.; Chervin, R.M.
1993-05-01
During the second year of CHAMMP funding to the principal investigators, progress has been made in the proposed areas of research, as follows: investigation of the physics of the thermohaline circulation; examination of resolution effects on ocean general circulation; and development of a massively parallel ocean climate model.
Parallelization of ARC3D with Computer-Aided Tools
NASA Technical Reports Server (NTRS)
Jin, Haoqiang; Hribar, Michelle; Yan, Jerry; Saini, Subhash (Technical Monitor)
1998-01-01
A series of efforts have been devoted to investigating methods of porting and parallelizing applications quickly and efficiently for new architectures, such as the SCSI Origin 2000 and Cray T3E. This report presents the parallelization of a CFD application, ARC3D, using the computer-aided tools, Cesspools. Steps of parallelizing this code and requirements of achieving better performance are discussed. The generated parallel version has achieved reasonably well performance, for example, having a speedup of 30 for 36 Cray T3E processors. However, this performance could not be obtained without modification of the original serial code. It is suggested that in many cases improving serial code and performing necessary code transformations are important parts for the automated parallelization process although user intervention in many of these parts are still necessary. Nevertheless, development and improvement of useful software tools, such as Cesspools, can help trim down many tedious parallelization details and improve the processing efficiency.
Misleading Performance Claims in Parallel Computations
Bailey, David H.
2009-05-29
In a previous humorous note entitled 'Twelve Ways to Fool the Masses,' I outlined twelve common ways in which performance figures for technical computer systems can be distorted. In this paper and accompanying conference talk, I give a reprise of these twelve 'methods' and give some actual examples that have appeared in peer-reviewed literature in years past. I then propose guidelines for reporting performance, the adoption of which would raise the level of professionalism and reduce the level of confusion, not only in the world of device simulation but also in the larger arena of technical computing.
Methods of parallel computation applied on granular simulations
NASA Astrophysics Data System (ADS)
Martins, Gustavo H. B.; Atman, Allbens P. F.
2017-06-01
Every year, parallel computing has becoming cheaper and more accessible. As consequence, applications were spreading over all research areas. Granular materials is a promising area for parallel computing. To prove this statement we study the impact of parallel computing in simulations of the BNE (Brazil Nut Effect). This property is due the remarkable arising of an intruder confined to a granular media when vertically shaken against gravity. By means of DEM (Discrete Element Methods) simulations, we study the code performance testing different methods to improve clock time. A comparison between serial and parallel algorithms, using OpenMP® is also shown. The best improvement was obtained by optimizing the function that find contacts using Verlet's cells.
OPENING REMARKS: Scientific Discovery through Advanced Computing
NASA Astrophysics Data System (ADS)
Strayer, Michael
2006-01-01
Good morning. Welcome to SciDAC 2006 and Denver. I share greetings from the new Undersecretary for Energy, Ray Orbach. Five years ago SciDAC was launched as an experiment in computational science. The goal was to form partnerships among science applications, computer scientists, and applied mathematicians to take advantage of the potential of emerging terascale computers. This experiment has been a resounding success. SciDAC has emerged as a powerful concept for addressing some of the biggest challenges facing our world. As significant as these successes were, I believe there is also significance in the teams that achieved them. In addition to their scientific aims these teams have advanced the overall field of computational science and set the stage for even larger accomplishments as we look ahead to SciDAC-2. I am sure that many of you are expecting to hear about the results of our current solicitation for SciDAC-2. I’m afraid we are not quite ready to make that announcement. Decisions are still being made and we will announce the results later this summer. Nearly 250 unique proposals were received and evaluated, involving literally thousands of researchers, postdocs, and students. These collectively requested more than five times our expected budget. This response is a testament to the success of SciDAC in the community. In SciDAC-2 our budget has been increased to about 70 million for FY 2007 and our partnerships have expanded to include the Environment and National Security missions of the Department. The National Science Foundation has also joined as a partner. These new partnerships are expected to expand the application space of SciDAC, and broaden the impact and visibility of the program. We have, with our recent solicitation, expanded to turbulence, computational biology, and groundwater reactive modeling and simulation. We are currently talking with the Department’s applied energy programs about risk assessment, optimization of complex systems - such
NASA Technical Reports Server (NTRS)
Treinish, Lloyd A.; Gough, Michael L.; Wildenhain, W. David
1987-01-01
The capability was developed of rapidly producing visual representations of large, complex, multi-dimensional space and earth sciences data sets via the implementation of computer graphics modeling techniques on the Massively Parallel Processor (MPP) by employing techniques recently developed for typically non-scientific applications. Such capabilities can provide a new and valuable tool for the understanding of complex scientific data, and a new application of parallel computing via the MPP. A prototype system with such capabilities was developed and integrated into the National Space Science Data Center's (NSSDC) Pilot Climate Data System (PCDS) data-independent environment for computer graphics data display to provide easy access to users. While developing these capabilities, several problems had to be solved independently of the actual use of the MPP, all of which are outlined.
Efficient Parallel Kernel Solvers for Computational Fluid Dynamics Applications
NASA Technical Reports Server (NTRS)
Sun, Xian-He
1997-01-01
Distributed-memory parallel computers dominate today's parallel computing arena. These machines, such as Intel Paragon, IBM SP2, and Cray Origin2OO, have successfully delivered high performance computing power for solving some of the so-called "grand-challenge" problems. Despite initial success, parallel machines have not been widely accepted in production engineering environments due to the complexity of parallel programming. On a parallel computing system, a task has to be partitioned and distributed appropriately among processors to reduce communication cost and to attain load balance. More importantly, even with careful partitioning and mapping, the performance of an algorithm may still be unsatisfactory, since conventional sequential algorithms may be serial in nature and may not be implemented efficiently on parallel machines. In many cases, new algorithms have to be introduced to increase parallel performance. In order to achieve optimal performance, in addition to partitioning and mapping, a careful performance study should be conducted for a given application to find a good algorithm-machine combination. This process, however, is usually painful and elusive. The goal of this project is to design and develop efficient parallel algorithms for highly accurate Computational Fluid Dynamics (CFD) simulations and other engineering applications. The work plan is 1) developing highly accurate parallel numerical algorithms, 2) conduct preliminary testing to verify the effectiveness and potential of these algorithms, 3) incorporate newly developed algorithms into actual simulation packages. The work plan has well achieved. Two highly accurate, efficient Poisson solvers have been developed and tested based on two different approaches: (1) Adopting a mathematical geometry which has a better capacity to describe the fluid, (2) Using compact scheme to gain high order accuracy in numerical discretization. The previously developed Parallel Diagonal Dominant (PDD) algorithm
Parallel Computing by Xeroxing on Transparencies
NASA Astrophysics Data System (ADS)
Head, Tom
We illustrate a procedure for solving instances of the Boolean satisfiability (SAT) problem by xeroxing onto transparent plastic sheets. Suppose that m clauses are given in which n variables occur and that the longest clause contains k literals. The associated instance of the SAT problem can be solved by using a xerox machine to form only n+2k+m successive transparencies. The applicability of this linear time algorithm is limited, of course, by the increase in the information density on the transparencies when n is large. This same scheme of computation can be carried out by using photographic or other optical processes. This work has been developed as an alternate implementation of procedures previously developed in the context of aqueous (DNA) computing.
Nonlinear hierarchical substructural parallelism and computer architecture
NASA Technical Reports Server (NTRS)
Padovan, Joe
1989-01-01
Computer architecture is investigated in conjunction with the algorithmic structures of nonlinear finite-element analysis. To help set the stage for this goal, the development is undertaken by considering the wide-ranging needs associated with the analysis of rolling tires which possess the full range of kinematic, material and boundary condition induced nonlinearity in addition to gross and local cord-matrix material properties.
Identifying failure in a tree network of a parallel computer
Archer, Charles J.; Pinnow, Kurt W.; Wallenfelt, Brian P.
2010-08-24
Methods, parallel computers, and products are provided for identifying failure in a tree network of a parallel computer. The parallel computer includes one or more processing sets including an I/O node and a plurality of compute nodes. For each processing set embodiments include selecting a set of test compute nodes, the test compute nodes being a subset of the compute nodes of the processing set; measuring the performance of the I/O node of the processing set; measuring the performance of the selected set of test compute nodes; calculating a current test value in dependence upon the measured performance of the I/O node of the processing set, the measured performance of the set of test compute nodes, and a predetermined value for I/O node performance; and comparing the current test value with a predetermined tree performance threshold. If the current test value is below the predetermined tree performance threshold, embodiments include selecting another set of test compute nodes. If the current test value is not below the predetermined tree performance threshold, embodiments include selecting from the test compute nodes one or more potential problem nodes and testing individually potential problem nodes and links to potential problem nodes.
A Lanczos eigenvalue method on a parallel computer
NASA Technical Reports Server (NTRS)
Bostic, Susan W.; Fulton, Robert E.
1987-01-01
Eigenvalue analyses of complex structures is a computationally intensive task which can benefit significantly from new and impending parallel computers. This study reports on a parallel computer implementation of the Lanczos method for free vibration analysis. The approach used here subdivides the major Lanczos calculation tasks into subtasks and introduces parallelism down to the subtask levels such as matrix decomposition and forward/backward substitution. The method was implemented on a commercial parallel computer and results were obtained for a long flexible space structure. While parallel computing efficiency for the Lanczos method was good for a moderate number of processors for the test problem, the greatest reduction in time was realized for the decomposition of the stiffness matrix, a calculation which took 70 percent of the time in the sequential program and which took 25 percent of the time on eight processors. For a sample calculation of the twenty lowest frequencies of a 486 degree of freedom problem, the total sequential computing time was reduced by almost a factor of ten using 16 processors.
A microeconomic scheduler for parallel computers
NASA Technical Reports Server (NTRS)
Stoica, Ion; Abdel-Wahab, Hussein; Pothen, Alex
1995-01-01
We describe a scheduler based on the microeconomic paradigm for scheduling on-line a set of parallel jobs in a multiprocessor system. In addition to the classical objectives of increasing the system throughput and reducing the response time, we consider fairness in allocating system resources among the users, and providing the user with control over the relative performances of his jobs. We associate with every user a savings account in which he receives money at a constant rate. When a user wants to run a job, he creates an expense account for that job to which he transfers money from his savings account. The job uses the funds in its expense account to obtain the system resources it needs for execution. The share of the system resources allocated to the user is directly related to the rate at which the user receives money; the rate at which the user transfers money into a job expense account controls the job's performance. We prove that starvation is not possible in our model. Simulation results show that our scheduler improves both system and user performances in comparison with two different variable partitioning policies. It is also shown to be effective in guaranteeing fairness and providing control over the performance of jobs.
History Matching in Parallel Computational Environments
Steven Bryant; Sanjay Srinivasan; Alvaro Barrera; Sharad Yadav
2005-10-01
A novel methodology for delineating multiple reservoir domains for the purpose of history matching in a distributed computing environment has been proposed. A fully probabilistic approach to perturb permeability within the delineated zones is implemented. The combination of robust schemes for identifying reservoir zones and distributed computing significantly increase the accuracy and efficiency of the probabilistic approach. The information pertaining to the permeability variations in the reservoir that is contained in dynamic data is calibrated in terms of a deformation parameter rD. This information is merged with the prior geologic information in order to generate permeability models consistent with the observed dynamic data as well as the prior geology. The relationship between dynamic response data and reservoir attributes may vary in different regions of the reservoir due to spatial variations in reservoir attributes, well configuration, flow constrains etc. The probabilistic approach then has to account for multiple r{sub D} values in different regions of the reservoir. In order to delineate reservoir domains that can be characterized with different rD parameters, principal component analysis (PCA) of the Hessian matrix has been done. The Hessian matrix summarizes the sensitivity of the objective function at a given step of the history matching to model parameters. It also measures the interaction of the parameters in affecting the objective function. The basic premise of PC analysis is to isolate the most sensitive and least correlated regions. The eigenvectors obtained during the PCA are suitably scaled and appropriate grid block volume cut-offs are defined such that the resultant domains are neither too large (which increases interactions between domains) nor too small (implying ineffective history matching). The delineation of domains requires calculation of Hessian, which could be computationally costly and as well as restricts the current approach to
History Matching in Parallel Computational Environments
Steven Bryant; Sanjay Srinivasan; Alvaro Barrera; Yonghwee Kim; Sharad Yadav
2006-08-31
A novel methodology for delineating multiple reservoir domains for the purpose of history matching in a distributed computing environment has been proposed. A fully probabilistic approach to perturb permeability within the delineated zones is implemented. The combination of robust schemes for identifying reservoir zones and distributed computing significantly increase the accuracy and efficiency of the probabilistic approach. The information pertaining to the permeability variations in the reservoir that is contained in dynamic data is calibrated in terms of a deformation parameter rD. This information is merged with the prior geologic information in order to generate permeability models consistent with the observed dynamic data as well as the prior geology. The relationship between dynamic response data and reservoir attributes may vary in different regions of the reservoir due to spatial variations in reservoir attributes, well configuration, flow constrains etc. The probabilistic approach then has to account for multiple r{sub D} values in different regions of the reservoir. In order to delineate reservoir domains that can be characterized with different r{sub D} parameters, principal component analysis (PCA) of the Hessian matrix has been done. The Hessian matrix summarizes the sensitivity of the objective function at a given step of the history matching to model parameters. It also measures the interaction of the parameters in affecting the objective function. The basic premise of PC analysis is to isolate the most sensitive and least correlated regions. The eigenvectors obtained during the PCA are suitably scaled and appropriate grid block volume cut-offs are defined such that the resultant domains are neither too large (which increases interactions between domains) nor too small (implying ineffective history matching). The delineation of domains requires calculation of Hessian, which could be computationally costly and as well as restricts the current
Institute for Scientific Computing Research Fiscal Year 2002 Annual Report
Keyes, D E; McGraw, J R; Bodtker, L K
2003-03-11
The Institute for Scientific Computing Research (ISCR) at Lawrence Livermore National Laboratory is jointly administered by the Computing Applications and Research Department (CAR) and the University Relations Program (URP), and this joint relationship expresses its mission. An extensively externally networked ISCR cost-effectively expands the level and scope of national computational science expertise available to the Laboratory through CAR. The URP, with its infrastructure for managing six institutes and numerous educational programs at LLNL, assumes much of the logistical burden that is unavoidable in bridging the Laboratory's internal computational research environment with that of the academic community. As large-scale simulations on the parallel platforms of DOE's Advanced Simulation and Computing (ASCI) become increasingly important to the overall mission of LLNL, the role of the ISCR expands in importance, accordingly. Relying primarily on non-permanent staffing, the ISCR complements Laboratory research in areas of the computer and information sciences that are needed at the frontier of Laboratory missions. The ISCR strives to be the ''eyes and ears'' of the Laboratory in the computer and information sciences, in keeping the Laboratory aware of and connected to important external advances. It also attempts to be ''feet and hands, in carrying those advances into the Laboratory and incorporating them into practice. In addition to conducting research, the ISCR provides continuing education opportunities to Laboratory personnel, in the form of on-site workshops taught by experts on novel software or hardware technologies. The ISCR also seeks to influence the research community external to the Laboratory to pursue Laboratory-related interests and to train the workforce that will be required by the Laboratory. Part of the performance of this function is interpreting to the external community appropriate (unclassified) aspects of the Laboratory's own contributions
Methods for operating parallel computing systems employing sequenced communications
Benner, R.E.; Gustafson, J.L.; Montry, G.R.
1999-08-10
A parallel computing system and method are disclosed having improved performance where a program is concurrently run on a plurality of nodes for reducing total processing time, each node having a processor, a memory, and a predetermined number of communication channels connected to the node and independently connected directly to other nodes. The present invention improves performance of the parallel computing system by providing a system which can provide efficient communication between the processors and between the system and input and output devices. A method is also disclosed which can locate defective nodes with the computing system. 15 figs.
Methods for operating parallel computing systems employing sequenced communications
Benner, Robert E.; Gustafson, John L.; Montry, Gary R.
1999-01-01
A parallel computing system and method having improved performance where a program is concurrently run on a plurality of nodes for reducing total processing time, each node having a processor, a memory, and a predetermined number of communication channels connected to the node and independently connected directly to other nodes. The present invention improves performance of performance of the parallel computing system by providing a system which can provide efficient communication between the processors and between the system and input and output devices. A method is also disclosed which can locate defective nodes with the computing system.
Six Years of Parallel Computing at NAS (1987 - 1993): What Have we Learned?
NASA Technical Reports Server (NTRS)
Simon, Horst D.; Cooper, D. M. (Technical Monitor)
1994-01-01
In the fall of 1987 the age of parallelism at NAS began with the installation of a 32K processor CM-2 from Thinking Machines. In 1987 this was described as an "experiment" in parallel processing. In the six years since, NAS acquired a series of parallel machines, and conducted an active research and development effort focused on the use of highly parallel machines for applications in the computational aerosciences. In this time period parallel processing for scientific applications evolved from a fringe research topic into the one of main activities at NAS. In this presentation I will review the history of parallel computing at NAS in the context of the major progress, which has been made in the field in general. I will attempt to summarize the lessons we have learned so far, and the contributions NAS has made to the state of the art. Based on these insights I will comment on the current state of parallel computing (including the HPCC effort) and try to predict some trends for the next six years.
Six Years of Parallel Computing at NAS (1987 - 1993): What Have we Learned?
NASA Technical Reports Server (NTRS)
Simon, Horst D.; Cooper, D. M. (Technical Monitor)
1994-01-01
In the fall of 1987 the age of parallelism at NAS began with the installation of a 32K processor CM-2 from Thinking Machines. In 1987 this was described as an "experiment" in parallel processing. In the six years since, NAS acquired a series of parallel machines, and conducted an active research and development effort focused on the use of highly parallel machines for applications in the computational aerosciences. In this time period parallel processing for scientific applications evolved from a fringe research topic into the one of main activities at NAS. In this presentation I will review the history of parallel computing at NAS in the context of the major progress, which has been made in the field in general. I will attempt to summarize the lessons we have learned so far, and the contributions NAS has made to the state of the art. Based on these insights I will comment on the current state of parallel computing (including the HPCC effort) and try to predict some trends for the next six years.
NASA Astrophysics Data System (ADS)
Mighell, Kenneth John
2011-11-01
The development of parallel-processing image-analysis codes is generally a challenging task that requires complicated choreography of interprocessor communications. If, however, the image-analysis algorithm is embarrassingly parallel, then the development of a parallel-processing implementation of that algorithm can be a much easier task to accomplish because, by definition, there is little need for communication between the compute processes. I describe the design, implementation, and performance of a parallel-processing image-analysis application, called CRBLASTER, which does cosmic-ray rejection of CCD (charge-coupled device) images using the embarrassingly-parallel L.A.COSMIC algorithm. CRBLASTER is written in C using the high-performance computing industry standard Message Passing Interface (MPI) library. The code has been designed to be used by research scientists who are familiar with C as a parallel-processing computational framework that enables the easy development of parallel-processing image-analysis programs based on embarrassingly-parallel algorithms. The CRBLASTER source code is freely available at the official application website at the National Optical Astronomy Observatory. Removing cosmic rays from a single 800x800 pixel Hubble Space Telescope WFPC2 image takes 44 seconds with the IRAF script lacos_im.cl running on a single core of an Apple Mac Pro computer with two 2.8-GHz quad-core Intel Xeon processors. CRBLASTER is 7.4 times faster processing the same image on a single core on the same machine. Processing the same image with CRBLASTER simultaneously on all 8 cores of the same machine takes 0.875 seconds -- which is a speedup factor of 50.3 times faster than the IRAF script. A detailed analysis is presented of the performance of CRBLASTER using between 1 and 57 processors on a low-power Tilera 700-MHz 64-core TILE64 processor.
Parallel Computational Fluid Dynamics: Current Status and Future Requirements
NASA Technical Reports Server (NTRS)
Simon, Horst D.; VanDalsem, William R.; Dagum, Leonardo; Kutler, Paul (Technical Monitor)
1994-01-01
One or the key objectives of the Applied Research Branch in the Numerical Aerodynamic Simulation (NAS) Systems Division at NASA Allies Research Center is the accelerated introduction of highly parallel machines into a full operational environment. In this report we discuss the performance results obtained from the implementation of some computational fluid dynamics (CFD) applications on the Connection Machine CM-2 and the Intel iPSC/860. We summarize some of the experiences made so far with the parallel testbed machines at the NAS Applied Research Branch. Then we discuss the long term computational requirements for accomplishing some of the grand challenge problems in computational aerosciences. We argue that only massively parallel machines will be able to meet these grand challenge requirements, and we outline the computer science and algorithm research challenges ahead.
Tutorial: Parallel Computing of Simulation Models for Risk Analysis.
Reilly, Allison C; Staid, Andrea; Gao, Michael; Guikema, Seth D
2016-10-01
Simulation models are widely used in risk analysis to study the effects of uncertainties on outcomes of interest in complex problems. Often, these models are computationally complex and time consuming to run. This latter point may be at odds with time-sensitive evaluations or may limit the number of parameters that are considered. In this article, we give an introductory tutorial focused on parallelizing simulation code to better leverage modern computing hardware, enabling risk analysts to better utilize simulation-based methods for quantifying uncertainty in practice. This article is aimed primarily at risk analysts who use simulation methods but do not yet utilize parallelization to decrease the computational burden of these models. The discussion is focused on conceptual aspects of embarrassingly parallel computer code and software considerations. Two complementary examples are shown using the languages MATLAB and R. A brief discussion of hardware considerations is located in the Appendix. © 2016 Society for Risk Analysis.
NASA Astrophysics Data System (ADS)
Fazanaro, Filipe I.; Soriano, Diogo C.; Suyama, Ricardo; Madrid, Marconi K.; Oliveira, José Raimundo de; Muñoz, Ignacio Bravo; Attux, Romis
2016-08-01
The characterization of nonlinear dynamical systems and their attractors in terms of invariant measures, basins of attractions and the structure of their vector fields usually outlines a task strongly related to the underlying computational cost. In this work, the practical aspects related to the use of parallel computing - specially the use of Graphics Processing Units (GPUS) and of the Compute Unified Device Architecture (CUDA) - are reviewed and discussed in the context of nonlinear dynamical systems characterization. In this work such characterization is performed by obtaining both local and global Lyapunov exponents for the classical forced Duffing oscillator. The local divergence measure was employed by the computation of the Lagrangian Coherent Structures (LCSS), revealing the general organization of the flow according to the obtained separatrices, while the global Lyapunov exponents were used to characterize the attractors obtained under one or more bifurcation parameters. These simulation sets also illustrate the required computation time and speedup gains provided by different parallel computing strategies, justifying the employment and the relevance of GPUS and CUDA in such extensive numerical approach. Finally, more than simply providing an overview supported by a representative set of simulations, this work also aims to be a unified introduction to the use of the mentioned parallel computing tools in the context of nonlinear dynamical systems, providing codes and examples to be executed in MATLAB and using the CUDA environment, something that is usually fragmented in different scientific communities and restricted to specialists on parallel computing strategies.
76 FR 45786 - Advanced Scientific Computing Advisory Committee; Meeting
Federal Register 2010, 2011, 2012, 2013, 2014
2011-08-01
... Advanced Scientific Computing Advisory Committee; Meeting AGENCY: Office of Science, Department of Energy... Computing Advisory Committee (ASCAC). Federal Advisory Committee Act (Pub. L. 92-463, 86 Stat. 770) requires... INFORMATION CONTACT: Melea Baker, Office of Advanced Scientific Computing Research; SC-21/Germantown...
Modeling groundwater flow on massively parallel computers
Ashby, S.F.; Falgout, R.D.; Fogwell, T.W.; Tompson, A.F.B.
1994-12-31
The authors will explore the numerical simulation of groundwater flow in three-dimensional heterogeneous porous media. An interdisciplinary team of mathematicians, computer scientists, hydrologists, and environmental engineers is developing a sophisticated simulation code for use on workstation clusters and MPPs. To date, they have concentrated on modeling flow in the saturated zone (single phase), which requires the solution of a large linear system. they will discuss their implementation of preconditioned conjugate gradient solvers. The preconditioners under consideration include simple diagonal scaling, s-step Jacobi, adaptive Chebyshev polynomial preconditioning, and multigrid. They will present some preliminary numerical results, including simulations of groundwater flow at the LLNL site. They also will demonstrate the code`s scalability.
InSAR Scientific Computing Environment (Invited)
NASA Astrophysics Data System (ADS)
Rosen, P. A.; Gurrola, E. M.; Sacco, G.; Zebker, H. A.; Simons, M.; Sandwell, D. T.
2009-12-01
The InSAR Scientific Computing Environment (ISCE) is a new development effort within the NASA Advanced Information Systems and Technology program, with the intent of recasting the JPL/Caltech ROI_PAC repeat-pass interferometry package into a modern, reconfigurable, open-source computing environment. The new capability initiates the next generation of geodetic imaging processing technology for InSAR sensors, providing flexibility and extensibility in reducing measurements from radar satellites and aircraft to new geophysical products. The NRC Decadal Survey recommended DESDynI mission will deliver to the science community data of unprecedented quantity and quality, making possible global-scale studies in climate research, natural hazards, and Earth’s ecosystem. DESDynI will provide time series and multi-image measurements that permit four-dimensional models of Earth surface processes so that, for example, climate-induced changes over time become apparent and quantifiable. In this paper, we describe the Environment, and illustrate how it can facility space-based geodesy from InSAR. The ISCE invokes object oriented scripts to control legacy and new codes, and abstracts and generalizes the data model for efficient manipulation of objects among modules. The module interfaces are suitable for command-line execution or GUI-programming. It exposes users gradually to its levels of capability, allowing novices to apply it readily for simple tasks and for experienced users to mine the data with great facility. The intent of the effort is to encourage user contributions to the code, creating an open source community that will extend its life and utility.
Parallel Domain Decomposition Preconditioning for Computational Fluid Dynamics
NASA Technical Reports Server (NTRS)
Barth, Timothy J.; Chan, Tony F.; Tang, Wei-Pai; Kutler, Paul (Technical Monitor)
1998-01-01
This viewgraph presentation gives an overview of the parallel domain decomposition preconditioning for computational fluid dynamics. Details are given on some difficult fluid flow problems, stabilized spatial discretizations, and Newton's method for solving the discretized flow equations. Schur complement domain decomposition is described through basic formulation, simplifying strategies (including iterative subdomain and Schur complement solves, matrix element dropping, localized Schur complement computation, and supersparse computations), and performance evaluation.
Three parallel computation methods for structural vibration analysis
NASA Technical Reports Server (NTRS)
Storaasli, Olaf; Bostic, Susan; Patrick, Merrell; Mahajan, Umesh; Ma, Shing
1988-01-01
The Lanczos (1950), multisectioning, and subspace iteration sequential methods for vibration analysis presently used as bases for three parallel algorithms are noted, in the aftermath of three example problems, to maintain reasonable accuracy in the computation of vibration frequencies. Significant computation time reductions are obtained as the number of processors increases. An analysis is made of the performance of each method, in order to characterize relative strengths and weaknesses as well as to identify those parameters that most strongly affect computation efficiency.
Computational mechanics analysis tools for parallel-vector supercomputers
NASA Technical Reports Server (NTRS)
Storaasli, O. O.; Nguyen, D. T.; Baddourah, M. A.; Qin, J.
1993-01-01
Computational algorithms for structural analysis on parallel-vector supercomputers are reviewed. These parallel algorithms, developed by the authors, are for the assembly of structural equations, 'out-of-core' strategies for linear equation solution, massively distributed-memory equation solution, unsymmetric equation solution, general eigen-solution, geometrically nonlinear finite element analysis, design sensitivity analysis for structural dynamics, optimization algorithm and domain decomposition. The source code for many of these algorithms is available from NASA Langley.
Computational mechanics analysis tools for parallel-vector supercomputers
NASA Technical Reports Server (NTRS)
Storaasli, Olaf O.; Nguyen, Duc T.; Baddourah, Majdi; Qin, Jiangning
1993-01-01
Computational algorithms for structural analysis on parallel-vector supercomputers are reviewed. These parallel algorithms, developed by the authors, are for the assembly of structural equations, 'out-of-core' strategies for linear equation solution, massively distributed-memory equation solution, unsymmetric equation solution, general eigensolution, geometrically nonlinear finite element analysis, design sensitivity analysis for structural dynamics, optimization search analysis and domain decomposition. The source code for many of these algorithms is available.
Parallel Algorithms for Least Squares and Related Computations.
1991-03-22
with K. Gallivan and A. Sameh at the University of Illinois, with the development of a comprehensive treatise (publications 7, 9) on parallel algorithms...algorithms. This is joint work with K. Gallivan and A. Sameh from the University of Illinois Center for Supercomputing Research and Development. (See...633 (with D. James). 7. Parallel algorithms for dense linear algebra computations, SIAM Review, 32 (1990), pp. 54- 135 (with K. Gallivan and A. Sameh
Parallel and pipeline computation of fast unitary transforms
NASA Technical Reports Server (NTRS)
Fino, B. J.; Algazi, V. R.
1975-01-01
The letter discusses the parallel and pipeline organization of fast-unitary-transform algorithms such as the fast Fourier transform, and points out the efficiency of a combined parallel-pipeline processor of a transform such as the Haar transform, in which (2 to the n-th power) -1 hardware 'butterflies' generate a transform of order 2 to the n-th power every computation cycle.
Parallel and pipeline computation of fast unitary transforms
NASA Technical Reports Server (NTRS)
Fino, B. J.; Algazi, V. R.
1975-01-01
The letter discusses the parallel and pipeline organization of fast-unitary-transform algorithms such as the fast Fourier transform, and points out the efficiency of a combined parallel-pipeline processor of a transform such as the Haar transform, in which (2 to the n-th power) -1 hardware 'butterflies' generate a transform of order 2 to the n-th power every computation cycle.
The InSAR Scientific Computing Environment
NASA Technical Reports Server (NTRS)
Rosen, Paul A.; Gurrola, Eric; Sacco, Gian Franco; Zebker, Howard
2012-01-01
We have developed a flexible and extensible Interferometric SAR (InSAR) Scientific Computing Environment (ISCE) for geodetic image processing. ISCE was designed from the ground up as a geophysics community tool for generating stacks of interferograms that lend themselves to various forms of time-series analysis, with attention paid to accuracy, extensibility, and modularity. The framework is python-based, with code elements rigorously componentized by separating input/output operations from the processing engines. This allows greater flexibility and extensibility in the data models, and creates algorithmic code that is less susceptible to unnecessary modification when new data types and sensors are available. In addition, the components support provenance and checkpointing to facilitate reprocessing and algorithm exploration. The algorithms, based on legacy processing codes, have been adapted to assume a common reference track approach for all images acquired from nearby orbits, simplifying and systematizing the geometry for time-series analysis. The framework is designed to easily allow user contributions, and is distributed for free use by researchers. ISCE can process data from the ALOS, ERS, EnviSAT, Cosmo-SkyMed, RadarSAT-1, RadarSAT-2, and TerraSAR-X platforms, starting from Level-0 or Level 1 as provided from the data source, and going as far as Level 3 geocoded deformation products. With its flexible design, it can be extended with raw/meta data parsers to enable it to work with radar data from other platforms
The InSAR Scientific Computing Environment
NASA Technical Reports Server (NTRS)
Rosen, Paul A.; Gurrola, Eric; Sacco, Gian Franco; Zebker, Howard
2012-01-01
We have developed a flexible and extensible Interferometric SAR (InSAR) Scientific Computing Environment (ISCE) for geodetic image processing. ISCE was designed from the ground up as a geophysics community tool for generating stacks of interferograms that lend themselves to various forms of time-series analysis, with attention paid to accuracy, extensibility, and modularity. The framework is python-based, with code elements rigorously componentized by separating input/output operations from the processing engines. This allows greater flexibility and extensibility in the data models, and creates algorithmic code that is less susceptible to unnecessary modification when new data types and sensors are available. In addition, the components support provenance and checkpointing to facilitate reprocessing and algorithm exploration. The algorithms, based on legacy processing codes, have been adapted to assume a common reference track approach for all images acquired from nearby orbits, simplifying and systematizing the geometry for time-series analysis. The framework is designed to easily allow user contributions, and is distributed for free use by researchers. ISCE can process data from the ALOS, ERS, EnviSAT, Cosmo-SkyMed, RadarSAT-1, RadarSAT-2, and TerraSAR-X platforms, starting from Level-0 or Level 1 as provided from the data source, and going as far as Level 3 geocoded deformation products. With its flexible design, it can be extended with raw/meta data parsers to enable it to work with radar data from other platforms
RAMA: A file system for massively parallel computers
NASA Technical Reports Server (NTRS)
Miller, Ethan L.; Katz, Randy H.
1993-01-01
This paper describes a file system design for massively parallel computers which makes very efficient use of a few disks per processor. This overcomes the traditional I/O bottleneck of massively parallel machines by storing the data on disks within the high-speed interconnection network. In addition, the file system, called RAMA, requires little inter-node synchronization, removing another common bottleneck in parallel processor file systems. Support for a large tertiary storage system can easily be integrated in lo the file system; in fact, RAMA runs most efficiently when tertiary storage is used.
Toward an automated parallel computing environment for geosciences
NASA Astrophysics Data System (ADS)
Zhang, Huai; Liu, Mian; Shi, Yaolin; Yuen, David A.; Yan, Zhenzhen; Liang, Guoping
2007-08-01
Software for geodynamic modeling has not kept up with the fast growing computing hardware and network resources. In the past decade supercomputing power has become available to most researchers in the form of affordable Beowulf clusters and other parallel computer platforms. However, to take full advantage of such computing power requires developing parallel algorithms and associated software, a task that is often too daunting for geoscience modelers whose main expertise is in geosciences. We introduce here an automated parallel computing environment built on open-source algorithms and libraries. Users interact with this computing environment by specifying the partial differential equations, solvers, and model-specific properties using an English-like modeling language in the input files. The system then automatically generates the finite element codes that can be run on distributed or shared memory parallel machines. This system is dynamic and flexible, allowing users to address different problems in geosciences. It is capable of providing web-based services, enabling users to generate source codes online. This unique feature will facilitate high-performance computing to be integrated with distributed data grids in the emerging cyber-infrastructures for geosciences. In this paper we discuss the principles of this automated modeling environment and provide examples to demonstrate its versatility.
CFD Analysis and Design Optimization Using Parallel Computers
NASA Technical Reports Server (NTRS)
Martinelli, Luigi; Alonso, Juan Jose; Jameson, Antony; Reuther, James
1997-01-01
A versatile and efficient multi-block method is presented for the simulation of both steady and unsteady flow, as well as aerodynamic design optimization of complete aircraft configurations. The compressible Euler and Reynolds Averaged Navier-Stokes (RANS) equations are discretized using a high resolution scheme on body-fitted structured meshes. An efficient multigrid implicit scheme is implemented for time-accurate flow calculations. Optimum aerodynamic shape design is achieved at very low cost using an adjoint formulation. The method is implemented on parallel computing systems using the MPI message passing interface standard to ensure portability. The results demonstrate that, by combining highly efficient algorithms with parallel computing, it is possible to perform detailed steady and unsteady analysis as well as automatic design for complex configurations using the present generation of parallel computers.
Parallel grid generation algorithm for distributed memory computers
NASA Technical Reports Server (NTRS)
Moitra, Stuti; Moitra, Anutosh
1994-01-01
A parallel grid-generation algorithm and its implementation on the Intel iPSC/860 computer are described. The grid-generation scheme is based on an algebraic formulation of homotopic relations. Methods for utilizing the inherent parallelism of the grid-generation scheme are described, and implementation of multiple levELs of parallelism on multiple instruction multiple data machines are indicated. The algorithm is capable of providing near orthogonality and spacing control at solid boundaries while requiring minimal interprocessor communications. Results obtained on the Intel hypercube for a blended wing-body configuration are used to demonstrate the effectiveness of the algorithm. Fortran implementations bAsed on the native programming model of the iPSC/860 computer and the Express system of software tools are reported. Computational gains in execution time speed-up ratios are given.
Multithreaded Model for Dynamic Load Balancing Parallel Adaptive PDE Computations
NASA Technical Reports Server (NTRS)
Chrisochoides, Nikos
1995-01-01
We present a multithreaded model for the dynamic load-balancing of numerical, adaptive computations required for the solution of Partial Differential Equations (PDE's) on multiprocessors. Multithreading is used as a means of exploring concurrency in the processor level in order to tolerate synchronization costs inherent to traditional (non-threaded) parallel adaptive PDE solvers. Our preliminary analysis for parallel, adaptive PDE solvers indicates that multithreading can be used an a mechanism to mask overheads required for the dynamic balancing of processor workloads with computations required for the actual numerical solution of the PDE's. Also, multithreading can simplify the implementation of dynamic load-balancing algorithms, a task that is very difficult for traditional data parallel adaptive PDE computations. Unfortunately, multithreading does not always simplify program complexity, often makes code re-usability not an easy task, and increases software complexity.
Solving unstructured grid problems on massively parallel computers
NASA Technical Reports Server (NTRS)
Hammond, Steven W.; Schreiber, Robert
1990-01-01
A highly parallel graph mapping technique that enables one to efficiently solve unstructured grid problems on massively parallel computers is presented. Many implicit and explicit methods for solving discretized partial differential equations require each point in the discretization to exchange data with its neighboring points every time step or iteration. The cost of this communication can negate the high performance promised by massively parallel computing. To eliminate this bottleneck, the graph of the irregular problem is mapped into the graph representing the interconnection topology of the computer such that the sum of the distances that the messages travel is minimized. It is shown that using the heuristic mapping algorithm significantly reduces the communication time compared to a naive assignment of processes to processors.
78 FR 41046 - Advanced Scientific Computing Advisory Committee
Federal Register 2010, 2011, 2012, 2013, 2014
2013-07-09
... following consultation with the Committee Management Secretariat, General Services Administration, notice is... (DOE), on the Advanced Scientific Computing Research Program managed by the Office of Advanced...
Single Circuit Parallel Computing with Phonons through Magneto-acoustics
NASA Astrophysics Data System (ADS)
Sklan, Sophia; Grossman, Jeffrey
2013-03-01
Phononic computing - the use of (typically thermal) vibrations for information processing - is a nascent technology; its capabilities are still being discovered. We analyze an alternative form of phononic computing inspired by optical, rather than electronic, computing. Using the acoustic Faraday effect, we design a phonon gyrator and thereby a means of performing computation through the manipulation of polarization in transverse phonon currents. Moreover, we establish that our gyrators act as generalized transistors and can construct digital logic gates. Exploiting the wave nature of phonons and the similarity of our logic gates, we demonstrate parallel computation within a single circuit, an effect presently unique to phonons. Finally, a generic method of designing these parallel circuits is introduced and used to analyze the feasibility of magneto-acoustic materials in realizing these circuits. This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. 1122374.
Panel on future directions in parallel computer architecture
VanTilborg, A.M. )
1989-06-01
One of the program highlights of the 15th Annual International Symposium on Computer Architecture, held May 30 - June 2, 1988 in Honolulu, was a panel session on future directions in parallel computer architecture. The panel was organized and chaired by the author, and was comprised of Prof. Jack Dennis (NASA Ames Research Institute for Advanced Computer Science), Prof. H.T. Kung (Carnegie Mellon), and Dr. Burton Smith (Tera Computer Company). The objective of the panel was to identify the likely trajectory of future parallel computer system progress, particularly from the sandpoint of marketplace acceptance. Approximately 250 attendees participated in the session, in which each panelist began with a ten minute viewgraph explanation of his views, followed by an open and sometimes lively exchange with the audience and fellow panelists. The session ran for ninety minutes.
Parallel Algorithms for Computational Models of Geophysical Systems
NASA Astrophysics Data System (ADS)
Carrillo Ledesma, A.; Herrera, I.; de la Cruz, L. M.; Hernández, G.; Grupo de Modelacion Matematica y Computacional
2013-05-01
Mathematical models of many systems of interest, including very important continuous systems of Earth Sciences and Engineering, lead to a great variety of partial differential equations (PDEs) whose solution methods are based on the computational processing of large-scale algebraic systems. Furthermore, the incredible expansion experienced by the existing computational hardware and software has made amenable to effective treatment problems of an ever increasing diversity and complexity, posed by scientific and engineering applications. Parallel computing is outstanding among the new computational tools and, in order to effectively use the most advanced computers available today, massively parallel software is required. Domain decomposition methods (DDMs) have been developed precisely for effectively treating PDEs in paralle. Ideally, the main objective of domain decomposition research is to produce algorithms capable of 'obtaining the global solution by exclusively solving local problems', but up-to-now this has only been an aspiration; that is, a strong desire for achieving such a property and so we call it 'the DDM-paradigm'. In recent times, numerically competitive DDM-algorithms are non-overlapping, preconditioned and necessarily incorporate constraints which pose an additional challenge for achieving the DDM-paradigm. Recently a group of four algorithms, referred to as the 'DVS-algorithms', which fulfill the DDM-paradigm, was developed. To derive them a new discretization method, which uses a non-overlapping system of nodes (the derived-nodes), was introduced. This discretization procedure can be applied to any boundary-value problem, or system of such equations. In turn, the resulting system of discrete equations can be treated using any available DDM-algorithm. In particular, two of the four DVS-algorithms mentioned above were obtained by application of the well-known and very effective algorithms BDDC and FETI-DP; these will be referred to as the DVS
Method for implementation of recursive hierarchical segmentation on parallel computers
NASA Technical Reports Server (NTRS)
Tilton, James C. (Inventor)
2005-01-01
A method, computer readable storage, and apparatus for implementing a recursive hierarchical segmentation algorithm on a parallel computing platform. The method includes setting a bottom level of recursion that defines where a recursive division of an image into sections stops dividing, and setting an intermediate level of recursion where the recursive division changes from a parallel implementation into a serial implementation. The segmentation algorithm is implemented according to the set levels. The method can also include setting a convergence check level of recursion with which the first level of recursion communicates with when performing a convergence check.
Implicit schemes and parallel computing in unstructured grid CFD
NASA Technical Reports Server (NTRS)
Venkatakrishnam, V.
1995-01-01
The development of implicit schemes for obtaining steady state solutions to the Euler and Navier-Stokes equations on unstructured grids is outlined. Applications are presented that compare the convergence characteristics of various implicit methods. Next, the development of explicit and implicit schemes to compute unsteady flows on unstructured grids is discussed. Next, the issues involved in parallelizing finite volume schemes on unstructured meshes in an MIMD (multiple instruction/multiple data stream) fashion are outlined. Techniques for partitioning unstructured grids among processors and for extracting parallelism in explicit and implicit solvers are discussed. Finally, some dynamic load balancing ideas, which are useful in adaptive transient computations, are presented.
Parallel computing alters approaches, raises integration challenges in reservoir modeling
Shiralkar, G.S.; Volz, R.F.; Stephenson, R.E.; Valle, M.J.; Hird, K.B.
1996-05-20
Parallel computing is emerging as an important force in reservoir characterization, with the potential of altering the way one approaches reservoir modeling. In just hours, it is possible to routinely simulate the fluid flow in reservoir models 10 times larger than the largest studies conducted previously within Amoco. Although parallel computing provides solutions to reservoir characterization problems not possible in the past, such a state-of-the-art technology also raises several new problems, including the need to handle large amounts of data and data integration. This paper presents a reservoir study recently conducted by Amoco providing a showcase for these emerging technologies.
Small file aggregation in a parallel computing system
Faibish, Sorin; Bent, John M.; Tzelnic, Percy; Grider, Gary; Zhang, Jingwang
2014-09-02
Techniques are provided for small file aggregation in a parallel computing system. An exemplary method for storing a plurality of files generated by a plurality of processes in a parallel computing system comprises aggregating the plurality of files into a single aggregated file; and generating metadata for the single aggregated file. The metadata comprises an offset and a length of each of the plurality of files in the single aggregated file. The metadata can be used to unpack one or more of the files from the single aggregated file.
Efficient Helicopter Aerodynamic and Aeroacoustic Predictions on Parallel Computers
NASA Technical Reports Server (NTRS)
Wissink, Andrew M.; Lyrintzis, Anastasios S.; Strawn, Roger C.; Oliker, Leonid; Biswas, Rupak
1996-01-01
This paper presents parallel implementations of two codes used in a combined CFD/Kirchhoff methodology to predict the aerodynamics and aeroacoustics properties of helicopters. The rotorcraft Navier-Stokes code, TURNS, computes the aerodynamic flowfield near the helicopter blades and the Kirchhoff acoustics code computes the noise in the far field, using the TURNS solution as input. The overall parallel strategy adds MPI message passing calls to the existing serial codes to allow for communication between processors. As a result, the total code modifications required for parallel execution are relatively small. The biggest bottleneck in running the TURNS code in parallel comes from the LU-SGS algorithm that solves the implicit system of equations. We use a new hybrid domain decomposition implementation of LU-SGS to obtain good parallel performance on the SP-2. TURNS demonstrates excellent parallel speedups for quasi-steady and unsteady three-dimensional calculations of a helicopter blade in forward flight. The execution rate attained by the code on 114 processors is six times faster than the same cases run on one processor of the Cray C-90. The parallel Kirchhoff code also shows excellent parallel speedups and fast execution rates. As a performance demonstration, unsteady acoustic pressures are computed at 1886 far-field observer locations for a sample acoustics problem. The calculation requires over two hundred hours of CPU time on one C-90 processor but takes only a few hours on 80 processors of the SP2. The resultant far-field acoustic field is analyzed with state of-the-art audio and video rendering of the propagating acoustic signals.
Archer, Charles J.; Blocksome, Michael A.; Ratterman, Joseph D.; Smith, Brian E.
2016-03-15
Processing data communications events in a parallel active messaging interface (`PAMI`) of a parallel computer that includes compute nodes that execute a parallel application, with the PAMI including data communications endpoints, and the endpoints are coupled for data communications through the PAMI and through other data communications resources, including determining by an advance function that there are no actionable data communications events pending for its context, placing by the advance function its thread of execution into a wait state, waiting for a subsequent data communications event for the context; responsive to occurrence of a subsequent data communications event for the context, awakening by the thread from the wait state; and processing by the advance function the subsequent data communications event now pending for the context.
TSE computers - A means for massively parallel computations
NASA Technical Reports Server (NTRS)
Strong, J. P., III
1976-01-01
A description is presented of hardware concepts for building a massively parallel processing system for two-dimensional data. The processing system is to use logic arrays of 128 x 128 elements which perform over 16 thousand operations simultaneously. Attention is given to image data, logic arrays, basic image logic functions, a prototype negator, an interleaver device, image logic circuits, and an image memory circuit.
Instant well-log inversion with a parallel computer
Kimminau, S.J.; Trivedi, H.
1993-08-01
Well-log analysis requires several vectors of input data to be inverted with a physical model that produces more vectors of output data. The problem is inherently suited to either vectorization or parallelization. PLATO (parallel log analysis, timely output) is a research prototype system that uses a parallel architecture computer with memory-mapped graphics to invert vector data and display the result rapidly. By combining this high-performance computing and display system with a graphical user interface, the analyst can interact with the system in real time'' and can visualize the result of changing parameters on up to 1,000 levels of computed volumes and reconstructed logs. It is expected that such instant'' inversion will remove the main disadvantages frequently cited for simultaneous analysis methods, namely difficulty in assessing sensitivity to different parameters and slow output response. Although the prototype system uses highly specific features of a parallel processor, a subsequent version has been implemented on a conventional (Serial) workstation with less performance but adequate functionality to preserve the apparently instant response. PLATO demonstrates the feasibility of petroleum computing applications combining an intuitive graphical interface, high-performance computing of physical models, and real-time output graphics.
Requirements for supercomputing in energy research: The transition to massively parallel computing
Not Available
1993-02-01
This report discusses: The emergence of a practical path to TeraFlop computing and beyond; requirements of energy research programs at DOE; implementation: supercomputer production computing environment on massively parallel computers; and implementation: user transition to massively parallel computing.
Accelerating EPI distortion correction by utilizing a modern GPU-based parallel computation.
Yang, Yao-Hao; Huang, Teng-Yi; Wang, Fu-Nien; Chuang, Tzu-Chao; Chen, Nan-Kuei
2013-04-01
The combination of phase demodulation and field mapping is a practical method to correct echo planar imaging (EPI) geometric distortion. However, since phase dispersion accumulates in each phase-encoding step, the calculation complexity of phase modulation is Ny-fold higher than conventional image reconstructions. Thus, correcting EPI images via phase demodulation is generally a time-consuming task. Parallel computing by employing general-purpose calculations on graphics processing units (GPU) can accelerate scientific computing if the algorithm is parallelized. This study proposes a method that incorporates the GPU-based technique into phase demodulation calculations to reduce computation time. The proposed parallel algorithm was applied to a PROPELLER-EPI diffusion tensor data set. The GPU-based phase demodulation method reduced the EPI distortion correctly, and accelerated the computation. The total reconstruction time of the 16-slice PROPELLER-EPI diffusion tensor images with matrix size of 128 × 128 was reduced from 1,754 seconds to 101 seconds by utilizing the parallelized 4-GPU program. GPU computing is a promising method to accelerate EPI geometric correction. The resulting reduction in computation time of phase demodulation should accelerate postprocessing for studies performed with EPI, and should effectuate the PROPELLER-EPI technique for clinical practice. Copyright © 2011 by the American Society of Neuroimaging.
Parallel-vector computation for CSI-design code
NASA Technical Reports Server (NTRS)
Nguyen, Duc T.
1990-01-01
Computational aspects of Control-Structure Interaction (CSI) DESIGN code is reviewed. Numerical intensive computation portions of CSI-DESIGN code were identified. Improvements in computational speed for the CSI-DESIGN code can be achieved by exploiting parallel and vector capabilities offered by modern computers, such as the Alliant, Convex, Cray-2, and Cray-YMP. Four options to generate the coefficient stiffness matrix and to solve the system of linear, simultaneous equations are currently available in the CSI-DESIGN code. A preprocessor to use RCM (Reverse Cuthill-Mackee) algorithm for bandwidth minimization was also developed for the CSI-DESIGN code. Preliminary results obtained by solving a small-scale, 97 node CSI finite element model (for eigensolution) have indicated that this new CSI-DESIGN code is 5 to 6 times faster (using 1 Alliant processor) than the old version of CSI-DESIGN code. This speed-up was achieved due to the RCM algorithm and the use of a new skyline solver. Efforts are underway to further improve the vector speed for CSI-DESIGN code, to evaluate its performance on a larger scale CSI model (such as phase zero CSI model) to make the code run efficiently on multiprocessor, parallel computer environment, and to make the code portable among different parallel computers available at NASA LaRC, such as Alliant, Convex, and Cray computers.
Solution of partial differential equations on vector and parallel computers
NASA Technical Reports Server (NTRS)
Ortega, J. M.; Voigt, R. G.
1985-01-01
The present status of numerical methods for partial differential equations on vector and parallel computers was reviewed. The relevant aspects of these computers are discussed and a brief review of their development is included, with particular attention paid to those characteristics that influence algorithm selection. Both direct and iterative methods are given for elliptic equations as well as explicit and implicit methods for initial boundary value problems. The intent is to point out attractive methods as well as areas where this class of computer architecture cannot be fully utilized because of either hardware restrictions or the lack of adequate algorithms. Application areas utilizing these computers are briefly discussed.
Computational Simulations and the Scientific Method
NASA Technical Reports Server (NTRS)
Kleb, Bil; Wood, Bill
2005-01-01
As scientific simulation software becomes more complicated, the scientific-software implementor's need for component tests from new model developers becomes more crucial. The community's ability to follow the basic premise of the Scientific Method requires independently repeatable experiments, and model innovators are in the best position to create these test fixtures. Scientific software developers also need to quickly judge the value of the new model, i.e., its cost-to-benefit ratio in terms of gains provided by the new model and implementation risks such as cost, time, and quality. This paper asks two questions. The first is whether other scientific software developers would find published component tests useful, and the second is whether model innovators think publishing test fixtures is a feasible approach.
Analysis of multigrid methods on massively parallel computers: Architectural implications
NASA Technical Reports Server (NTRS)
Matheson, Lesley R.; Tarjan, Robert E.
1993-01-01
We study the potential performance of multigrid algorithms running on massively parallel computers with the intent of discovering whether presently envisioned machines will provide an efficient platform for such algorithms. We consider the domain parallel version of the standard V cycle algorithm on model problems, discretized using finite difference techniques in two and three dimensions on block structured grids of size 10(exp 6) and 10(exp 9), respectively. Our models of parallel computation were developed to reflect the computing characteristics of the current generation of massively parallel multicomputers. These models are based on an interconnection network of 256 to 16,384 message passing, 'workstation size' processors executing in an SPMD mode. The first model accomplishes interprocessor communications through a multistage permutation network. The communication cost is a logarithmic function which is similar to the costs in a variety of different topologies. The second model allows single stage communication costs only. Both models were designed with information provided by machine developers and utilize implementation derived parameters. With the medium grain parallelism of the current generation and the high fixed cost of an interprocessor communication, our analysis suggests an efficient implementation requires the machine to support the efficient transmission of long messages, (up to 1000 words) or the high initiation cost of a communication must be significantly reduced through an alternative optimization technique. Furthermore, with variable length message capability, our analysis suggests the low diameter multistage networks provide little or no advantage over a simple single stage communications network.
Constructing Scientific Arguments Using Evidence from Dynamic Computational Climate Models
ERIC Educational Resources Information Center
Pallant, Amy; Lee, Hee-Sun
2015-01-01
Modeling and argumentation are two important scientific practices students need to develop throughout school years. In this paper, we investigated how middle and high school students (N = 512) construct a scientific argument based on evidence from computational models with which they simulated climate change. We designed scientific argumentation…
Constructing Scientific Arguments Using Evidence from Dynamic Computational Climate Models
ERIC Educational Resources Information Center
Pallant, Amy; Lee, Hee-Sun
2015-01-01
Modeling and argumentation are two important scientific practices students need to develop throughout school years. In this paper, we investigated how middle and high school students (N = 512) construct a scientific argument based on evidence from computational models with which they simulated climate change. We designed scientific argumentation…
The science of computing - The evolution of parallel processing
NASA Technical Reports Server (NTRS)
Denning, P. J.
1985-01-01
The present paper is concerned with the approaches to be employed to overcome the set of limitations in software technology which impedes currently an effective use of parallel hardware technology. The process required to solve the arising problems is found to involve four different stages. At the present time, Stage One is nearly finished, while Stage Two is under way. Tentative explorations are beginning on Stage Three, and Stage Four is more distant. In Stage One, parallelism is introduced into the hardware of a single computer, which consists of one or more processors, a main storage system, a secondary storage system, and various peripheral devices. In Stage Two, parallel execution of cooperating programs on different machines becomes explicit, while in Stage Three, new languages will make parallelism implicit. In Stage Four, there will be very high level user interfaces capable of interacting with scientists at the same level of abstraction as scientists do with each other.
Solving tridiagonal linear systems on the Butterfly parallel computer
Kumar, S.P.
1989-01-01
A parallel block partitioning method to solve a tri-diagonal system of linear equations is adapted to the BBN Butterfly multiprocessor. A performance analysis of the programming experiments on the 32-node Butterfly is presented. An upper bound on the number of processors to achieve the best performance with this method is derived. The computational results verify the theoretical speedup and efficiency results of the parallel algorithm over its serial counterpart. Also included is a study comparing performance runs of the same code on the Butterfly processor with a hardware floating point unit and on one with a software floating point facility. The total parallel time of the given code is considerably reduced by making use of the hardware floating point facility whereas the speedup and efficiency of the parallel program considerably improve on the system with software floating point capability. The achieved results are shown to be within 82% to 90% of the predicted performance.
Kindlmann, Gordon; Chiw, Charisee; Seltzer, Nicholas; Samuels, Lamont; Reppy, John
2016-01-01
Many algorithms for scientific visualization and image analysis are rooted in the world of continuous scalar, vector, and tensor fields, but are programmed in low-level languages and libraries that obscure their mathematical foundations. Diderot is a parallel domain-specific language that is designed to bridge this semantic gap by providing the programmer with a high-level, mathematical programming notation that allows direct expression of mathematical concepts in code. Furthermore, Diderot provides parallel performance that takes advantage of modern multicore processors and GPUs. The high-level notation allows a concise and natural expression of the algorithms and the parallelism allows efficient execution on real-world datasets.
NASA Astrophysics Data System (ADS)
Mighell, Kenneth John
2010-10-01
The development of parallel-processing image-analysis codes is generally a challenging task that requires complicated choreography of interprocessor communications. If, however, the image-analysis algorithm is embarrassingly parallel, then the development of a parallel-processing implementation of that algorithm can be a much easier task to accomplish because, by definition, there is little need for communication between the compute processes. I describe the design, implementation, and performance of a parallel-processing image-analysis application, called crblaster, which does cosmic-ray rejection of CCD images using the embarrassingly parallel l.a.cosmic algorithm. crblaster is written in C using the high-performance computing industry standard Message Passing Interface (MPI) library. crblaster uses a two-dimensional image partitioning algorithm that partitions an input image into N rectangular subimages of nearly equal area; the subimages include sufficient additional pixels along common image partition edges such that the need for communication between computer processes is eliminated. The code has been designed to be used by research scientists who are familiar with C as a parallel-processing computational framework that enables the easy development of parallel-processing image-analysis programs based on embarrassingly parallel algorithms. The crblaster source code is freely available at the official application Web site at the National Optical Astronomy Observatory. Removing cosmic rays from a single 800 × 800 pixel Hubble Space Telescope WFPC2 image takes 44 s with the IRAF script lacos_im.cl running on a single core of an Apple Mac Pro computer with two 2.8 GHz quad-core Intel Xeon processors. crblaster is 7.4 times faster when processing the same image on a single core on the same machine. Processing the same image with crblaster simultaneously on all eight cores of the same machine takes 0.875 s—which is a speedup factor of 50.3 times faster than the
Parallelization of Finite Element Analysis Codes Using Heterogeneous Distributed Computing
NASA Technical Reports Server (NTRS)
Ozguner, Fusun
1996-01-01
Performance gains in computer design are quickly consumed as users seek to analyze larger problems to a higher degree of accuracy. Innovative computational methods, such as parallel and distributed computing, seek to multiply the power of existing hardware technology to satisfy the computational demands of large applications. In the early stages of this project, experiments were performed using two large, coarse-grained applications, CSTEM and METCAN. These applications were parallelized on an Intel iPSC/860 hypercube. It was found that the overall speedup was very low, due to large, inherently sequential code segments present in the applications. The overall execution time T(sub par), of the application is dependent on these sequential segments. If these segments make up a significant fraction of the overall code, the application will have a poor speedup measure.
Parallel algorithms for computation of the manipulator inertia matrix
NASA Technical Reports Server (NTRS)
Amin-Javaheri, Masoud; Orin, David E.
1989-01-01
The development of an O(log2N) parallel algorithm for the manipulator inertia matrix is presented. It is based on the most efficient serial algorithm which uses the composite rigid body method. Recursive doubling is used to reformulate the linear recurrence equations which are required to compute the diagonal elements of the matrix. It results in O(log2N) levels of computation. Computation of the off-diagonal elements involves N linear recurrences of varying-size and a new method, which avoids redundant computation of position and orientation transforms for the manipulator, is developed. The O(log2N) algorithm is presented in both equation and graphic forms which clearly show the parallelism inherent in the algorithm.
A Low-Cost, Portable, Parallel Computing Cluster
NASA Astrophysics Data System (ADS)
Bullock, Daniel; Poppeliers, Christian; Allen, Charles
2006-10-01
Research in modern physical sciences has placed an increasing demand on computers for complex algorithms that push the limits of consumer personal computers. Parallel supercomputers are often required for large-scale algorithms, however the cost of these systems can be prohibitive. The purpose of this project is to construct a low-cost, portable, parallel computer system as an alternative to large-scale supercomputers, using Commercial Off The Shelf (COTS) components. These components can be networked together to allow processors to communicate with one another for faster computations. The overall design of this system is based on the development of ``Little Fe'' at Contra Costa College in San Pablo, California. Revisions to this design include improved design components, smaller physical size, easier transportation, less wiring, and a single AC power supply.
Variable-Complexity Multidisciplinary Optimization on Parallel Computers
NASA Technical Reports Server (NTRS)
Grossman, Bernard; Mason, William H.; Watson, Layne T.; Haftka, Raphael T.
1998-01-01
This report covers work conducted under grant NAG1-1562 for the NASA High Performance Computing and Communications Program (HPCCP) from December 7, 1993, to December 31, 1997. The objective of the research was to develop new multidisciplinary design optimization (MDO) techniques which exploit parallel computing to reduce the computational burden of aircraft MDO. The design of the High-Speed Civil Transport (HSCT) air-craft was selected as a test case to demonstrate the utility of our MDO methods. The three major tasks of this research grant included: development of parallel multipoint approximation methods for the aerodynamic design of the HSCT, use of parallel multipoint approximation methods for structural optimization of the HSCT, mathematical and algorithmic development including support in the integration of parallel computation for items (1) and (2). These tasks have been accomplished with the development of a response surface methodology that incorporates multi-fidelity models. For the aerodynamic design we were able to optimize with up to 20 design variables using hundreds of expensive Euler analyses together with thousands of inexpensive linear theory simulations. We have thereby demonstrated the application of CFD to a large aerodynamic design problem. For the predicting structural weight we were able to combine hundreds of structural optimizations of refined finite element models with thousands of optimizations based on coarse models. Computations have been carried out on the Intel Paragon with up to 128 nodes. The parallel computation allowed us to perform combined aerodynamic-structural optimization using state of the art models of a complex aircraft configurations.
Traffic simulations on parallel computers using domain decomposition techniques
Hanebutte, U.R.; Tentner, A.M.
1995-12-31
Large scale simulations of Intelligent Transportation Systems (ITS) can only be achieved by using the computing resources offered by parallel computing architectures. Domain decomposition techniques are proposed which allow the performance of traffic simulations with the standard simulation package TRAF-NETSIM on a 128 nodes IBM SPx parallel supercomputer as well as on a cluster of SUN workstations. Whilst this particular parallel implementation is based on NETSIM, a microscopic traffic simulation model, the presented strategy is applicable to a broad class of traffic simulations. An outer iteration loop must be introduced in order to converge to a global solution. A performance study that utilizes a scalable test network that consist of square-grids is presented, which addresses the performance penalty introduced by the additional iteration loop.
Parallelization of implicit finite difference schemes in computational fluid dynamics
NASA Technical Reports Server (NTRS)
Decker, Naomi H.; Naik, Vijay K.; Nicoules, Michel
1990-01-01
Implicit finite difference schemes are often the preferred numerical schemes in computational fluid dynamics, requiring less stringent stability bounds than the explicit schemes. Each iteration in an implicit scheme involves global data dependencies in the form of second and higher order recurrences. Efficient parallel implementations of such iterative methods are considerably more difficult and non-intuitive. The parallelization of the implicit schemes that are used for solving the Euler and the thin layer Navier-Stokes equations and that require inversions of large linear systems in the form of block tri-diagonal and/or block penta-diagonal matrices is discussed. Three-dimensional cases are emphasized and schemes that minimize the total execution time are presented. Partitioning and scheduling schemes for alleviating the effects of the global data dependencies are described. An analysis of the communication and the computation aspects of these methods is presented. The effect of the boundary conditions on the parallel schemes is also discussed.
Measuring performance of parallel computers. Progress report, 1989
Sullivan, F.
1994-07-01
Performance Measurement - the authors have developed a taxonomy of parallel algorithms based on data motion and example applications have been coded for each class of the taxonomy. Computational benchmark kernels have been extracted for several applications, and detailed measurements have been performed. Algorithms for Massively Parallel SIMD machines - measurement results and computational experiences indicate that top performance will be achieved by `iteration` type algorithms running on massively parallel SIMD machines. Reformulation as iteration may entail unorthodox approaches based on probabilistic methods. The authors have developed such methods for some applications. Here they discuss their approach to performance measurement, describe the taxonomy and measurements which have been made, and report on some general conclusions which can be drawn from the results of the measurements.
Multivariate Geographic Clustering Using a Beowulf-Style Parallel Computer
Hoffman, F.M.
1999-06-28
The authors present an application of multivariate non-hierarchical statistical clustering to geographic environmental data from the 48 conterminous United States in order to produce maps of regions of ecological similarity called ecoregions. Nine input variables thought to affect the growth of vegetation are clustered at a resolution of one square kilometer. These data represent over 7.8 million map cells in a 9-dimensional data space. For the analysis, the authors built a 126-node heterogeneous cluster--aptly named the Stone SouperComputer--out of surplus PCs. The authors developed a parallel iterative statistical clustering algorithm which uses the MPI message passing routines, employs a classical master/slave single program multiple data (SPMD) organization, performs dynamic load balancing, and provides fault tolerance. In addition to being run on the Stone SouperComputer, the parallel algorithm was tested on other parallel platforms without code modification. Finally, the results of the geographic clustering are presented.
Multivariate Geographic Clustering Using a Beowulf-Style Parallel Computer
Hargrove, W.W.; Hoffman, F.M.
1999-06-28
The authors present an application of multivariate non-hierarchical statistical clustering to geographic environmental data from the 48 conterminous United States in order to produce maps of regions of ecological similarity called ecoregions. Nine input variables thought to aflect the growth of vegetation are clustered at a resolution of one square kilometer. These data represent over 7.8 million map cells in a g-dimensional data space. For the analysis, the authors built a 126-node heterogeneous cluster--aptly named the Stone SouperComputer--out of surplus PCs. The authors developed a parallel iterative statistical clustering algorithm which uses the MPI message pawing routines, employs a classical master/slave single program multiple data (SPMD) organization, performs dynamic load balancing, and provides fault tolerance. In addition to being run on the Stone Souper-Computer, the parallel algorithm was tested on other parallel platforms without code modification. Finally, the results of the geographic clustering are presented.
Element-topology-independent preconditioners for parallel finite element computations
NASA Technical Reports Server (NTRS)
Park, K. C.; Alexander, Scott
1992-01-01
A family of preconditioners for the solution of finite element equations are presented, which are element-topology independent and thus can be applicable to element order-free parallel computations. A key feature of the present preconditioners is the repeated use of element connectivity matrices and their left and right inverses. The properties and performance of the present preconditioners are demonstrated via beam and two-dimensional finite element matrices for implicit time integration computations.
Element-topology-independent preconditioners for parallel finite element computations
NASA Technical Reports Server (NTRS)
Park, K. C.; Alexander, Scott
1992-01-01
A family of preconditioners for the solution of finite element equations are presented, which are element-topology independent and thus can be applicable to element order-free parallel computations. A key feature of the present preconditioners is the repeated use of element connectivity matrices and their left and right inverses. The properties and performance of the present preconditioners are demonstrated via beam and two-dimensional finite element matrices for implicit time integration computations.
75 FR 9887 - Advanced Scientific Computing Advisory Committee
Federal Register 2010, 2011, 2012, 2013, 2014
2010-03-04
... Baker, Office of Advanced Scientific Computing Research; SC-21/Germantown Building; U.S. Department of... meeting is to provide advice on the advanced scientific computing research program. Tentative Agenda... Information Public Reading Room; 1E-190, Forrestal Building; 1000 Independence Avenue, SW.; Washington, DC...
Adaptive Methods and Parallel Computation for Partial Differential Equations
1992-05-01
E. Batcher, W. C. Meilander, and J. L. Potter, Eds ., Proceedings of the Inter- national Conference on Parallel Processing, Computer Society Press...11. P. L. Baehmann, S. L. Wittchen , M. S. Shephard, K. R. Grice, and M. A. Yerry, Robust, geometrically based, automatic two-dimensional mesh
Hardware packet pacing using a DMA in a parallel computer
Chen, Dong; Heidelberger, Phillip; Vranas, Pavlos
2013-08-13
Method and system for hardware packet pacing using a direct memory access controller in a parallel computer which, in one aspect, keeps track of a total number of bytes put on the network as a result of a remote get operation, using a hardware token counter.
Efficiently modeling neural networks on massively parallel computers
NASA Technical Reports Server (NTRS)
Farber, Robert M.
1993-01-01
Neural networks are a very useful tool for analyzing and modeling complex real world systems. Applying neural network simulations to real world problems generally involves large amounts of data and massive amounts of computation. To efficiently handle the computational requirements of large problems, we have implemented at Los Alamos a highly efficient neural network compiler for serial computers, vector computers, vector parallel computers, and fine grain SIMD computers such as the CM-2 connection machine. This paper describes the mapping used by the compiler to implement feed-forward backpropagation neural networks for a SIMD (Single Instruction Multiple Data) architecture parallel computer. Thinking Machines Corporation has benchmarked our code at 1.3 billion interconnects per second (approximately 3 gigaflops) on a 64,000 processor CM-2 connection machine (Singer 1990). This mapping is applicable to other SIMD computers and can be implemented on MIMD computers such as the CM-5 connection machine. Our mapping has virtually no communications overhead with the exception of the communications required for a global summation across the processors (which has a sub-linear runtime growth on the order of O(log(number of processors)). We can efficiently model very large neural networks which have many neurons and interconnects and our mapping can extend to arbitrarily large networks (within memory limitations) by merging the memory space of separate processors with fast adjacent processor interprocessor communications. This paper will consider the simulation of only feed forward neural network although this method is extendable to recurrent networks.
Fencing data transfers in a parallel active messaging interface of a parallel computer
Blocksome, Michael A.; Mamidala, Amith R.
2015-08-11
Fencing data transfers in a parallel active messaging interface (`PAMI`) of a parallel computer, the PAMI including data communications endpoints, each endpoint comprising a specification of data communications parameters for a thread of execution on a compute node, including specifications of a client, a context, and a task, the compute nodes coupled for data communications through the PAMI and through data communications resources including a deterministic data communications network, including initiating execution through the PAMI of an ordered sequence of active SEND instructions for SEND data transfers between two endpoints, effecting deterministic SEND data transfers; and executing through the PAMI, with no FENCE accounting for SEND data transfers, an active FENCE instruction, the FENCE instruction completing execution only after completion of all SEND instructions initiated prior to execution of the FENCE instruction for SEND data transfers between the two endpoints.
Fencing data transfers in a parallel active messaging interface of a parallel computer
Blocksome, Michael A.; Mamidala, Amith R.
2015-06-02
Fencing data transfers in a parallel active messaging interface (`PAMI`) of a parallel computer, the PAMI including data communications endpoints, each endpoint including a specification of data communications parameters for a thread of execution on a compute node, including specifications of a client, a context, and a task; the compute nodes coupled for data communications through the PAMI and through data communications resources including at least one segment of shared random access memory; including initiating execution through the PAMI of an ordered sequence of active SEND instructions for SEND data transfers between two endpoints, effecting deterministic SEND data transfers through a segment of shared memory; and executing through the PAMI, with no FENCE accounting for SEND data transfers, an active FENCE instruction, the FENCE instruction completing execution only after completion of all SEND instructions initiated prior to execution of the FENCE instruction for SEND data transfers between the two endpoints.
Fencing data transfers in a parallel active messaging interface of a parallel computer
Blocksome, Michael A.; Mamidala, Amith R.
2015-06-09
Fencing data transfers in a parallel active messaging interface (`PAMI`) of a parallel computer, the PAMI including data communications endpoints, each endpoint including a specification of data communications parameters for a thread of execution on a compute node, including specifications of a client, a context, and a task; the compute nodes coupled for data communications through the PAMI and through data communications resources including at least one segment of shared random access memory; including initiating execution through the PAMI of an ordered sequence of active SEND instructions for SEND data transfers between two endpoints, effecting deterministic SEND data transfers through a segment of shared memory; and executing through the PAMI, with no FENCE accounting for SEND data transfers, an active FENCE instruction, the FENCE instruction completing execution only after completion of all SEND instructions initiated prior to execution of the FENCE instruction for SEND data transfers between the two endpoints.
Fencing data transfers in a parallel active messaging interface of a parallel computer
Blocksome, Michael A.; Mamidala, Amith R.
2015-06-30
Fencing data transfers in a parallel active messaging interface (`PAMI`) of a parallel computer, the PAMI including data communications endpoints, each endpoint comprising a specification of data communications parameters for a thread of execution on a compute node, including specifications of a client, a context, and a task, the compute nodes coupled for data communications through the PAMI and through data communications resources including a deterministic data communications network, including initiating execution through the PAMI of an ordered sequence of active SEND instructions for SEND data transfers between two endpoints, effecting deterministic SEND data transfers; and executing through the PAMI, with no FENCE accounting for SEND data transfers, an active FENCE instruction, the FENCE instruction completing execution only after completion of all SEND instructions initiated prior to execution of the FENCE instruction for SEND data transfers between the two endpoints.
Computing through Scientific Abstractions in SysBioPS
Chin, George; Stephan, Eric G.; Gracio, Deborah K.
2004-10-13
Today, biologists and bioinformaticists have a tremendous amount of computational power at their disposal. With the availability of supercomputers, burgeoning scientific databases and digital libraries such as GenBank and PubMed, and pervasive computational environments such as the Grid, biologists have access to a wealth of computational capabilities and scientific data at hand. Yet, the rapid development of computational technologies has far exceeded the typical biologist’s ability to effectively apply the technology in their research. Computational sciences research and development efforts such as the Biology Workbench, BioSPICE (Biological Simulation Program for Intra-Cellular Evaluation), and BioCoRE (Biological Collaborative Research Environment) are important in connecting biologists and their scientific problems to computational infrastructures. On the Computational Cell Environment and Heuristic Entity-Relationship Building Environment projects at the Pacific Northwest National Laboratory, we are jointly developing a new breed of scientific problem solving environment called SysBioPSE that will allow biologists to access and apply computational resources in the scientific research context. In contrast to other computational science environments, SysBioPSE operates as an abstraction layer above a computational infrastructure. The goal of SysBioPSE is to allow biologists to apply computational resources in the context of the scientific problems they are addressing and the scientific perspectives from which they conduct their research. More specifically, SysBioPSE allows biologists to capture and represent scientific concepts and theories and experimental processes, and to link these views to scientific applications, data repositories, and computer systems.
Scientific computing infrastructure and services in Moldova
NASA Astrophysics Data System (ADS)
Bogatencov, P. P.; Secrieru, G. V.; Degteariov, N. V.; Iliuha, N. P.
2016-09-01
In recent years distributed information processing and high-performance computing technologies (HPC, distributed Cloud and Grid computing infrastructures) for solving complex tasks with high demands of computing resources are actively developing. In Moldova the works on creation of high-performance and distributed computing infrastructures were started relatively recently due to participation in implementation of a number of international projects. Research teams from Moldova participated in a series of regional and pan-European projects that allowed them to begin forming the national heterogeneous computing infrastructure, get access to regional and European computing resources, and expand the range and areas of solving tasks.
High Performance Input/Output for Parallel Computer Systems
NASA Technical Reports Server (NTRS)
Ligon, W. B.
1996-01-01
The goal of our project is to study the I/O characteristics of parallel applications used in Earth Science data processing systems such as Regional Data Centers (RDCs) or EOSDIS. Our approach is to study the runtime behavior of typical programs and the effect of key parameters of the I/O subsystem both under simulation and with direct experimentation on parallel systems. Our three year activity has focused on two items: developing a test bed that facilitates experimentation with parallel I/O, and studying representative programs from the Earth science data processing application domain. The Parallel Virtual File System (PVFS) has been developed for use on a number of platforms including the Tiger Parallel Architecture Workbench (TPAW) simulator, The Intel Paragon, a cluster of DEC Alpha workstations, and the Beowulf system (at CESDIS). PVFS provides considerable flexibility in configuring I/O in a UNIX- like environment. Access to key performance parameters facilitates experimentation. We have studied several key applications fiom levels 1,2 and 3 of the typical RDC processing scenario including instrument calibration and navigation, image classification, and numerical modeling codes. We have also considered large-scale scientific database codes used to organize image data.
Computationally efficient implementation of combustion chemistry in parallel PDF calculations
Lu Liuyan Lantz, Steven R.; Ren Zhuyin; Pope, Stephen B.
2009-08-20
In parallel calculations of combustion processes with realistic chemistry, the serial in situ adaptive tabulation (ISAT) algorithm [S.B. Pope, Computationally efficient implementation of combustion chemistry using in situ adaptive tabulation, Combustion Theory and Modelling, 1 (1997) 41-63; L. Lu, S.B. Pope, An improved algorithm for in situ adaptive tabulation, Journal of Computational Physics 228 (2009) 361-386] substantially speeds up the chemistry calculations on each processor. To improve the parallel efficiency of large ensembles of such calculations in parallel computations, in this work, the ISAT algorithm is extended to the multi-processor environment, with the aim of minimizing the wall clock time required for the whole ensemble. Parallel ISAT strategies are developed by combining the existing serial ISAT algorithm with different distribution strategies, namely purely local processing (PLP), uniformly random distribution (URAN), and preferential distribution (PREF). The distribution strategies enable the queued load redistribution of chemistry calculations among processors using message passing. They are implemented in the software x2f{sub m}pi, which is a Fortran 95 library for facilitating many parallel evaluations of a general vector function. The relative performance of the parallel ISAT strategies is investigated in different computational regimes via the PDF calculations of multiple partially stirred reactors burning methane/air mixtures. The results show that the performance of ISAT with a fixed distribution strategy strongly depends on certain computational regimes, based on how much memory is available and how much overlap exists between tabulated information on different processors. No one fixed strategy consistently achieves good performance in all the regimes. Therefore, an adaptive distribution strategy, which blends PLP, URAN and PREF, is devised and implemented. It yields consistently good performance in all regimes. In the adaptive
Computationally efficient implementation of combustion chemistry in parallel PDF calculations
NASA Astrophysics Data System (ADS)
Lu, Liuyan; Lantz, Steven R.; Ren, Zhuyin; Pope, Stephen B.
2009-08-01
In parallel calculations of combustion processes with realistic chemistry, the serial in situ adaptive tabulation (ISAT) algorithm [S.B. Pope, Computationally efficient implementation of combustion chemistry using in situ adaptive tabulation, Combustion Theory and Modelling, 1 (1997) 41-63; L. Lu, S.B. Pope, An improved algorithm for in situ adaptive tabulation, Journal of Computational Physics 228 (2009) 361-386] substantially speeds up the chemistry calculations on each processor. To improve the parallel efficiency of large ensembles of such calculations in parallel computations, in this work, the ISAT algorithm is extended to the multi-processor environment, with the aim of minimizing the wall clock time required for the whole ensemble. Parallel ISAT strategies are developed by combining the existing serial ISAT algorithm with different distribution strategies, namely purely local processing (PLP), uniformly random distribution (URAN), and preferential distribution (PREF). The distribution strategies enable the queued load redistribution of chemistry calculations among processors using message passing. They are implemented in the software x2f_mpi, which is a Fortran 95 library for facilitating many parallel evaluations of a general vector function. The relative performance of the parallel ISAT strategies is investigated in different computational regimes via the PDF calculations of multiple partially stirred reactors burning methane/air mixtures. The results show that the performance of ISAT with a fixed distribution strategy strongly depends on certain computational regimes, based on how much memory is available and how much overlap exists between tabulated information on different processors. No one fixed strategy consistently achieves good performance in all the regimes. Therefore, an adaptive distribution strategy, which blends PLP, URAN and PREF, is devised and implemented. It yields consistently good performance in all regimes. In the adaptive parallel
Aggregating job exit statuses of a plurality of compute nodes executing a parallel application
Aho, Michael E.; Attinella, John E.; Gooding, Thomas M.; Mundy, Michael B.
2015-07-21
Aggregating job exit statuses of a plurality of compute nodes executing a parallel application, including: identifying a subset of compute nodes in the parallel computer to execute the parallel application; selecting one compute node in the subset of compute nodes in the parallel computer as a job leader compute node; initiating execution of the parallel application on the subset of compute nodes; receiving an exit status from each compute node in the subset of compute nodes, where the exit status for each compute node includes information describing execution of some portion of the parallel application by the compute node; aggregating each exit status from each compute node in the subset of compute nodes; and sending an aggregated exit status for the subset of compute nodes in the parallel computer.
Berkeley Lab Computing Sciences: Accelerating Scientific Discovery
Hules, John A
2008-12-12
Scientists today rely on advances in computer science, mathematics, and computational science, as well as large-scale computing and networking facilities, to increase our understanding of ourselves, our planet, and our universe. Berkeley Lab's Computing Sciences organization researches, develops, and deploys new tools and technologies to meet these needs and to advance research in such areas as global climate change, combustion, fusion energy, nanotechnology, biology, and astrophysics.
Superfast robust digital image correlation analysis with parallel computing
NASA Astrophysics Data System (ADS)
Pan, Bing; Tian, Long
2015-03-01
Existing digital image correlation (DIC) using the robust reliability-guided displacement tracking (RGDT) strategy for full-field displacement measurement is a path-dependent process that can only be executed sequentially. This path-dependent tracking strategy not only limits the potential of DIC for further improvement of its computational efficiency but also wastes the parallel computing power of modern computers with multicore processors. To maintain the robustness of the existing RGDT strategy and to overcome its deficiency, an improved RGDT strategy using a two-section tracking scheme is proposed. In the improved RGDT strategy, the calculated points with correlation coefficients higher than a preset threshold are all taken as reliably computed points and given the same priority to extend the correlation analysis to their neighbors. Thus, DIC calculation is first executed in parallel at multiple points by separate independent threads. Then for the few calculated points with correlation coefficients smaller than the threshold, DIC analysis using existing RGDT strategy is adopted. Benefiting from the improved RGDT strategy and the multithread computing, superfast DIC analysis can be accomplished without sacrificing its robustness and accuracy. Experimental results show that the presented parallel DIC method performed on a common eight-core laptop can achieve about a 7 times speedup.
Parallel peak pruning for scalable SMP contour tree computation
Carr, Hamish A.; Weber, Gunther H.; Sewell, Christopher M.; ...
2017-03-09
As data sets grow to exascale, automated data analysis and visualisation are increasingly important, to intermediate human understanding and to reduce demands on disk storage via in situ analysis. Trends in architecture of high performance computing systems necessitate analysis algorithms to make effective use of combinations of massively multicore and distributed systems. One of the principal analytic tools is the contour tree, which analyses relationships between contours to identify features of more than local importance. Unfortunately, the predominant algorithms for computing the contour tree are explicitly serial, and founded on serial metaphors, which has limited the scalability of this formmore » of analysis. While there is some work on distributed contour tree computation, and separately on hybrid GPU-CPU computation, there is no efficient algorithm with strong formal guarantees on performance allied with fast practical performance. Here in this paper, we report the first shared SMP algorithm for fully parallel contour tree computation, withfor-mal guarantees of O(lgnlgt) parallel steps and O(n lgn) work, and implementations with up to 10x parallel speed up in OpenMP and up to 50x speed up in NVIDIA Thrust.« less
A Simple Physical Optics Algorithm Perfect for Parallel Computing
NASA Technical Reports Server (NTRS)
Imbriale, W. A.; Cwik, T.
1993-01-01
One of the simplest reflector antenna computer programs is based upon a discrete approximation of the radiation integral. This calculation replaces the actual reflector surface with a triangular facet representation so that the reflector resembles a geodesic dome. The Physical Optics (PO) current is assumed to be constant in magnitude and phase over each facet so the radiation integral is reduced to a simple summation. This program has proven to be surprisingly robust and useful for the analysis of arbitrary reflectors, particularly when the near-field is desired and surface derivatives are not known. Because of its simplicity, the algorithm has proven to be extremely easy to adapt to the parallel computing architecture of a modest number of large-grain computing elements such as are used in the Intel iPSC and Touchstone Delta parallel machines.
A Simple Physical Optics Algorithm Perfect for Parallel Computing
NASA Technical Reports Server (NTRS)
Imbriale, W. A.; Cwik, T.
1993-01-01
One of the simplest reflector antenna computer programs is based upon a discrete approximation of the radiation integral. This calculation replaces the actual reflector surface with a triangular facet representation so that the reflector resembles a geodesic dome. The Physical Optics (PO) current is assumed to be constant in magnitude and phase over each facet so the radiation integral is reduced to a simple summation. This program has proven to be surprisingly robust and useful for the analysis of arbitrary reflectors, particularly when the near-field is desired and surface derivatives are not known. Because of its simplicity, the algorithm has proven to be extremely easy to adapt to the parallel computing architecture of a modest number of large-grain computing elements such as are used in the Intel iPSC and Touchstone Delta parallel machines.
Boundary element analysis on vector and parallel computers
NASA Technical Reports Server (NTRS)
Kane, J. H.
1994-01-01
Boundary element analysis (BEA) can be characterized as a numerical technique that generally shifts the computational burden in the analysis toward numerical integration and the solution of nonsymmetric and either dense or blocked sparse systems of algebraic equations. Researchers have explored the concept that the fundamental characteristics of BEA can be exploited to generate effective implementations on vector and parallel computers. In this paper, the results of some of these investigations are discussed. The performance of overall algorithms for BEA on vector supercomputers, massively data parallel single instruction multiple data (SIMD), and relatively fine grained distributed memory multiple instruction multiple data (MIMD) computer systems is described. Some general trends and conclusions are discussed, along with indications of future developments that may prove fruitful in this regard.
Computing NLTE Opacities -- Node Level Parallel
Holladay, Daniel
2015-09-11
Presentation. The goal: to produce a robust library capable of computing reasonably accurate opacities inline with the assumption of LTE relaxed (non-LTE). Near term: demonstrate acceleration of non-LTE opacity computation. Far term (if funded): connect to application codes with in-line capability and compute opacities. Study science problems. Use efficient algorithms that expose many levels of parallelism and utilize good memory access patterns for use on advanced architectures. Portability to multiple types of hardware including multicore processors, manycore processors such as KNL, GPUs, etc. Easily coupled to radiation hydrodynamics and thermal radiative transfer codes.
Parallel distance matrix computation for Matlab data mining
NASA Astrophysics Data System (ADS)
Skurowski, Przemysław; Staniszewski, Michał
2016-06-01
The paper presents utility functions for computing of a distance matrix, which plays a crucial role in data mining. The goal in the design was to enable operating on relatively large datasets by overcoming basic shortcoming - computing time - with an interface easy to use. The presented solution is a set of functions, which were created with emphasis on practical applicability in real life. The proposed solution is presented along the theoretical background for the performance scaling. Furthermore, different approaches of the parallel computing are analyzed, including shared memory, which is uncommon in Matlab environment.
Parallel computing in genomic research: advances and applications
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
Parallelism in computational chemistry: Applications in quantum and statistical mechanics
NASA Astrophysics Data System (ADS)
Clementi, E.; Corongiu, G.; Detrich, J. H.; Kahnmohammadbaigi, H.; Chin, S.; Domingo, L.; Laaksonen, A.; Nguyen, N. L.
1985-08-01
Often very fundamental biochemical and biophysical problems defy simulations because of limitation in today's computers. We present and discuss a distributed system composed of two IBM-4341 and one IBM-4381, as front-end processors, and ten FPS-164 attached array processors. This parallel system-called LCAP-has presently a peak performance of about 120 MFlops; extensions to higher performance are discussed. Presently, the system applications use a modified version of VM/SP as the operating system: description of the modifications is given. Three applications programs have migrated from sequential to parallel; a molecular quantum mechanical, a Metropolis-Monte Carlo and a Molecular Dynamics program. Descriptions of the parallel codes are briefly outlined. As examples and tests of these applications we report on a study for proton tunneling in DNA base-pairs, very relevant to spontaneous mutations in genetics. As a second example, we present a Monte Carlo study of liquid water at room temperature where not only two- and three-body interactions are considered but-for the first time-also four-body interactions are included. Finally we briefly summarize a molecular dynamics study where two- and three-body interactions have been considered. These examples, and very positive performance comparison with today's supercomputers allow us to conclude that parallel computers and programming of the type we have considered, represent a pragmatic answer to many computer intensive problems.
New Parallel computing framework for radiation transport codes
Kostin, M.A.; Mokhov, N.V.; Niita, K.; /JAERI, Tokai
2010-09-01
A new parallel computing framework has been developed to use with general-purpose radiation transport codes. The framework was implemented as a C++ module that uses MPI for message passing. The module is significantly independent of radiation transport codes it can be used with, and is connected to the codes by means of a number of interface functions. The framework was integrated with the MARS15 code, and an effort is under way to deploy it in PHITS. Besides the parallel computing functionality, the framework offers a checkpoint facility that allows restarting calculations with a saved checkpoint file. The checkpoint facility can be used in single process calculations as well as in the parallel regime. Several checkpoint files can be merged into one thus combining results of several calculations. The framework also corrects some of the known problems with the scheduling and load balancing found in the original implementations of the parallel computing functionality in MARS15 and PHITS. The framework can be used efficiently on homogeneous systems and networks of workstations, where the interference from the other users is possible.
Parallel computing in genomic research: advances and applications.
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.
Experiences with the Lanczos method on a parallel computer
NASA Technical Reports Server (NTRS)
Bostic, Susan W.; Fulton, Robert E.
1987-01-01
A parallel computer implementation of the Lanczos method for the free-vibration analysis of structures is considered, and results for two example problems show substantial time-reduction over the sequential solutions. The major Lanczos calculation tasks are subdivided into subtasks, and parallelism is introduced at the subtask level. A speedup of 7.8 on eight processors was obtained for the decomposition step of the problem involving a 60-m three-longeron space mast, and a speedup of 14.6 on 16 processors was obtained for the decomposition step of the problem involving a blade-stiffened graphite-epoxy panel.
Good enough practices in scientific computing.
Wilson, Greg; Bryan, Jennifer; Cranston, Karen; Kitzes, Justin; Nederbragt, Lex; Teal, Tracy K
2017-06-01
Computers are now essential in all branches of science, but most researchers are never taught the equivalent of basic lab skills for research computing. As a result, data can get lost, analyses can take much longer than necessary, and researchers are limited in how effectively they can work with software and data. Computing workflows need to follow the same practices as lab projects and notebooks, with organized data, documented steps, and the project structured for reproducibility, but researchers new to computing often don't know where to start. This paper presents a set of good computing practices that every researcher can adopt, regardless of their current level of computational skill. These practices, which encompass data management, programming, collaborating with colleagues, organizing projects, tracking work, and writing manuscripts, are drawn from a wide variety of published sources from our daily lives and from our work with volunteer organizations that have delivered workshops to over 11,000 people since 2010.
Data communications in a parallel active messaging interface of a parallel computer
Archer, Charles J; Blocksome, Michael A; Ratterman, Joseph D; Smith, Brian E
2015-02-03
Data communications in a parallel active messaging interface (`PAMI`) of a parallel computer, the PAMI composed of data communications endpoints, each endpoint including a specification of data communications parameters for a thread of execution on a compute node, including specifications of a client, a context, and a task, endpoints coupled for data communications through the PAMI and through data communications resources, including receiving in an origin endpoint of the PAMI a SEND instruction, the SEND instruction specifying a transmission of transfer data from the origin endpoint to a first target endpoint; transmitting from the origin endpoint to the first target endpoint a Request-To-Send (`RTS`) message advising the first target endpoint of the location and size of the transfer data; assigning by the first target endpoint to each of a plurality of target endpoints separate portions of the transfer data; and receiving by the plurality of target endpoints the transfer data.
Data communications in a parallel active messaging interface of a parallel computer
Archer, Charles J; Blocksome, Michael A; Ratterman, Joseph D; Smith, Brian E
2014-11-18
Data communications in a parallel active messaging interface (`PAMI`) of a parallel computer, the PAMI composed of data communications endpoints, each endpoint including a specification of data communications parameters for a thread of execution on a compute node, including specifications of a client, a context, and a task, endpoints coupled for data communications through the PAMI and through data communications resources, including receiving in an origin endpoint of the PAMI a SEND instruction, the SEND instruction specifying a transmission of transfer data from the origin endpoint to a first target endpoint; transmitting from the origin endpoint to the first target endpoint a Request-To-Send (`RTS`) message advising the first target endpoint of the location and size of the transfer data; assigning by the first target endpoint to each of a plurality of target endpoints separate portions of the transfer data; and receiving by the plurality of target endpoints the transfer data.
User documentation for PVODE, an ODE solver for parallel computers
Hindmarsh, A.C., LLNL
1998-05-01
PVODE is a general purpose ordinary differential equation (ODE) solver for stiff and nonstiff ODES It is based on CVODE [5] [6], which is written in ANSI- standard C PVODE uses MPI (Message-Passing Interface) [8] and a revised version of the vector module in CVODE to achieve parallelism and portability PVODE is intended for the SPMD (Single Program Multiple Data) environment with distributed memory, in which all vectors are identically distributed across processors In particular, the vector module is designed to help the user assign a contiguous segment of a given vector to each of the processors for parallel computation The idea is for each processor to solve a certain fixed subset of the ODES To better understand PVODE, we first need to understand CVODE and its historical background The ODE solver CVODE, which was written by Cohen and Hindmarsh, combines features of two earlier Fortran codes, VODE [l] and VODPK [3] Those two codes were written by Brown, Byrne, and Hindmarsh. Both use variable-coefficient multi-step integration methods, and address both stiff and nonstiff systems (Stiffness is defined as the presence of one or more very small damping time constants ) VODE uses direct linear algebraic techniques to solve the underlying banded or dense linear systems of equations in conjunction with a modified Newton method in the stiff ODE case On the other hand, VODPK uses a preconditioned Krylov iterative method [2] to solve the underlying linear system User-supplied preconditioners directly address the dominant source of stiffness Consequently, CVODE implements both the direct and iterative methods Currently, with regard to the nonlinear and linear system solution, PVODE has three method options available. functional iteration, Newton iteration with a diagonal approximate Jacobian, and Newton iteration with the iterative method SPGMR (Scaled Preconditioned Generalized Minimal Residual method) Both CVODE and PVODE are written in such a way that other linear
The 2nd Symposium on the Frontiers of Massively Parallel Computations
NASA Technical Reports Server (NTRS)
Mills, Ronnie (Editor)
1988-01-01
Programming languages, computer graphics, neural networks, massively parallel computers, SIMD architecture, algorithms, digital terrain models, sort computation, simulation of charged particle transport on the massively parallel processor and image processing are among the topics discussed.
Distributed parallel computing in stochastic modeling of groundwater systems.
Dong, Yanhui; Li, Guomin; Xu, Haizhen
2013-03-01
Stochastic modeling is a rapidly evolving, popular approach to the study of the uncertainty and heterogeneity of groundwater systems. However, the use of Monte Carlo-type simulations to solve practical groundwater problems often encounters computational bottlenecks that hinder the acquisition of meaningful results. To improve the computational efficiency, a system that combines stochastic model generation with MODFLOW-related programs and distributed parallel processing is investigated. The distributed computing framework, called the Java Parallel Processing Framework, is integrated into the system to allow the batch processing of stochastic models in distributed and parallel systems. As an example, the system is applied to the stochastic delineation of well capture zones in the Pinggu Basin in Beijing. Through the use of 50 processing threads on a cluster with 10 multicore nodes, the execution times of 500 realizations are reduced to 3% compared with those of a serial execution. Through this application, the system demonstrates its potential in solving difficult computational problems in practical stochastic modeling. © 2012, The Author(s). Groundwater © 2012, National Ground Water Association.
3D seismic imaging on massively parallel computers
Womble, D.E.; Ober, C.C.; Oldfield, R.
1997-02-01
The ability to image complex geologies such as salt domes in the Gulf of Mexico and thrusts in mountainous regions is a key to reducing the risk and cost associated with oil and gas exploration. Imaging these structures, however, is computationally expensive. Datasets can be terabytes in size, and the processing time required for the multiple iterations needed to produce a velocity model can take months, even with the massively parallel computers available today. Some algorithms, such as 3D, finite-difference, prestack, depth migration remain beyond the capacity of production seismic processing. Massively parallel processors (MPPs) and algorithms research are the tools that will enable this project to provide new seismic processing capabilities to the oil and gas industry. The goals of this work are to (1) develop finite-difference algorithms for 3D, prestack, depth migration; (2) develop efficient computational approaches for seismic imaging and for processing terabyte datasets on massively parallel computers; and (3) develop a modular, portable, seismic imaging code.
Comprehensive climate system modeling on massively parallel computers
Wehner, M.F.; Eltgroth, P.G.; Mirin, A.A.; Duffy, P.B.; Caldeira, K.G.; Bolstad, J.H.; Wang, H.; Matarazzo, C.M.; Creach, U,E.
1996-10-01
A better understanding of both natural and human induced changes to the Earth`s climate is necessary for policy makers to make informed decisions regarding energy usage and other greenhouse gas producing activities. To achieve this, substantial increases in the sophistication of climate models are required. Coupling between the climate subsystems of the atmosphere, oceans, cryosphere and biosphere is only now beginning to be explored in global models. the enormous computational expenses of such models is one significant factor limiting progress. A comprehensive climate system model targeted to distributed memory massively parallel processing (MPP) computers is under development at Lawrence Livermore National Laboratory. This class of computers promises the computational power to permit the timely execution of climate models of substantially more sophistication than current generation models. Our strategy for achieving high performance on large numbers of processors is to exploit the multiple layers of parallelism naturally contained within highly coupled global climate models. The centerpiece of this strategy is the concurrent execution of multiple independently parallelized components of the climate system model. This methodology allows the assignment of an arbitrary number of processors to each of the major climate subsystems. Hence, a higher total number of processors may be efficiently used. Furthermore, load imbalances arising from the coupling of submodels may be minimized by adjusting the distribution of processors among the submodels.
A language comparison for scientific computing on MIMD architectures
NASA Technical Reports Server (NTRS)
Jones, Mark T.; Patrick, Merrell L.; Voigt, Robert G.
1989-01-01
Choleski's method for solving banded symmetric, positive definite systems is implemented on a multiprocessor computer using three FORTRAN based parallel programming languages, the Force, PISCES and Concurrent FORTRAN. The capabilities of the language for expressing parallelism and their user friendliness are discussed, including readability of the code, debugging assistance offered, and expressiveness of the languages. The performance of the different implementations is compared. It is argued that PISCES, using the Force for medium-grained parallelism, is the appropriate choice for programming Choleski's method on the multiprocessor computer, Flex/32.
Implementation of ADI: Schemes on MIMD parallel computers
NASA Technical Reports Server (NTRS)
Vanderwijngaart, Rob F.
1993-01-01
In order to simulate the effects of the impingement of hot exhaust jets of High Performance Aircraft on landing surfaces a multi-disciplinary computation coupling flow dynamics to heat conduction in the runway needs to be carried out. Such simulations, which are essentially unsteady, require very large computational power in order to be completed within a reasonable time frame of the order of an hour. Such power can be furnished by the latest generation of massively parallel computers. These remove the bottleneck of ever more congested data paths to one or a few highly specialized central processing units (CPU's) by having many off-the-shelf CPU's work independently on their own data, and exchange information only when needed. During the past year the first phase of this project was completed, in which the optimal strategy for mapping an ADI-algorithm for the three dimensional unsteady heat equation to a MIMD parallel computer was identified. This was done by implementing and comparing three different domain decomposition techniques that define the tasks for the CPU's in the parallel machine. These implementations were done for a Cartesian grid and Dirichlet boundary conditions. The most promising technique was then used to implement the heat equation solver on a general curvilinear grid with a suite of nontrivial boundary conditions. Finally, this technique was also used to implement the Scalar Penta-diagonal (SP) benchmark, which was taken from the NAS Parallel Benchmarks report. All implementations were done in the programming language C on the Intel iPSC/860 computer.
InSAR Scientific Computing Environment - The Home Stretch
NASA Astrophysics Data System (ADS)
Rosen, P. A.; Gurrola, E. M.; Sacco, G.; Zebker, H. A.
2011-12-01
The Interferometric Synthetic Aperture Radar (InSAR) Scientific Computing Environment (ISCE) is a software development effort in its third and final year within the NASA Advanced Information Systems and Technology program. The ISCE is a new computing environment for geodetic image processing for InSAR sensors enabling scientists to reduce measurements directly from radar satellites to new geophysical products with relative ease. The environment can serve as the core of a centralized processing center to bring Level-0 raw radar data up to Level-3 data products, but is adaptable to alternative processing approaches for science users interested in new and different ways to exploit mission data. Upcoming international SAR missions will deliver data of unprecedented quantity and quality, making possible global-scale studies in climate research, natural hazards, and Earth's ecosystem. The InSAR Scientific Computing Environment has the functionality to become a key element in processing data from NASA's proposed DESDynI mission into higher level data products, supporting a new class of analyses that take advantage of the long time and large spatial scales of these new data. At the core of ISCE is a new set of efficient and accurate InSAR algorithms. These algorithms are placed into an object-oriented, flexible, extensible software package that is informed by modern programming methods, including rigorous componentization of processing codes, abstraction and generalization of data models. The environment is designed to easily allow user contributions, enabling an open source community to extend the framework into the indefinite future. ISCE supports data from nearly all of the available satellite platforms, including ERS, EnviSAT, Radarsat-1, Radarsat-2, ALOS, TerraSAR-X, and Cosmo-SkyMed. The code applies a number of parallelization techniques and sensible approximations for speed. It is configured to work on modern linux-based computers with gcc compilers and python
Using Interactive Computer to Communicate Scientific Information.
ERIC Educational Resources Information Center
Selnow, Gary W.
1988-01-01
Asks whether the computer is another channel of communication, if its interactive qualities make it an information source, or if it is an undefined hybrid. Concludes that computers are neither the medium nor the source but will in the future provide the possibility of a sophisticated interaction between human intelligence and artificial…
Using Interactive Computer to Communicate Scientific Information.
ERIC Educational Resources Information Center
Selnow, Gary W.
1988-01-01
Asks whether the computer is another channel of communication, if its interactive qualities make it an information source, or if it is an undefined hybrid. Concludes that computers are neither the medium nor the source but will in the future provide the possibility of a sophisticated interaction between human intelligence and artificial…
Executing a gather operation on a parallel computer
Archer, Charles J [Rochester, MN; Ratterman, Joseph D [Rochester, MN
2012-03-20
Methods, apparatus, and computer program products are disclosed for executing a gather operation on a parallel computer according to embodiments of the present invention. Embodiments include configuring, by the logical root, a result buffer or the logical root, the result buffer having positions, each position corresponding to a ranked node in the operational group and for storing contribution data gathered from that ranked node. Embodiments also include repeatedly for each position in the result buffer: determining, by each compute node of an operational group, whether the current position in the result buffer corresponds with the rank of the compute node, if the current position in the result buffer corresponds with the rank of the compute node, contributing, by that compute node, the compute node's contribution data, if the current position in the result buffer does not correspond with the rank of the compute node, contributing, by that compute node, a value of zero for the contribution data, and storing, by the logical root in the current position in the result buffer, results of a bitwise OR operation of all the contribution data by all compute nodes of the operational group for the current position, the results received through the global combining network.
Numerical computation on massively parallel hypercubes. [Connection machine
McBryan, O.A.
1986-01-01
We describe numerical computations on the Connection Machine, a massively parallel hypercube architecture with 65,536 single-bit processors and 32 Mbytes of memory. A parallel extension of COMMON LISP, provides access to the processors and network. The rich software environment is further enhanced by a powerful virtual processor capability, which extends the degree of fine-grained parallelism beyond 1,000,000. We briefly describe the hardware and indicate the principal features of the parallel programming environment. We then present implementations of SOR, multigrid and pre-conditioned conjugate gradient algorithms for solving partial differential equations on the Connection Machine. Despite the lack of floating point hardware, computation rates above 100 megaflops have been achieved in PDE solution. Virtual processors prove to be a real advantage, easing the effort of software development while improving system performance significantly. The software development effort is also facilitated by the fact that hypercube communications prove to be fast and essentially independent of distance. 29 refs., 4 figs.
A Parallel Computational Model for Multichannel Phase Unwrapping Problem
NASA Astrophysics Data System (ADS)
Imperatore, Pasquale; Pepe, Antonio; Lanari, Riccardo
2015-05-01
In this paper, a parallel model for the solution of the computationally intensive multichannel phase unwrapping (MCh-PhU) problem is proposed. Firstly, the Extended Minimum Cost Flow (EMCF) algorithm for solving MCh-PhU problem is revised within the rigorous mathematical framework of the discrete calculus ; thus permitting to capture its topological structure in terms of meaningful discrete differential operators. Secondly, emphasis is placed on those methodological and practical aspects, which lead to a parallel reformulation of the EMCF algorithm. Thus, a novel dual-level parallel computational model, in which the parallelism is hierarchically implemented at two different (i.e., process and thread) levels, is presented. The validity of our approach has been demonstrated through a series of experiments that have revealed a significant speedup. Therefore, the attained high-performance prototype is suitable for the solution of large-scale phase unwrapping problems in reasonable time frames, with a significant impact on the systematic exploitation of the existing, and rapidly growing, large archives of SAR data.
77 FR 12823 - Advanced Scientific Computing Advisory Committee
Federal Register 2010, 2011, 2012, 2013, 2014
2012-03-02
... final report, Advanced Networking update Status from Computer Science COV Early Career technical talks Summary of Applied Math and Computer Science Workshops ASCR's new SBIR awards Data-intensive Science... Advanced Scientific Computing Advisory Committee AGENCY: Department of Energy, Office of Science. ACTION...
Bassetti, F.; Davis, K.; Quinlan, D.
1998-12-01
High-performance scientific computing relies increasingly on high-level large-scale object-oriented software frameworks to manage both algorithmic complexity and the complexities of parallelism: distributed data management, process management, inter-process communication, and load balancing. This encapsulation of data management, together with the prescribed semantics of a typical fundamental component of such object-oriented frameworks--a parallel or serial array-class library--provides an opportunity for increasingly sophisticated compile-time optimization techniques. This paper describes a technique for introducing cache blocking suitable for certain classes of numerical algorithms, demonstrates and analyzes the resulting performance gains, and indicates how this optimization transformation is being automated.
Bassetti, F.; Davis, K.; Quinlan, D.
1998-12-31
High-performance scientific computing relies increasingly on high-level large-scale object-oriented software frameworks to manage both algorithmic complexity and the complexities of parallelism: distributed data management, process management, inter-process communication, and load balancing. This encapsulation of data management, together with the prescribed semantics of a typical fundamental component of such object-oriented frameworks--a parallel or serial array-class library--provides an opportunity for increasingly sophisticated compile-time optimization techniques. This paper describes two optimizing transformations suitable for certain classes of numerical algorithms, one for reducing the cost of inter-processor communication, and one for improving cache utilization; demonstrates and analyzes the resulting performance gains; and indicates how these transformations are being automated.
Establishing a group of endpoints in a parallel computer
Archer, Charles J.; Blocksome, Michael A.; Ratterman, Joseph D.; Smith, Brian E.; Xue, Hanhong
2016-02-02
A parallel computer executes a number of tasks, each task includes a number of endpoints and the endpoints are configured to support collective operations. In such a parallel computer, establishing a group of endpoints receiving a user specification of a set of endpoints included in a global collection of endpoints, where the user specification defines the set in accordance with a predefined virtual representation of the endpoints, the predefined virtual representation is a data structure setting forth an organization of tasks and endpoints included in the global collection of endpoints and the user specification defines the set of endpoints without a user specification of a particular endpoint; and defining a group of endpoints in dependence upon the predefined virtual representation of the endpoints and the user specification.
Performing a local reduction operation on a parallel computer
Blocksome, Michael A.; Faraj, Daniel A.
2012-12-11
A parallel computer including compute nodes, each including two reduction processing cores, a network write processing core, and a network read processing core, each processing core assigned an input buffer. Copying, in interleaved chunks by the reduction processing cores, contents of the reduction processing cores' input buffers to an interleaved buffer in shared memory; copying, by one of the reduction processing cores, contents of the network write processing core's input buffer to shared memory; copying, by another of the reduction processing cores, contents of the network read processing core's input buffer to shared memory; and locally reducing in parallel by the reduction processing cores: the contents of the reduction processing core's input buffer; every other interleaved chunk of the interleaved buffer; the copied contents of the network write processing core's input buffer; and the copied contents of the network read processing core's input buffer.
Final Report: Center for Programming Models for Scalable Parallel Computing
Mellor-Crummey, John
2011-09-13
As part of the Center for Programming Models for Scalable Parallel Computing, Rice University collaborated with project partners in the design, development and deployment of language, compiler, and runtime support for parallel programming models to support application development for the “leadership-class” computer systems at DOE national laboratories. Work over the course of this project has focused on the design, implementation, and evaluation of a second-generation version of Coarray Fortran. Research and development efforts of the project have focused on the CAF 2.0 language, compiler, runtime system, and supporting infrastructure. This has involved working with the teams that provide infrastructure for CAF that we rely on, implementing new language and runtime features, producing an open source compiler that enabled us to evaluate our ideas, and evaluating our design and implementation through the use of benchmarks. The report details the research, development, findings, and conclusions from this work.
Performing a local reduction operation on a parallel computer
Blocksome, Michael A; Faraj, Daniel A
2013-06-04
A parallel computer including compute nodes, each including two reduction processing cores, a network write processing core, and a network read processing core, each processing core assigned an input buffer. Copying, in interleaved chunks by the reduction processing cores, contents of the reduction processing cores' input buffers to an interleaved buffer in shared memory; copying, by one of the reduction processing cores, contents of the network write processing core's input buffer to shared memory; copying, by another of the reduction processing cores, contents of the network read processing core's input buffer to shared memory; and locally reducing in parallel by the reduction processing cores: the contents of the reduction processing core's input buffer; every other interleaved chunk of the interleaved buffer; the copied contents of the network write processing core's input buffer; and the copied contents of the network read processing core's input buffer.
Probabilistic structural mechanics research for parallel processing computers
NASA Technical Reports Server (NTRS)
Sues, Robert H.; Chen, Heh-Chyun; Twisdale, Lawrence A.; Martin, William R.
1991-01-01
Aerospace structures and spacecraft are a complex assemblage of structural components that are subjected to a variety of complex, cyclic, and transient loading conditions. Significant modeling uncertainties are present in these structures, in addition to the inherent randomness of material properties and loads. To properly account for these uncertainties in evaluating and assessing the reliability of these components and structures, probabilistic structural mechanics (PSM) procedures must be used. Much research has focused on basic theory development and the development of approximate analytic solution methods in random vibrations and structural reliability. Practical application of PSM methods was hampered by their computationally intense nature. Solution of PSM problems requires repeated analyses of structures that are often large, and exhibit nonlinear and/or dynamic response behavior. These methods are all inherently parallel and ideally suited to implementation on parallel processing computers. New hardware architectures and innovative control software and solution methodologies are needed to make solution of large scale PSM problems practical.
Analysis of coupled heat and moisture transport on parallel computers
NASA Astrophysics Data System (ADS)
Koudelka, Tomáš; Krejčí, Tomáš
2017-07-01
Coupled analysis of heat and moisture transport in complicated structural elements or in whole structures deserves a special attention because after space discretization, large number of degrees of freedom are needed. This paper describes possible solution of such problems based on domain decomposition methods executed on parallel computers. The Schur complement method is used with respect to nonsymmetric systems of algebraic equations. The method described is an alternative to other methods, e.g. two or more scale homogenization.
Modeling of supersonic combustor flows using parallel computing
NASA Technical Reports Server (NTRS)
Riggins, D.; Underwood, M.; Mcmillin, B.; Reeves, L.; Lu, E. J.-L.
1992-01-01
While current 3D CFD codes and modeling techniques have been shown capable of furnishing engineering data for complex scramjet flowfields, the usefulness of such efforts is primarily limited by solutions' CPU time requirements, and secondarily by memory requirements. Attention is presently given to the use of parallel computing capabilities for engineering CFD tools for the analysis of supersonic reacting flows, and to an illustrative incompressible CFD problem using up to 16 iPSC/2 processors with single-domain decomposition.
Simulation methods for advanced scientific computing
Booth, T.E.; Carlson, J.A.; Forster, R.A.
1998-11-01
This is the final report of a three-year, Laboratory Directed Research and Development (LDRD) project at the Los Alamos National Laboratory (LANL). The objective of the project was to create effective new algorithms for solving N-body problems by computer simulation. The authors concentrated on developing advanced classical and quantum Monte Carlo techniques. For simulations of phase transitions in classical systems, they produced a framework generalizing the famous Swendsen-Wang cluster algorithms for Ising and Potts models. For spin-glass-like problems, they demonstrated the effectiveness of an extension of the multicanonical method for the two-dimensional, random bond Ising model. For quantum mechanical systems, they generated a new method to compute the ground-state energy of systems of interacting electrons. They also improved methods to compute excited states when the diffusion quantum Monte Carlo method is used and to compute longer time dynamics when the stationary phase quantum Monte Carlo method is used.
Woodward, P. R.
2003-03-26
This report summarizes the results of the project entitled, ''Piecewise-Parabolic Methods for Parallel Computation with Applications to Unstable Fluid Flow in 2 and 3 Dimensions'' This project covers a span of many years, beginning in early 1987. It has provided over that considerable period the core funding to my research activities in scientific computation at the University of Minnesota. It has supported numerical algorithm development, application of those algorithms to fundamental fluid dynamics problems in order to demonstrate their effectiveness, and the development of scientific visualization software and systems to extract scientific understanding from those applications.
Performing an allreduce operation on a plurality of compute nodes of a parallel computer
Faraj, Ahmad [Rochester, MN
2012-04-17
Methods, apparatus, and products are disclosed for performing an allreduce operation on a plurality of compute nodes of a parallel computer. Each compute node includes at least two processing cores. Each processing core has contribution data for the allreduce operation. Performing an allreduce operation on a plurality of compute nodes of a parallel computer includes: establishing one or more logical rings among the compute nodes, each logical ring including at least one processing core from each compute node; performing, for each logical ring, a global allreduce operation using the contribution data for the processing cores included in that logical ring, yielding a global allreduce result for each processing core included in that logical ring; and performing, for each compute node, a local allreduce operation using the global allreduce results for each processing core on that compute node.
Domain decomposition methods for the parallel computation of reacting flows
NASA Technical Reports Server (NTRS)
Keyes, David E.
1988-01-01
Domain decomposition is a natural route to parallel computing for partial differential equation solvers. Subdomains of which the original domain of definition is comprised are assigned to independent processors at the price of periodic coordination between processors to compute global parameters and maintain the requisite degree of continuity of the solution at the subdomain interfaces. In the domain-decomposed solution of steady multidimensional systems of PDEs by finite difference methods using a pseudo-transient version of Newton iteration, the only portion of the computation which generally stands in the way of efficient parallelization is the solution of the large, sparse linear systems arising at each Newton step. For some Jacobian matrices drawn from an actual two-dimensional reacting flow problem, comparisons are made between relaxation-based linear solvers and also preconditioned iterative methods of Conjugate Gradient and Chebyshev type, focusing attention on both iteration count and global inner product count. The generalized minimum residual method with block-ILU preconditioning is judged the best serial method among those considered, and parallel numerical experiments on the Encore Multimax demonstrate for it approximately 10-fold speedup on 16 processors.
Digital image processing using parallel computing based on CUDA technology
NASA Astrophysics Data System (ADS)
Skirnevskiy, I. P.; Pustovit, A. V.; Abdrashitova, M. O.
2017-01-01
This article describes expediency of using a graphics processing unit (GPU) in big data processing in the context of digital images processing. It provides a short description of a parallel computing technology and its usage in different areas, definition of the image noise and a brief overview of some noise removal algorithms. It also describes some basic requirements that should be met by certain noise removal algorithm in the projection to computer tomography. It provides comparison of the performance with and without using GPU as well as with different percentage of using CPU and GPU.
Parallel and vector computation for stochastic optimal control applications
NASA Technical Reports Server (NTRS)
Hanson, F. B.
1989-01-01
A general method for parallel and vector numerical solutions of stochastic dynamic programming problems is described for optimal control of general nonlinear, continuous time, multibody dynamical systems, perturbed by Poisson as well as Gaussian random white noise. Possible applications include lumped flight dynamics models for uncertain environments, such as large scale and background random atmospheric fluctuations. The numerical formulation is highly suitable for a vector multiprocessor or vectorizing supercomputer, and results exhibit high processor efficiency and numerical stability. Advanced computing techniques, data structures, and hardware help alleviate Bellman's curse of dimensionality in dynamic programming computations.
Local rollback for fault-tolerance in parallel computing systems
Blumrich, Matthias A [Yorktown Heights, NY; Chen, Dong [Yorktown Heights, NY; Gara, Alan [Yorktown Heights, NY; Giampapa, Mark E [Yorktown Heights, NY; Heidelberger, Philip [Yorktown Heights, NY; Ohmacht, Martin [Yorktown Heights, NY; Steinmacher-Burow, Burkhard [Boeblingen, DE; Sugavanam, Krishnan [Yorktown Heights, NY
2012-01-24
A control logic device performs a local rollback in a parallel super computing system. The super computing system includes at least one cache memory device. The control logic device determines a local rollback interval. The control logic device runs at least one instruction in the local rollback interval. The control logic device evaluates whether an unrecoverable condition occurs while running the at least one instruction during the local rollback interval. The control logic device checks whether an error occurs during the local rollback. The control logic device restarts the local rollback interval if the error occurs and the unrecoverable condition does not occur during the local rollback interval.
Runtime optimization of an application executing on a parallel computer
Faraj, Daniel A.; Smith, Brian E.
2013-01-29
Identifying a collective operation within an application executing on a parallel computer; identifying a call site of the collective operation; determining whether the collective operation is root-based; if the collective operation is not root-based: establishing a tuning session and executing the collective operation in the tuning session; if the collective operation is root-based, determining whether all compute nodes executing the application identified the collective operation at the same call site; if all compute nodes identified the collective operation at the same call site, establishing a tuning session and executing the collective operation in the tuning session; and if all compute nodes executing the application did not identify the collective operation at the same call site, executing the collective operation without establishing a tuning session.
Runtime optimization of an application executing on a parallel computer
Faraj, Daniel A; Smith, Brian E
2014-11-18
Identifying a collective operation within an application executing on a parallel computer; identifying a call site of the collective operation; determining whether the collective operation is root-based; if the collective operation is not root-based: establishing a tuning session and executing the collective operation in the tuning session; if the collective operation is root-based, determining whether all compute nodes executing the application identified the collective operation at the same call site; if all compute nodes identified the collective operation at the same call site, establishing a tuning session and executing the collective operation in the tuning session; and if all compute nodes executing the application did not identify the collective operation at the same call site, executing the collective operation without establishing a tuning session.
Runtime optimization of an application executing on a parallel computer
Faraj, Daniel A; Smith, Brian E
2014-11-25
Identifying a collective operation within an application executing on a parallel computer; identifying a call site of the collective operation; determining whether the collective operation is root-based; if the collective operation is not root-based: establishing a tuning session and executing the collective operation in the tuning session; if the collective operation is root-based, determining whether all compute nodes executing the application identified the collective operation at the same call site; if all compute nodes identified the collective operation at the same call site, establishing a tuning session and executing the collective operation in the tuning session; and if all compute nodes executing the application did not identify the collective operation at the same call site, executing the collective operation without establishing a tuning session.
National Energy Research Scientific Computing Center 2007 Annual Report
Hules, John A.; Bashor, Jon; Wang, Ucilia; Yarris, Lynn; Preuss, Paul
2008-10-23
This report presents highlights of the research conducted on NERSC computers in a variety of scientific disciplines during the year 2007. It also reports on changes and upgrades to NERSC's systems and services aswell as activities of NERSC staff.
Developing Concept-Based User Interfaces for Scientific Computing
Chin, George; Stephan, Eric G.; Gracio, Deborah K.; Kuchar, Olga A.; Whitney, Paul D.; Schuchardt, Karen L.
2006-09-01
From our interactions with researchers from different scientific fields and disciplines, we have observed that scientists often describe and convey concepts, theories, processes, and results using basic graphs and diagrams. Semantic graphs such as these provide a universal language that all scientists may apply to document their scientific knowledge and to communicate this knowledge to others. Furthermore, studies have shown that the cognitive processing of complex subject matter is improved when the structure of ideas and concepts are made explicit [39] and that semantic graphs may serve as effective “scaffolds” for cognitive processing [29]. At Pacific Northwest National Laboratory, we are deploying semantic graphs within scientific computing systems as central user representations of scientific knowledge. These systems provide concept-based user interfaces that allow scientists to visually define and capture conceptual models of their scientific problems, hypotheses, theories, and processes. Once defined, the visual models then become interaction framework for accessing and applying scientific and computational resources and capabilities. In this paper, through the examination of three visual research systems, we illustrate different ways concept-based user interfaces and semantic graph knowledge representations may make scientific knowledge concrete, usable, shareable, and computable in scientific computing systems.
Basic mathematical function libraries for scientific computation
NASA Technical Reports Server (NTRS)
Galant, David C.
1989-01-01
Ada packages implementing selected mathematical functions for the support of scientific and engineering applications were written. The packages provide the Ada programmer with the mathematical function support found in the languages Pascal and FORTRAN as well as an extended precision arithmetic and a complete complex arithmetic. The algorithms used are fully described and analyzed. Implementation assumes that the Ada type FLOAT objects fully conform to the IEEE 754-1985 standard for single binary floating-point arithmetic, and that INTEGER objects are 32-bit entities. Codes for the Ada packages are included as appendixes.
Scientific development of a massively parallel ocean climate model. Final report
Semtner, A.J.; Chervin, R.M.
1996-09-01
Over the last three years, very significant advances have been made in refining the grid resolution of ocean models and in improving the physical and numerical treatments of ocean hydrodynamics. Some of these advances have occurred as a result of the successful transition of ocean models onto massively parallel computers, which has been led by Los Alamos investigators. Major progress has been made in simulating global ocean circulation and in understanding various ocean climatic aspects such as the effect of wind driving on heat and freshwater transports. These steps have demonstrated the capability to conduct realistic decadal to century ocean integrations at high resolution on massively parallel computers.
Multicore Challenges and Benefits for High Performance Scientific Computing
Nielsen, Ida M. B.; Janssen, Curtis L.
2008-01-01
Until recently, performance gains in processors were achieved largely by improvements in clock speeds and instruction level parallelism. Thus, applications could obtain performance increases with relatively minor changes by upgrading to the latest generation of computing hardware. Currently, however, processor performance improvements are realized by using multicore technology and hardware support for multiple threads within each core, and taking full advantage of this technology to improve the performance of applications requires exposure of extreme levels of software parallelism. We will here discuss the architecture of parallel computers constructed from many multicore chips as well as techniques for managing the complexitymore » of programming such computers, including the hybrid message-passing/multi-threading programming model. We will illustrate these ideas with a hybrid distributed memory matrix multiply and a quantum chemistry algorithm for energy computation using Møller–Plesset perturbation theory.« less
Use Computer-Aided Tools to Parallelize Large CFD Applications
NASA Technical Reports Server (NTRS)
Jin, H.; Frumkin, M.; Yan, J.
2000-01-01
Porting applications to high performance parallel computers is always a challenging task. It is time consuming and costly. With rapid progressing in hardware architectures and increasing complexity of real applications in recent years, the problem becomes even more sever. Today, scalability and high performance are mostly involving handwritten parallel programs using message-passing libraries (e.g. MPI). However, this process is very difficult and often error-prone. The recent reemergence of shared memory parallel (SMP) architectures, such as the cache coherent Non-Uniform Memory Access (ccNUMA) architecture used in the SGI Origin 2000, show good prospects for scaling beyond hundreds of processors. Programming on an SMP is simplified by working in a globally accessible address space. The user can supply compiler directives, such as OpenMP, to parallelize the code. As an industry standard for portable implementation of parallel programs for SMPs, OpenMP is a set of compiler directives and callable runtime library routines that extend Fortran, C and C++ to express shared memory parallelism. It promises an incremental path for parallel conversion of existing software, as well as scalability and performance for a complete rewrite or an entirely new development. Perhaps the main disadvantage of programming with directives is that inserted directives may not necessarily enhance performance. In the worst cases, it can create erroneous results. While vendors have provided tools to perform error-checking and profiling, automation in directive insertion is very limited and often failed on large programs, primarily due to the lack of a thorough enough data dependence analysis. To overcome the deficiency, we have developed a toolkit, CAPO, to automatically insert OpenMP directives in Fortran programs and apply certain degrees of optimization. CAPO is aimed at taking advantage of detailed inter-procedural dependence analysis provided by CAPTools, developed by the University of
Use Computer-Aided Tools to Parallelize Large CFD Applications
NASA Technical Reports Server (NTRS)
Jin, H.; Frumkin, M.; Yan, J.
2000-01-01
Porting applications to high performance parallel computers is always a challenging task. It is time consuming and costly. With rapid progressing in hardware architectures and increasing complexity of real applications in recent years, the problem becomes even more sever. Today, scalability and high performance are mostly involving handwritten parallel programs using message-passing libraries (e.g. MPI). However, this process is very difficult and often error-prone. The recent reemergence of shared memory parallel (SMP) architectures, such as the cache coherent Non-Uniform Memory Access (ccNUMA) architecture used in the SGI Origin 2000, show good prospects for scaling beyond hundreds of processors. Programming on an SMP is simplified by working in a globally accessible address space. The user can supply compiler directives, such as OpenMP, to parallelize the code. As an industry standard for portable implementation of parallel programs for SMPs, OpenMP is a set of compiler directives and callable runtime library routines that extend Fortran, C and C++ to express shared memory parallelism. It promises an incremental path for parallel conversion of existing software, as well as scalability and performance for a complete rewrite or an entirely new development. Perhaps the main disadvantage of programming with directives is that inserted directives may not necessarily enhance performance. In the worst cases, it can create erroneous results. While vendors have provided tools to perform error-checking and profiling, automation in directive insertion is very limited and often failed on large programs, primarily due to the lack of a thorough enough data dependence analysis. To overcome the deficiency, we have developed a toolkit, CAPO, to automatically insert OpenMP directives in Fortran programs and apply certain degrees of optimization. CAPO is aimed at taking advantage of detailed inter-procedural dependence analysis provided by CAPTools, developed by the University of
Data communications in a parallel active messaging interface of a parallel computer
Blocksome, Michael A.; Ratterman, Joseph D.; Smith, Brian E.
2014-09-16
Eager send data communications in a parallel active messaging interface (`PAMI`) of a parallel computer, the PAMI composed of data communications endpoints that specify a client, a context, and a task, including receiving an eager send data communications instruction with transfer data disposed in a send buffer characterized by a read/write send buffer memory address in a read/write virtual address space of the origin endpoint; determining for the send buffer a read-only send buffer memory address in a read-only virtual address space, the read-only virtual address space shared by both the origin endpoint and the target endpoint, with all frames of physical memory mapped to pages of virtual memory in the read-only virtual address space; and communicating by the origin endpoint to the target endpoint an eager send message header that includes the read-only send buffer memory address.
Data communications in a parallel active messaging interface of a parallel computer
Blocksome, Michael A.; Ratterman, Joseph D.; Smith, Brian E.
2014-09-02
Eager send data communications in a parallel active messaging interface (`PAMI`) of a parallel computer, the PAMI composed of data communications endpoints that specify a client, a context, and a task, including receiving an eager send data communications instruction with transfer data disposed in a send buffer characterized by a read/write send buffer memory address in a read/write virtual address space of the origin endpoint; determining for the send buffer a read-only send buffer memory address in a read-only virtual address space, the read-only virtual address space shared by both the origin endpoint and the target endpoint, with all frames of physical memory mapped to pages of virtual memory in the read-only virtual address space; and communicating by the origin endpoint to the target endpoint an eager send message header that includes the read-only send buffer memory address.
Blocksome, Michael A.; Mamidala, Amith R.
2013-09-03
Fencing direct memory access (`DMA`) data transfers in a parallel active messaging interface (`PAMI`) of a parallel computer, the PAMI including data communications endpoints, each endpoint including specifications of a client, a context, and a task, the endpoints coupled for data communications through the PAMI and through DMA controllers operatively coupled to segments of shared random access memory through which the DMA controllers deliver data communications deterministically, including initiating execution through the PAMI of an ordered sequence of active DMA instructions for DMA data transfers between two endpoints, effecting deterministic DMA data transfers through a DMA controller and a segment of shared memory; and executing through the PAMI, with no FENCE accounting for DMA data transfers, an active FENCE instruction, the FENCE instruction completing execution only after completion of all DMA instructions initiated prior to execution of the FENCE instruction for DMA data transfers between the two endpoints.
Blocksome, Michael A; Mamidala, Amith R
2014-02-11
Fencing direct memory access (`DMA`) data transfers in a parallel active messaging interface (`PAMI`) of a parallel computer, the PAMI including data communications endpoints, each endpoint including specifications of a client, a context, and a task, the endpoints coupled for data communications through the PAMI and through DMA controllers operatively coupled to segments of shared random access memory through which the DMA controllers deliver data communications deterministically, including initiating execution through the PAMI of an ordered sequence of active DMA instructions for DMA data transfers between two endpoints, effecting deterministic DMA data transfers through a DMA controller and a segment of shared memory; and executing through the PAMI, with no FENCE accounting for DMA data transfers, an active FENCE instruction, the FENCE instruction completing execution only after completion of all DMA instructions initiated prior to execution of the FENCE instruction for DMA data transfers between the two endpoints.
Faraj, Daniel A.
2015-11-19
Algorithm selection for data communications in a parallel active messaging interface (`PAMI`) of a parallel computer, the PAMI composed of data communications endpoints, each endpoint including specifications of a client, a context, and a task, endpoints coupled for data communications through the PAMI, including associating in the PAMI data communications algorithms and bit masks; receiving in an origin endpoint of the PAMI a collective instruction, the instruction specifying transmission of a data communications message from the origin endpoint to a target endpoint; constructing a bit mask for the received collective instruction; selecting, from among the associated algorithms and bit masks, a data communications algorithm in dependence upon the constructed bit mask; and executing the collective instruction, transmitting, according to the selected data communications algorithm from the origin endpoint to the target endpoint, the data communications message.
Data communications in a parallel active messaging interface of a parallel computer
Davis, Kristan D.; Faraj, Daniel A.
2014-07-22
Algorithm selection for data communications in a parallel active messaging interface (`PAMI`) of a parallel computer, the PAMI composed of data communications endpoints, each endpoint including specifications of a client, a context, and a task, endpoints coupled for data communications through the PAMI, including associating in the PAMI data communications algorithms and ranges of message sizes so that each algorithm is associated with a separate range of message sizes; receiving in an origin endpoint of the PAMI a data communications instruction, the instruction specifying transmission of a data communications message from the origin endpoint to a target endpoint, the data communications message characterized by a message size; selecting, from among the associated algorithms and ranges, a data communications algorithm in dependence upon the message size; and transmitting, according to the selected data communications algorithm from the origin endpoint to the target endpoint, the data communications message.
Blocksome, Michael A.; Mamidala, Amith R.
2013-09-03
Fencing direct memory access (`DMA`) data transfers in a parallel active messaging interface (`PAMI`) of a parallel computer, the PAMI including data communications endpoints, each endpoint including specifications of a client, a context, and a task, the endpoints coupled for data communications through the PAMI and through DMA controllers operatively coupled to segments of shared random access memory through which the DMA controllers deliver data communications deterministically, including initiating execution through the PAMI of an ordered sequence of active DMA instructions for DMA data transfers between two endpoints, effecting deterministic DMA data transfers through a DMA controller and a segment of shared memory; and executing through the PAMI, with no FENCE accounting for DMA data transfers, an active FENCE instruction, the FENCE instruction completing execution only after completion of all DMA instructions initiated prior to execution of the FENCE instruction for DMA data transfers between the two endpoints.
Data communications in a parallel active messaging interface of a parallel computer
Davis, Kristan D; Faraj, Daniel A
2013-07-09
Algorithm selection for data communications in a parallel active messaging interface (`PAMI`) of a parallel computer, the PAMI composed of data communications endpoints, each endpoint including specifications of a client, a context, and a task, endpoints coupled for data communications through the PAMI, including associating in the PAMI data communications algorithms and ranges of message sizes so that each algorithm is associated with a separate range of message sizes; receiving in an origin endpoint of the PAMI a data communications instruction, the instruction specifying transmission of a data communications message from the origin endpoint to a target endpoint, the data communications message characterized by a message size; selecting, from among the associated algorithms and ranges, a data communications algorithm in dependence upon the message size; and transmitting, according to the selected data communications algorithm from the origin endpoint to the target endpoint, the data communications message.
Faraj, Daniel A
2013-07-16
Algorithm selection for data communications in a parallel active messaging interface (`PAMI`) of a parallel computer, the PAMI composed of data communications endpoints, each endpoint including specifications of a client, a context, and a task, endpoints coupled for data communications through the PAMI, including associating in the PAMI data communications algorithms and bit masks; receiving in an origin endpoint of the PAMI a collective instruction, the instruction specifying transmission of a data communications message from the origin endpoint to a target endpoint; constructing a bit mask for the received collective instruction; selecting, from among the associated algorithms and bit masks, a data communications algorithm in dependence upon the constructed bit mask; and executing the collective instruction, transmitting, according to the selected data communications algorithm from the origin endpoint to the target endpoint, the data communications message.
Event parallelism: Distributed memory parallel computing for high energy physics experiments
Nash, T.
1989-05-01
This paper describes the present and expected future development of distributed memory parallel computers for high energy physics experiments. It covers the use of event parallel microprocessor farms, particularly at Fermilab, including both ACP multiprocessors and farms of MicroVAXES. These systems have proven very cost effective in the past. A case is made for moving to the more open environment of UNIX and RISC processors. The 2nd Generation ACP Multiprocessor System, which is based on powerful RISC systems, is described. Given the promise of still more extraordinary increases in processor performance, a new emphasis on point to point, rather than bussed, communication will be required. Developments in this direction are described. 6 figs.
Performance of parallel computation using CUDA for solving the one-dimensional elasticity equations
NASA Astrophysics Data System (ADS)
Darmawan, J. B. B.; Mungkasi, S.
2017-01-01
In this paper, we investigate the performance of parallel computation in solving the one-dimensional elasticity equations. Elasticity equations are usually implemented in engineering science. Solving these equations fast and efficiently is desired. Therefore, we propose the use of parallel computation. Our parallel computation uses CUDA of the NVIDIA. Our research results show that parallel computation using CUDA has a great advantage and is powerful when the computation is of large scale.
Scientific computations section monthly report, November 1993
Buckner, M.R.
1993-12-30
This progress report from the Savannah River Technology Center contains abstracts from papers from the computational modeling, applied statistics, applied physics, experimental thermal hydraulics, and packaging and transportation groups. Specific topics covered include: engineering modeling and process simulation, criticality methods and analysis, plutonium disposition.
PS3 CELL Development for Scientific Computation and Research
NASA Astrophysics Data System (ADS)
Christiansen, M.; Sevre, E.; Wang, S. M.; Yuen, D. A.; Liu, S.; Lyness, M. D.; Broten, M.
2007-12-01
The Cell processor is one of the most powerful processors on the market, and researchers in the earth sciences may find its parallel architecture to be very useful. A cell processor, with 7 cores, can easily be obtained for experimentation by purchasing a PlayStation 3 (PS3) and installing linux and the IBM SDK. Each core of the PS3 is capable of 25 GFLOPS giving a potential limit of 150 GFLOPS when using all 6 SPUs (synergistic processing units) by using vectorized algorithms. We have used the Cell's computational power to create a program which takes simulated tsunami datasets, parses them, and returns a colorized height field image using ray casting techniques. As expected, the time required to create an image is inversely proportional to the number of SPUs used. We believe that this trend will continue when multiple PS3s are chained using OpenMP functionality and are in the process of researching this. By using the Cell to visualize tsunami data, we have found that its greatest feature is its power. This fact entwines well with the needs of the scientific community where the limiting factor is time. Any algorithm, such as the heat equation, that can be subdivided into multiple parts can take advantage of the PS3 Cell's ability to split the computations across the 6 SPUs reducing required run time by one sixth. Further vectorization of the code can allow for 4 simultanious floating point operations by using the SIMD (single instruction multiple data) capabilities of the SPU increasing efficiency 24 times.
Semi-coarsening multigrid methods for parallel computing
Jones, J.E.
1996-12-31
Standard multigrid methods are not well suited for problems with anisotropic coefficients which can occur, for example, on grids that are stretched to resolve a boundary layer. There are several different modifications of the standard multigrid algorithm that yield efficient methods for anisotropic problems. In the paper, we investigate the parallel performance of these multigrid algorithms. Multigrid algorithms which work well for anisotropic problems are based on line relaxation and/or semi-coarsening. In semi-coarsening multigrid algorithms a grid is coarsened in only one of the coordinate directions unlike standard or full-coarsening multigrid algorithms where a grid is coarsened in each of the coordinate directions. When both semi-coarsening and line relaxation are used, the resulting multigrid algorithm is robust and automatic in that it requires no knowledge of the nature of the anisotropy. This is the basic multigrid algorithm whose parallel performance we investigate in the paper. The algorithm is currently being implemented on an IBM SP2 and its performance is being analyzed. In addition to looking at the parallel performance of the basic semi-coarsening algorithm, we present algorithmic modifications with potentially better parallel efficiency. One modification reduces the amount of computational work done in relaxation at the expense of using multiple coarse grids. This modification is also being implemented with the aim of comparing its performance to that of the basic semi-coarsening algorithm.
A Framework to Simulate Semiconductor Devices Using Parallel Computer Architecture
NASA Astrophysics Data System (ADS)
Kumar, Gaurav; Singh, Mandeep; Bulusu, Anand; Trivedi, Gaurav
2016-10-01
Device simulations have become an integral part of semiconductor technology to address many issues (short channel effects, narrow width effects, hot-electron effect) as it goes into nano regime, helping us to continue further with the Moore's Law. TCAD provides a simulation environment to design and develop novel devices, thus a leap forward to study their electrical behaviour in advance. In this paper, a parallel 2D simulator for semiconductor devices using Discontinuous Galerkin Finite Element Method (DG-FEM) is presented. Discontinuous Galerkin (DG) method is used to discretize essential device equations and later these equations are analyzed by using a suitable methodology to find the solution. DG method is characterized to provide more accurate solution as it efficiently conserve the flux and easily handles complex geometries. OpenMP is used to parallelize solution of device equations on manycore processors and a speed of 1.4x is achieved during assembly process of discretization. This study is important for more accurate analysis of novel devices (such as FinFET, GAAFET etc.) on a parallel computing platform and will help us to develop a parallel device simulator which will be able to address this issue efficiently. A case study of PN junction diode is presented to show the effectiveness of proposed approach.
Performance of parallel computers for spectral atmospheric models
Foster, I.T.; Toonen, B.; Worley, P.H.
1995-06-01
Massively parallel processing (MPP) computer systems use high-speed interconnection networks to link hundreds or thousands of RISC microprocessors. With each microprocessor having a peak performance of 100 Mflops/sec or more, there is at least the possibility of achieving very high performance. However, the question of exactly how to achieve this performance remains unanswered. MPP systems and vector multiprocessors require very different coding styles. Different MPP systems have widely varying architectures and performance characteristics. For most problems, a range of different parallel algorithms is possible, again with varying performance characteristics. In this paper, we provide a detailed, fair evaluation of MPP performance for a weather and climate modeling application. Using a specially designed spectral transform code, we study performance on three different MPP systems: Intel Paragon, IBM SP2, and Cray T3D. We take great care to control for performance differences due to varying algorithmic characteristics. The results yield insights into MPP performance characteristics, parallel spectral transform algorithms, and coding style for MPP systems. We conclude that it is possible to construct parallel models that achieve multi-Gflop/sec performance on a range of MPPs if the models are constructed to allow run-time selection of appropriate algorithms.
Parallelization of Nullspace Algorithm for the computation of metabolic pathways.
Jevremović, Dimitrije; Trinh, Cong T; Srienc, Friedrich; Sosa, Carlos P; Boley, Daniel
2011-06-01
Elementary mode analysis is a useful metabolic pathway analysis tool in understanding and analyzing cellular metabolism, since elementary modes can represent metabolic pathways with unique and minimal sets of enzyme-catalyzed reactions of a metabolic network under steady state conditions. However, computation of the elementary modes of a genome- scale metabolic network with 100-1000 reactions is very expensive and sometimes not feasible with the commonly used serial Nullspace Algorithm. In this work, we develop a distributed memory parallelization of the Nullspace Algorithm to handle efficiently the computation of the elementary modes of a large metabolic network. We give an implementation in C++ language with the support of MPI library functions for the parallel communication. Our proposed algorithm is accompanied with an analysis of the complexity and identification of major bottlenecks during computation of all possible pathways of a large metabolic network. The algorithm includes methods to achieve load balancing among the compute-nodes and specific communication patterns to reduce the communication overhead and improve efficiency.
Massively parallel finite element computation of three dimensional flow problems
NASA Astrophysics Data System (ADS)
Tezduyar, T.; Aliabadi, S.; Behr, M.; Johnson, A.; Mittal, S.
1992-12-01
The parallel finite element computation of three-dimensional compressible, and incompressible flows, with emphasis on the space-time formulations, mesh moving schemes and implementations on the Connection Machines CM-200 and CM-5 are presented. For computation of unsteady compressible and incompressible flows involving moving boundaries and interfaces, the Deformable-Spatial-Domain/Stabilized-Space-Time (DSD/SST) formulation that previously developed are employed. In this approach, the stabilized finite element formulations of the governing equations are written over the space-time domain of the problem; therefore, the deformation of the spatial domain with respect to time is taken into account automatically. This approach gives the capability to solve a large class of problems involving free surfaces, moving interfaces, and fluid-structure and fluid-particle interactions. By using special mesh moving schemes, the frequency of remeshing is minimized to reduce the projection errors involved in remeshing and also to increase the parallelization ease of the computations. The implicit equation systems arising from the finite element discretizations are solved iteratively by using the GMRES update technique with the diagonal and nodal-block-diagonal preconditioners. These formulations have all been implemented on the CM-200 and CM-5, and have been applied to several large-scale problems. The three-dimensional problems in this report were all computed on the CM-200 and CM-5.
Parallel matrix transpose algorithms on distributed memory concurrent computers
Choi, J.; Walker, D.W.; Dongarra, J.J. |
1993-10-01
This paper describes parallel matrix transpose algorithms on distributed memory concurrent processors. It is assumed that the matrix is distributed over a P x Q processor template with a block scattered data distribution. P, Q, and the block size can be arbitrary, so the algorithms have wide applicability. The communication schemes of the algorithms are determined by the greatest common divisor (GCD) of P and Q. If P and Q are relatively prime, the matrix transpose algorithm involves complete exchange communication. If P and Q are not relatively prime, processors are divided into GCD groups and the communication operations are overlapped for different groups of processors. Processors transpose GCD wrapped diagonal blocks simultaneously, and the matrix can be transposed with LCM/GCD steps, where LCM is the least common multiple of P and Q. The algorithms make use of non-blocking, point-to-point communication between processors. The use of nonblocking communication allows a processor to overlap the messages that it sends to different processors, thereby avoiding unnecessary synchronization. Combined with the matrix multiplication routine, C = A{center_dot}B, the algorithms are used to compute parallel multiplications of transposed matrices, C = A{sup T}{center_dot}B{sup T}, in the PUMMA package. Details of the parallel implementation of the algorithms are given, and results are presented for runs on the Intel Touchstone Delta computer.
Fast electrostatic force calculation on parallel computer clusters
Kia, Amirali Kim, Daejoong Darve, Eric
2008-10-01
The fast multipole method (FMM) and smooth particle mesh Ewald (SPME) are well known fast algorithms to evaluate long range electrostatic interactions in molecular dynamics and other fields. FMM is a multi-scale method which reduces the computation cost by approximating the potential due to a group of particles at a large distance using few multipole functions. This algorithm scales like O(N) for N particles. SPME algorithm is an O(NlnN) method which is based on an interpolation of the Fourier space part of the Ewald sum and evaluating the resulting convolutions using fast Fourier transform (FFT). Those algorithms suffer from relatively poor efficiency on large parallel machines especially for mid-size problems around hundreds of thousands of atoms. A variation of the FMM, called PWA, based on plane wave expansions is presented in this paper. A new parallelization strategy for PWA, which takes advantage of the specific form of this expansion, is described. Its parallel efficiency is compared with SPME through detail time measurements on two different computer clusters.
Nash, T.; Areti, H.; Atac, R.; Biel, J.; Cook, A.; Deppe, J.; Edel, M.; Fischler, M.; Gaines, I.; Hance, R.
1988-08-01
Fermilab's Advanced Computer Program (ACP) has been developing highly cost effective, yet practical, parallel computers for high energy physics since 1984. The ACP's latest developments are proceeding in two directions. A Second Generation ACP Multiprocessor System for experiments will include $3500 RISC processors each with performance over 15 VAX MIPS. To support such high performance, the new system allows parallel I/O, parallel interprocess communication, and parallel host processes. The ACP Multi-Array Processor, has been developed for theoretical physics. Each $4000 node is a FORTRAN or C programmable pipelined 20 MFlops (peak), 10 MByte single board computer. These are plugged into a 16 port crossbar switch crate which handles both inter and intra crate communication. The crates are connected in a hypercube. Site oriented applications like lattice gauge theory are supported by system software called CANOPY, which makes the hardware virtually transparent to users. A 256 node, 5 GFlop, system is under construction. 10 refs., 7 figs.
Determining collective barrier operation skew in a parallel computer
Faraj, Daniel A.
2015-11-24
Determining collective barrier operation skew in a parallel computer that includes a number of compute nodes organized into an operational group includes: for each of the nodes until each node has been selected as a delayed node: selecting one of the nodes as a delayed node; entering, by each node other than the delayed node, a collective barrier operation; entering, after a delay by the delayed node, the collective barrier operation; receiving an exit signal from a root of the collective barrier operation; and measuring, for the delayed node, a barrier completion time. The barrier operation skew is calculated by: identifying, from the compute nodes' barrier completion times, a maximum barrier completion time and a minimum barrier completion time and calculating the barrier operation skew as the difference of the maximum and the minimum barrier completion time.
DMA shared byte counters in a parallel computer
Chen, Dong; Gara, Alan G.; Heidelberger, Philip; Vranas, Pavlos
2010-04-06
A parallel computer system is constructed as a network of interconnected compute nodes. Each of the compute nodes includes at least one processor, a memory and a DMA engine. The DMA engine includes a processor interface for interfacing with the at least one processor, DMA logic, a memory interface for interfacing with the memory, a DMA network interface for interfacing with the network, injection and reception byte counters, injection and reception FIFO metadata, and status registers and control registers. The injection FIFOs maintain memory locations of the injection FIFO metadata memory locations including its current head and tail, and the reception FIFOs maintain the reception FIFO metadata memory locations including its current head and tail. The injection byte counters and reception byte counters may be shared between messages.
Parallelized Vlasov-Fokker-Planck solver for desktop personal computers
NASA Astrophysics Data System (ADS)
Schönfeldt, Patrik; Brosi, Miriam; Schwarz, Markus; Steinmann, Johannes L.; Müller, Anke-Susanne
2017-03-01
The numerical solution of the Vlasov-Fokker-Planck equation is a well established method to simulate the dynamics, including the self-interaction with its own wake field, of an electron bunch in a storage ring. In this paper we present Inovesa, a modularly extensible program that uses opencl to massively parallelize the computation. It allows a standard desktop PC to work with appropriate accuracy and yield reliable results within minutes. We provide numerical stability-studies over a wide parameter range and compare our numerical findings to known results. Simulation results for the case of coherent synchrotron radiation will be compared to measurements that probe the effects of the microbunching instability occurring in the short bunch operation at ANKA. It will be shown that the impedance model based on the shielding effect of two parallel plates can not only describe the instability threshold, but also the presence of multiple regimes that show differences in the emission of coherent synchrotron radiation.
Parallel computation of automatic differentiation applied to magnetic field calculations
Hinkins, R.L. |
1994-09-01
The author presents a parallelization of an accelerator physics application to simulate magnetic field in three dimensions. The problem involves the evaluation of high order derivatives with respect to two variables of a multivariate function. Automatic differentiation software had been used with some success, but the computation time was prohibitive. The implementation runs on several platforms, including a network of workstations using PVM, a MasPar using MPFortran, and a CM-5 using CMFortran. A careful examination of the code led to several optimizations that improved its serial performance by a factor of 8.7. The parallelization produced further improvements, especially on the MasPar with a speedup factor of 620. As a result a problem that took six days on a SPARC 10/41 now runs in minutes on the MasPar, making it feasible for physicists at Lawrence Berkeley Laboratory to simulate larger magnets.
3D finite-difference seismic migration with parallel computers
Ober, C.C.; Gjertsen, R.; Minkoff, S.; Womble, D.E.
1998-11-01
The ability to image complex geologies such as salt domes in the Gulf of Mexico and thrusts in mountainous regions is essential for reducing the risk associated with oil exploration. Imaging these structures, however, is computationally expensive as datasets can be terabytes in size. Traditional ray-tracing migration methods cannot handle complex velocity variations commonly found near such salt structures. Instead the authors use the full 3D acoustic wave equation, discretized via a finite difference algorithm. They reduce the cost of solving the apraxial wave equation by a number of numerical techniques including the method of fractional steps and pipelining the tridiagonal solves. The imaging code, Salvo, uses both frequency parallelism (generally 90% efficient) and spatial parallelism (65% efficient). Salvo has been tested on synthetic and real data and produces clear images of the subsurface even beneath complicated salt structures.
Parallel computation of transverse wakes in linear colliders
Zhan, Xiaowei; Ko, Kwok
1996-11-01
SLAC has proposed the detuned structure (DS) as one possible design to control the emittance growth of long bunch trains due to transverse wakefields in the Next Linear Collider (NLC). The DS consists of 206 cells with tapering from cell to cell of the order of few microns to provide Gaussian detuning of the dipole modes. The decoherence of these modes leads to two orders of magnitude reduction in wakefield experienced by the trailing bunch. To model such a large heterogeneous structure realistically is impractical with finite-difference codes using structured grids. The authors have calculated the wakefield in the DS on a parallel computer with a finite-element code using an unstructured grid. The parallel implementation issues are presented along with simulation results that include contributions from higher dipole bands and wall dissipation.
Center for Programming Models for Scalable Parallel Computing
John Mellor-Crummey
2008-02-29
Rice University's achievements as part of the Center for Programming Models for Scalable Parallel Computing include: (1) design and implemention of cafc, the first multi-platform CAF compiler for distributed and shared-memory machines, (2) performance studies of the efficiency of programs written using the CAF and UPC programming models, (3) a novel technique to analyze explicitly-parallel SPMD programs that facilitates optimization, (4) design, implementation, and evaluation of new language features for CAF, including communication topologies, multi-version variables, and distributed multithreading to simplify development of high-performance codes in CAF, and (5) a synchronization strength reduction transformation for automatically replacing barrier-based synchronization with more efficient point-to-point synchronization. The prototype Co-array Fortran compiler cafc developed in this project is available as open source software from http://www.hipersoft.rice.edu/caf.
Planet crossing asteroids and parallel computing: Project spaceguard
NASA Astrophysics Data System (ADS)
Milani, Andrea
1988-03-01
The orbits of the asteroids crossing the orbit of the Earth and other planets are chaotic and cannot be computed in a deterministic way for a time span long enough to study the probability of collisions. It is possible to study the statistical behaviour of a large number of such orbits over a long span of time, provided enough computing resources and intelligent post processing software are available. The former, problem can be handled by exploiting the enormous power of parallel computing systems. The orbit of the asteroids can be studied as a restricted (N+M)-body problem which is suitable for the use of parallel processing, by using one processor to compute the orbits of the planets and the others to compute the orbits the asteroids. This scheme has been implemented on LCAP-2, an array of IBM and FPS processors with shared memory designed by E. Clementi (IBM). The parallelisation efficiency has been over 80%, and the overall speed over 90 MegaFLOPS; the orbits of all the asteroids with perihelia lower than the aphelion of Mars (410 objects) have been computed for 200,000, years (Project SPACEGUARD). The most difficult step of the project is the post processing of the very large amount of output data and to gather qualitative information on the behaviour of so many orbits without resorting to the traditional technique, i.e. human examination of output in graphical form. Within Project SPACEGUARD we have developed a qualitative classification of the orbits of the planet crossers. To develop an entirely automated classification of the qualitative orbital behaviour-including crossing behaviour, resonances (mean motion and secular), and protection mechanisms avoiding collisions-is a challenge to be met.
Scalable, massively parallel approaches to upstream drainage area computation
NASA Astrophysics Data System (ADS)
Richardson, A.; Hill, C. N.; Perron, T.
2011-12-01
Accumulated drainage area maps of large regions are required for several applications. Among these are assessments of regional patterns of flow and sediment routing, high-resolution landscape evolution models in which drainage basin geometry evolves with time, and surveys of the characteristics of river basins that drain to continental margins. The computation of accumulated drainage areas is accomplished by inferring the vector field of drainage flow directions from a two-dimensional digital elevation map, and then computing the area that drains to each tile. From this map of elevations we can compute the integrated, upstream area that drains to each tile of the map. Generally this last step is done with a recursive algorithm, that accumulates upstream areas sequentially. The inherently serial nature of this restricts the number of tiles that can be included, thereby limiting the resolution of continental-size domains. This is because of the requirements of both memory, which will rise proportionally to the number of tiles, N, and computing time, which is O(N2). The fundamental sequential property of this approach prohibits effective use of large scale parallelism. An alternate method of calculating accumulated drainage area from drainage direction data can be arrived at by reformulating the problem as the solution of a system of simultaneous linear equations. The equations define the relation that the total upslope area of a particular tile is the sum of all the upslope areas for tiles immediately adjacent to that tile that drain to it, and the tile's own area. Solving these equations amounts to finding the solution of a sparse, nine-diagonal matrix operating on a vector for a right-hand-side that is simply the individual tile areas and where the diagonals of the matrix are determined by the landscape geometry. We show how an iterative method, Bi-CGSTAB, can be used to solve this problem in a scalable, massively parallel manner. However, this introduces
Parallel computation of multigroup reactivity coefficient using iterative method
NASA Astrophysics Data System (ADS)
Susmikanti, Mike; Dewayatna, Winter
2013-09-01
One of the research activities to support the commercial radioisotope production program is a safety research target irradiation FPM (Fission Product Molybdenum). FPM targets form a tube made of stainless steel in which the nuclear degrees of superimposed high-enriched uranium. FPM irradiation tube is intended to obtain fission. The fission material widely used in the form of kits in the world of nuclear medicine. Irradiation FPM tube reactor core would interfere with performance. One of the disorders comes from changes in flux or reactivity. It is necessary to study a method for calculating safety terrace ongoing configuration changes during the life of the reactor, making the code faster became an absolute necessity. Neutron safety margin for the research reactor can be reused without modification to the calculation of the reactivity of the reactor, so that is an advantage of using perturbation method. The criticality and flux in multigroup diffusion model was calculate at various irradiation positions in some uranium content. This model has a complex computation. Several parallel algorithms with iterative method have been developed for the sparse and big matrix solution. The Black-Red Gauss Seidel Iteration and the power iteration parallel method can be used to solve multigroup diffusion equation system and calculated the criticality and reactivity coeficient. This research was developed code for reactivity calculation which used one of safety analysis with parallel processing. It can be done more quickly and efficiently by utilizing the parallel processing in the multicore computer. This code was applied for the safety limits calculation of irradiated targets FPM with increment Uranium.
Parallel computation of multigroup reactivity coefficient using iterative method
Susmikanti, Mike; Dewayatna, Winter
2013-09-09
One of the research activities to support the commercial radioisotope production program is a safety research target irradiation FPM (Fission Product Molybdenum). FPM targets form a tube made of stainless steel in which the nuclear degrees of superimposed high-enriched uranium. FPM irradiation tube is intended to obtain fission. The fission material widely used in the form of kits in the world of nuclear medicine. Irradiation FPM tube reactor core would interfere with performance. One of the disorders comes from changes in flux or reactivity. It is necessary to study a method for calculating safety terrace ongoing configuration changes during the life of the reactor, making the code faster became an absolute necessity. Neutron safety margin for the research reactor can be reused without modification to the calculation of the reactivity of the reactor, so that is an advantage of using perturbation method. The criticality and flux in multigroup diffusion model was calculate at various irradiation positions in some uranium content. This model has a complex computation. Several parallel algorithms with iterative method have been developed for the sparse and big matrix solution. The Black-Red Gauss Seidel Iteration and the power iteration parallel method can be used to solve multigroup diffusion equation system and calculated the criticality and reactivity coeficient. This research was developed code for reactivity calculation which used one of safety analysis with parallel processing. It can be done more quickly and efficiently by utilizing the parallel processing in the multicore computer. This code was applied for the safety limits calculation of irradiated targets FPM with increment Uranium.
Building Cognition: The Construction of Computational Representations for Scientific Discovery
ERIC Educational Resources Information Center
Chandrasekharan, Sanjay; Nersessian, Nancy J.
2015-01-01
Novel computational representations, such as simulation models of complex systems and video games for scientific discovery (Foldit, EteRNA etc.), are dramatically changing the way discoveries emerge in science and engineering. The cognitive roles played by such computational representations in discovery are not well understood. We present a…
Building Cognition: The Construction of Computational Representations for Scientific Discovery
ERIC Educational Resources Information Center
Chandrasekharan, Sanjay; Nersessian, Nancy J.
2015-01-01
Novel computational representations, such as simulation models of complex systems and video games for scientific discovery (Foldit, EteRNA etc.), are dramatically changing the way discoveries emerge in science and engineering. The cognitive roles played by such computational representations in discovery are not well understood. We present a…
High performance computing in chemistry and massively parallel computers: A simple transition?
Kendall, R.A.
1993-03-01
A review of the various problems facing any software developer targeting massively parallel processing (MPP) systems is presented. Issues specific to computational chemistry application software will be also outlined. Computational chemistry software ported to and designed for the Intel Touchstone Delta Supercomputer will be discussed. Recommendations for future directions will also be made.
Not Available
1993-10-01
The bibliography contains citations concerning the development and performance analysis of parallel architecture in image processing and computing. Cost and performance evaluations of multiple processor systems are described. Applications are described, including supercomputer design, database management, computer communication systems, and robot control. (Contains 250 citations and includes a subject term index and title list.)
Evaluation of Cache-based Superscalar and Cacheless Vector Architectures for Scientific Computations
NASA Technical Reports Server (NTRS)
Oliker, Leonid; Carter, Jonathan; Shalf, John; Skinner, David; Ethier, Stephane; Biswas, Rupak; Djomehri, Jahed; VanderWijngaart, Rob
2003-01-01
The growing gap between sustained and peak performance for scientific applications has become a well-known problem in high performance computing. The recent development of parallel vector systems offers the potential to bridge this gap for a significant number of computational science codes and deliver a substantial increase in computing capabilities. This paper examines the intranode performance of the NEC SX6 vector processor and the cache-based IBM Power3/4 superscalar architectures across a number of key scientific computing areas. First, we present the performance of a microbenchmark suite that examines a full spectrum of low-level machine characteristics. Next, we study the behavior of the NAS Parallel Benchmarks using some simple optimizations. Finally, we evaluate the perfor- mance of several numerical codes from key scientific computing domains. Overall results demonstrate that the SX6 achieves high performance on a large fraction of our application suite and in many cases significantly outperforms the RISC-based architectures. However, certain classes of applications are not easily amenable to vectorization and would likely require extensive reengineering of both algorithm and implementation to utilize the SX6 effectively.
Evaluation of cache-based superscalar and cacheless vector architectures for scientific computations
Oliker, Leonid; Canning, Andrew; Carter, Jonathan; Shalf, John; Skinner, David; Ethier, Stephane; Biswas, Rupak; Djomehri, Jahed; Van der Wijngaart, Rob
2003-05-01
The growing gap between sustained and peak performance for scientific applications is a well-known problem in high end computing. The recent development of parallel vector systems offers the potential to bridge this gap for many computational science codes and deliver a substantial increase in computing capabilities. This paper examines the intranode performance of the NEC SX-6 vector processor and the cache-based IBM Power3/4 superscalar architectures across a number of scientific computing areas. First, we present the performance of a microbenchmark suite that examines low-level machine characteristics. Next, we study the behavior of the NAS Parallel Benchmarks. Finally, we evaluate the performance of several scientific computing codes. Results demonstrate that the SX-6 achieves high performance on a large fraction of our applications and often significantly out performs the cache-based architectures. However, certain applications are not easily amenable to vectorization and would re quire extensive algorithm and implementation reengineering to utilize the SX-6 effectively.
Large-scale parallel genome assembler over cloud computing environment.
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.
An experiment in hurricane track prediction using parallel computing methods
NASA Technical Reports Server (NTRS)
Song, Chang G.; Jwo, Jung-Sing; Lakshmivarahan, S.; Dhall, S. K.; Lewis, John M.; Velden, Christopher S.
1994-01-01
The barotropic model is used to explore the advantages of parallel processing in deterministic forecasting. We apply this model to the track forecasting of hurricane Elena (1985). In this particular application, solutions to systems of elliptic equations are the essence of the computational mechanics. One set of equations is associated with the decomposition of the wind into irrotational and nondivergent components - this determines the initial nondivergent state. Another set is associated with recovery of the streamfunction from the forecasted vorticity. We demonstrate that direct parallel methods based on accelerated block cyclic reduction (BCR) significantly reduce the computational time required to solve the elliptic equations germane to this decomposition and forecast problem. A 72-h track prediction was made using incremental time steps of 16 min on a network of 3000 grid points nominally separated by 100 km. The prediction took 30 sec on the 8-processor Alliant FX/8 computer. This was a speed-up of 3.7 when compared to the one-processor version. The 72-h prediction of Elena's track was made as the storm moved toward Florida's west coast. Approximately 200 km west of Tampa Bay, Elena executed a dramatic recurvature that ultimately changed its course toward the northwest. Although the barotropic track forecast was unable to capture the hurricane's tight cycloidal looping maneuver, the subsequent northwesterly movement was accurately forecasted as was the location and timing of landfall near Mobile Bay.
Hyper-parallel photonic quantum computation with coupled quantum dots
Ren, Bao-Cang; Deng, Fu-Guo
2014-01-01
It is well known that a parallel quantum computer is more powerful than a classical one. So far, there are some important works about the construction of universal quantum logic gates, the key elements in quantum computation. However, they are focused on operating on one degree of freedom (DOF) of quantum systems. Here, we investigate the possibility of achieving scalable hyper-parallel quantum computation based on two DOFs of photon systems. We construct a deterministic hyper-controlled-not (hyper-CNOT) gate operating on both the spatial-mode and the polarization DOFs of a two-photon system simultaneously, by exploiting the giant optical circular birefringence induced by quantum-dot spins in double-sided optical microcavities as a result of cavity quantum electrodynamics (QED). This hyper-CNOT gate is implemented by manipulating the four qubits in the two DOFs of a two-photon system without auxiliary spatial modes or polarization modes. It reduces the operation time and the resources consumed in quantum information processing, and it is more robust against the photonic dissipation noise, compared with the integration of several cascaded CNOT gates in one DOF. PMID:24721781
Introduction to the LaRC central scientific computing complex
NASA Technical Reports Server (NTRS)
Shoosmith, John N.
1993-01-01
The computers and associated equipment that make up the Central Scientific Computing Complex of the Langley Research Center are briefly described. The electronic networks that provide access to the various components of the complex and a number of areas that can be used by Langley and contractors staff for special applications (scientific visualization, image processing, software engineering, and grid generation) are also described. Flight simulation facilities that use the central computers are described. Management of the complex, procedures for its use, and available services and resources are discussed. This document is intended for new users of the complex, for current users who wish to keep appraised of changes, and for visitors who need to understand the role of central scientific computers at Langley.
ASCR Cybersecurity for Scientific Computing Integrity
Piesert, Sean
2015-02-27
The Department of Energy (DOE) has the responsibility to address the energy, environmental, and nuclear security challenges that face our nation. Much of DOE’s enterprise involves distributed, collaborative teams; a signi¬cant fraction involves “open science,” which depends on multi-institutional, often international collaborations that must access or share signi¬cant amounts of information between institutions and over networks around the world. The mission of the Office of Science is the delivery of scienti¬c discoveries and major scienti¬c tools to transform our understanding of nature and to advance the energy, economic, and national security of the United States. The ability of DOE to execute its responsibilities depends critically on its ability to assure the integrity and availability of scienti¬c facilities and computer systems, and of the scienti¬c, engineering, and operational software and data that support its mission.
An efficient parallel algorithm for accelerating computational protein design
Zhou, Yichao; Xu, Wei; Donald, Bruce R.; Zeng, Jianyang
2014-01-01
Motivation: Structure-based computational protein design (SCPR) is an important topic in protein engineering. Under the assumption of a rigid backbone and a finite set of discrete conformations of side-chains, various methods have been proposed to address this problem. A popular method is to combine the dead-end elimination (DEE) and A* tree search algorithms, which provably finds the global minimum energy conformation (GMEC) solution. Results: In this article, we improve the efficiency of computing A* heuristic functions for protein design and propose a variant of A* algorithm in which the search process can be performed on a single GPU in a massively parallel fashion. In addition, we make some efforts to address the memory exceeding problem in A* search. As a result, our enhancements can achieve a significant speedup of the A*-based protein design algorithm by four orders of magnitude on large-scale test data through pre-computation and parallelization, while still maintaining an acceptable memory overhead. We also show that our parallel A* search algorithm could be successfully combined with iMinDEE, a state-of-the-art DEE criterion, for rotamer pruning to further improve SCPR with the consideration of continuous side-chain flexibility. Availability: Our software is available and distributed open-source under the GNU Lesser General License Version 2.1 (GNU, February 1999). The source code can be downloaded from http://www.cs.duke.edu/donaldlab/osprey.php or http://iiis.tsinghua.edu.cn/∼compbio/software.html. Contact: zengjy321@tsinghua.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24931991
Charon Toolkit for Parallel, Implicit Structured-Grid Computations: Functional Design
NASA Technical Reports Server (NTRS)
VanderWijngaart, Rob F.; Kutler, Paul (Technical Monitor)
1997-01-01
In a previous report the design concepts of Charon were presented. Charon is a toolkit that aids engineers in developing scientific programs for structured-grid applications to be run on MIMD parallel computers. It constitutes an augmentation of the general-purpose MPI-based message-passing layer, and provides the user with a hierarchy of tools for rapid prototyping and validation of parallel programs, and subsequent piecemeal performance tuning. Here we describe the implementation of the domain decomposition tools used for creating data distributions across sets of processors. We also present the hierarchy of parallelization tools that allows smooth translation of legacy code (or a serial design) into a parallel program. Along with the actual tool descriptions, we will present the considerations that led to the particular design choices. Many of these are motivated by the requirement that Charon must be useful within the traditional computational environments of Fortran 77 and C. Only the Fortran 77 syntax will be presented in this report.
The FORCE: A portable parallel programming language supporting computational structural mechanics
NASA Technical Reports Server (NTRS)
Jordan, Harry F.; Benten, Muhammad S.; Brehm, Juergen; Ramanan, Aruna
1989-01-01
This project supports the conversion of codes in Computational Structural Mechanics (CSM) to a parallel form which will efficiently exploit the computational power available from multiprocessors. The work is a part of a comprehensive, FORTRAN-based system to form a basis for a parallel version of the NICE/SPAR combination which will form the CSM Testbed. The software is macro-based and rests on the force methodology developed by the principal investigator in connection with an early scientific multiprocessor. Machine independence is an important characteristic of the system so that retargeting it to the Flex/32, or any other multiprocessor on which NICE/SPAR might be imnplemented, is well supported. The principal investigator has experience in producing parallel software for both full and sparse systems of linear equations using the force macros. Other researchers have used the Force in finite element programs. It has been possible to rapidly develop software which performs at maximum efficiency on a multiprocessor. The inherent machine independence of the system also means that the parallelization will not be limited to a specific multiprocessor.
Particle orbit tracking on a parallel computer: Hypertrack
Cole, B.; Bourianoff, G.; Pilat, F. ); Talman, R. )
1991-05-01
A program has been written which performs particle orbit tracking on the Intel iPSC/860 distributed memory parallel computer. The tracking is performed using a thin element approach. A brief description of the structure and performance of the code is presented, along with applications of the code to the analysis of accelerator lattices for the SSC. The concept of ensemble tracking'', i.e. the tracking of ensemble averages of noninteracting particles, such as the emittance, is presented. Preliminary results of such studies will be presented. 2 refs., 6 figs.
Parallel Computation of Persistent Homology using the Blowup Complex
Lewis, Ryan; Morozov, Dmitriy
2015-04-27
We describe a parallel algorithm that computes persistent homology, an algebraic descriptor of a filtered topological space. Our algorithm is distinguished by operating on a spatial decomposition of the domain, as opposed to a decomposition with respect to the filtration. We rely on a classical construction, called the Mayer--Vietoris blowup complex, to glue global topological information about a space from its disjoint subsets. We introduce an efficient algorithm to perform this gluing operation, which may be of independent interest, and describe how to process the domain hierarchically. We report on a set of experiments that help assess the strengths and identify the limitations of our method.
Routing performance analysis and optimization within a massively parallel computer
Archer, Charles Jens; Peters, Amanda; Pinnow, Kurt Walter; Swartz, Brent Allen
2013-04-16
An apparatus, program product and method optimize the operation of a massively parallel computer system by, in part, receiving actual performance data concerning an application executed by the plurality of interconnected nodes, and analyzing the actual performance data to identify an actual performance pattern. A desired performance pattern may be determined for the application, and an algorithm may be selected from among a plurality of algorithms stored within a memory, the algorithm being configured to achieve the desired performance pattern based on the actual performance data.
Semi-automatic process partitioning for parallel computation
NASA Technical Reports Server (NTRS)
Koelbel, Charles; Mehrotra, Piyush; Vanrosendale, John
1988-01-01
On current multiprocessor architectures one must carefully distribute data in memory in order to achieve high performance. Process partitioning is the operation of rewriting an algorithm as a collection of tasks, each operating primarily on its own portion of the data, to carry out the computation in parallel. A semi-automatic approach to process partitioning is considered in which the compiler, guided by advice from the user, automatically transforms programs into such an interacting task system. This approach is illustrated with a picture processing example written in BLAZE, which is transformed into a task system maximizing locality of memory reference.
Representing and computing regular languages on massively parallel networks
Miller, M.I.; O'Sullivan, J.A. ); Boysam, B. ); Smith, K.R. )
1991-01-01
This paper proposes a general method for incorporating rule-based constraints corresponding to regular languages into stochastic inference problems, thereby allowing for a unified representation of stochastic and syntactic pattern constraints. The authors' approach first established the formal connection of rules to Chomsky grammars, and generalizes the original work of Shannon on the encoding of rule-based channel sequences to Markov chains of maximum entropy. This maximum entropy probabilistic view leads to Gibb's representations with potentials which have their number of minima growing at precisely the exponential rate that the language of deterministically constrained sequences grow. These representations are coupled to stochastic diffusion algorithms, which sample the language-constrained sequences by visiting the energy minima according to the underlying Gibbs' probability law. The coupling to stochastic search methods yields the all-important practical result that fully parallel stochastic cellular automata may be derived to generate samples from the rule-based constraint sets. The production rules and neighborhood state structure of the language of sequences directly determines the necessary connection structures of the required parallel computing surface. Representations of this type have been mapped to the DAP-510 massively-parallel processor consisting of 1024 mesh-connected bit-serial processing elements for performing automated segmentation of electron-micrograph images.
Signal processing applications of massively parallel charge domain computing devices
NASA Technical Reports Server (NTRS)
Fijany, Amir (Inventor); Barhen, Jacob (Inventor); Toomarian, Nikzad (Inventor)
1999-01-01
The present invention is embodied in a charge coupled device (CCD)/charge injection device (CID) architecture capable of performing a Fourier transform by simultaneous matrix vector multiplication (MVM) operations in respective plural CCD/CID arrays in parallel in O(1) steps. For example, in one embodiment, a first CCD/CID array stores charge packets representing a first matrix operator based upon permutations of a Hartley transform and computes the Fourier transform of an incoming vector. A second CCD/CID array stores charge packets representing a second matrix operator based upon different permutations of a Hartley transform and computes the Fourier transform of an incoming vector. The incoming vector is applied to the inputs of the two CCD/CID arrays simultaneously, and the real and imaginary parts of the Fourier transform are produced simultaneously in the time required to perform a single MVM operation in a CCD/CID array.
Analytic reconstruction approach for parallel translational computed tomography.
Kong, Huihua; Yu, Hengyong
2015-01-01
To develop low-cost and low-dose computed tomography (CT) scanners for developing countries, recently a parallel translational computed tomography (PTCT) is proposed, and the source and detector are translated oppositely with respect to the imaging object without a slip-ring. In this paper, we develop an analytic filtered-backprojection (FBP)-type reconstruction algorithm for two dimensional (2D) fan-beam PTCT and extend it to three dimensional (3D) cone-beam geometry in a Feldkamp-type framework. Particularly, a weighting function is constructed to deal with data redundancy for multiple translations PTCT to eliminate image artifacts. Extensive numerical simulations are performed to validate and evaluate the proposed analytic reconstruction algorithms, and the results confirm their correctness and merits.
Parallel computing-based sclera recognition for human identification
NASA Astrophysics Data System (ADS)
Lin, Yong; Du, Eliza Y.; Zhou, Zhi
2012-06-01
Compared to iris recognition, sclera recognition which uses line descriptor can achieve comparable recognition accuracy in visible wavelengths. However, this method is too time-consuming to be implemented in a real-time system. In this paper, we propose a GPU-based parallel computing approach to reduce the sclera recognition time. We define a new descriptor in which the information of KD tree structure and sclera edge are added. Registration and matching task is divided into subtasks in various sizes according to their computation complexities. Every affine transform parameters are generated by searching on KD tree. Texture memory, constant memory, and shared memory are used to store templates and transform matrixes. The experiment results show that the proposed method executed on GPU can dramatically improve the sclera matching speed in hundreds of times without accuracy decreasing.
Solving the Cauchy-Riemann equations on parallel computers
NASA Technical Reports Server (NTRS)
Fatoohi, Raad A.; Grosch, Chester E.
1987-01-01
Discussed is the implementation of a single algorithm on three parallel-vector computers. The algorithm is a relaxation scheme for the solution of the Cauchy-Riemann equations; a set of coupled first order partial differential equations. The computers were chosen so as to encompass a variety of architectures. They are: the MPP, and SIMD machine with 16K bit serial processors; FLEX/32, an MIMD machine with 20 processors; and CRAY/2, an MIMD machine with four vector processors. The machine architectures are briefly described. The implementation of the algorithm is discussed in relation to these architectures and measures of the performance on each machine are given. Simple performance models are used to describe the performance. These models highlight the bottlenecks and limiting factors for this algorithm on these architectures. Conclusions are presented.
Applications of massively parallel computers in telemetry processing
NASA Technical Reports Server (NTRS)
El-Ghazawi, Tarek A.; Pritchard, Jim; Knoble, Gordon
1994-01-01
Telemetry processing refers to the reconstruction of full resolution raw instrumentation data with artifacts, of space and ground recording and transmission, removed. Being the first processing phase of satellite data, this process is also referred to as level-zero processing. This study is aimed at investigating the use of massively parallel computing technology in providing level-zero processing to spaceflights that adhere to the recommendations of the Consultative Committee on Space Data Systems (CCSDS). The workload characteristics, of level-zero processing, are used to identify processing requirements in high-performance computing systems. An example of level-zero functions on a SIMD MPP, such as the MasPar, is discussed. The requirements in this paper are based in part on the Earth Observing System (EOS) Data and Operation System (EDOS).
InSAR Scientific Computing Environment
NASA Technical Reports Server (NTRS)
Rosen, Paul A.; Sacco, Gian Franco; Gurrola, Eric M.; Zabker, Howard A.
2011-01-01
This computing environment is the next generation of geodetic image processing technology for repeat-pass Interferometric Synthetic Aperture (InSAR) sensors, identified by the community as a needed capability to provide flexibility and extensibility in reducing measurements from radar satellites and aircraft to new geophysical products. This software allows users of interferometric radar data the flexibility to process from Level 0 to Level 4 products using a variety of algorithms and for a range of available sensors. There are many radar satellites in orbit today delivering to the science community data of unprecedented quantity and quality, making possible large-scale studies in climate research, natural hazards, and the Earth's ecosystem. The proposed DESDynI mission, now under consideration by NASA for launch later in this decade, would provide time series and multiimage measurements that permit 4D models of Earth surface processes so that, for example, climate-induced changes over time would become apparent and quantifiable. This advanced data processing technology, applied to a global data set such as from the proposed DESDynI mission, enables a new class of analyses at time and spatial scales unavailable using current approaches. This software implements an accurate, extensible, and modular processing system designed to realize the full potential of InSAR data from future missions such as the proposed DESDynI, existing radar satellite data, as well as data from the NASA UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar), and other airborne platforms. The processing approach has been re-thought in order to enable multi-scene analysis by adding new algorithms and data interfaces, to permit user-reconfigurable operation and extensibility, and to capitalize on codes already developed by NASA and the science community. The framework incorporates modern programming methods based on recent research, including object-oriented scripts controlling legacy and
Constructing Scientific Arguments Using Evidence from Dynamic Computational Climate Models
NASA Astrophysics Data System (ADS)
Pallant, Amy; Lee, Hee-Sun
2015-04-01
Modeling and argumentation are two important scientific practices students need to develop throughout school years. In this paper, we investigated how middle and high school students ( N = 512) construct a scientific argument based on evidence from computational models with which they simulated climate change. We designed scientific argumentation tasks with three increasingly complex dynamic climate models. Each scientific argumentation task consisted of four parts: multiple-choice claim, openended explanation, five-point Likert scale uncertainty rating, and open-ended uncertainty rationale. We coded 1,294 scientific arguments in terms of a claim's consistency with current scientific consensus, whether explanations were model based or knowledge based and categorized the sources of uncertainty (personal vs. scientific). We used chi-square and ANOVA tests to identify significant patterns. Results indicate that (1) a majority of students incorporated models as evidence to support their claims, (2) most students used model output results shown on graphs to confirm their claim rather than to explain simulated molecular processes, (3) students' dependence on model results and their uncertainty rating diminished as the dynamic climate models became more and more complex, (4) some students' misconceptions interfered with observing and interpreting model results or simulated processes, and (5) students' uncertainty sources reflected more frequently on their assessment of personal knowledge or abilities related to the tasks than on their critical examination of scientific evidence resulting from models. These findings have implications for teaching and research related to the integration of scientific argumentation and modeling practices to address complex Earth systems.
Hayes, J C; Norman, M
1999-10-28
This report details an investigation into the efficacy of two approaches to solving the radiation diffusion equation within a radiation hydrodynamic simulation. Because leading-edge scientific computing platforms have evolved from large single-node vector processors to parallel aggregates containing tens to thousands of individual CPU's, the ability of an algorithm to maintain high compute efficiency when distributed over a large array of nodes is critically important. The viability of an algorithm thus hinges upon the tripartite question of numerical accuracy, total time to solution, and parallel efficiency.
Energy Proportionality and Performance in Data Parallel Computing Clusters
Kim, Jinoh; Chou, Jerry; Rotem, Doron
2011-02-14
Energy consumption in datacenters has recently become a major concern due to the rising operational costs andscalability issues. Recent solutions to this problem propose the principle of energy proportionality, i.e., the amount of energy consumedby the server nodes must be proportional to the amount of work performed. For data parallelism and fault tolerancepurposes, most common file systems used in MapReduce-type clusters maintain a set of replicas for each data block. A coveringset is a group of nodes that together contain at least one replica of the data blocks needed for performing computing tasks. In thiswork, we develop and analyze algorithms to maintain energy proportionality by discovering a covering set that minimizesenergy consumption while placing the remaining nodes in lowpower standby mode. Our algorithms can also discover coveringsets in heterogeneous computing environments. In order to allow more data parallelism, we generalize our algorithms so that itcan discover k-covering sets, i.e., a set of nodes that contain at least k replicas of the data blocks. Our experimental results showthat we can achieve substantial energy saving without significant performance loss in diverse cluster configurations and workingenvironments.
High performance computational chemistry: Towards fully distributed parallel algorithms
Guest, M.F.; Apra, E.; Bernholdt, D.E.
1994-07-01
An account is given of work in progress within the High Performance Computational Chemistry Group (HPCC) at the Pacific Northwest Laboratory (PNL) to develop molecular modeling software applications for massively parallel processors (MPPs). A discussion of the issues in developing scalable parallel algorithms is presented, with a particular focus on the distribution, as opposed to the replication, of key data structures. Replication of large data structures limits the maximum calculation size by imposing a low ratio of processors to memory. Only applications that distribute both data and computation across processors are truly scalable. The use of shared data structures, which may be independently accessed by each process even in a distributed-memory environment, greatly simplifies development and provides a significant performance enhancement. In describing tools to support this programming paradigm, an outline is given of the implementation and performance of a highly efficient and scalable algorithm to perform quadratically convergent, self-consistent field calculations on molecular systems. A brief account is given of the development of corresponding MPP capabilities in the areas of periodic Hartree Fock, Moeller-Plesset perturbation theory (MP2), density functional theory, and molecular dynamics. Performance figures are presented using both the Intel Touchstone Delta and Kendall Square Research KSR-2 supercomputers.
Efficient clustering of large EST data sets on parallel computers
Kalyanaraman, Anantharaman; Aluru, Srinivas; Kothari, Suresh; Brendel, Volker
2003-01-01
Clustering expressed sequence tags (ESTs) is a powerful strategy for gene identification, gene expression studies and identifying important genetic variations such as single nucleotide polymorphisms. To enable fast clustering of large-scale EST data, we developed PaCE (for Parallel Clustering of ESTs), a software program for EST clustering on parallel computers. In this paper, we report on the design and development of PaCE and its evaluation using Arabidopsis ESTs. The novel features of our approach include: (i) design of memory efficient algorithms to reduce the memory required to linear in the size of the input, (ii) a combination of algorithmic techniques to reduce the computational work without sacrificing the quality of clustering, and (iii) use of parallel processing to reduce run-time and facilitate clustering of larger data sets. Using a combination of these techniques, we report the clustering of 168 200 Arabidopsis ESTs in 15 min on an IBM xSeries cluster with 30 dual-processor nodes. We also clustered 327 632 rat ESTs in 47 min and 420 694 Triticum aestivum ESTs in 3 h and 15 min. We demonstrate the quality of our software using benchmark Arabidopsis EST data, and by comparing it with CAP3, a software widely used for EST assembly. Our software allows clustering of much larger EST data sets than is possible with current software. Because of its speed, it also facilitates multiple runs with different parameters, providing biologists a tool to better analyze EST sequence data. Using PaCE, we clustered EST data from 23 plant species and the results are available at the PlantGDB website. PMID:12771222
Wong Unhong; Wong Honcheng; Tang Zesheng
2010-05-21
The smoothed particle hydrodynamics (SPH), which is a class of meshfree particle methods (MPMs), has a wide range of applications from micro-scale to macro-scale as well as from discrete systems to continuum systems. Graphics hardware, originally designed for computer graphics, now provide unprecedented computational power for scientific computation. Particle system needs a huge amount of computations in physical simulation. In this paper, an efficient parallel implementation of a SPH method on graphics hardware using the Compute Unified Device Architecture is developed for fluid simulation. Comparing to the corresponding CPU implementation, our experimental results show that the new approach allows significant speedups of fluid simulation through handling huge amount of computations in parallel on graphics hardware.
Hey, Tony; Agarwal, Deborah; Borgman, Christine; Cartaro, Concetta; Crivelli, Silvia; Van Dam, Kerstin Kleese; Luce, Richard; Arjun, Shankar; Trefethen, Anne; Wade, Alex; Williams, Dean
2015-09-04
The Advanced Scientific Computing Advisory Committee (ASCAC) was charged to form a standing subcommittee to review the Department of Energy’s Office of Scientific and Technical Information (OSTI) and to begin by assessing the quality and effectiveness of OSTI’s recent and current products and services and to comment on its mission and future directions in the rapidly changing environment for scientific publication and data. The Committee met with OSTI staff and reviewed available products, services and other materials. This report summaries their initial findings and recommendations.
NASA Astrophysics Data System (ADS)
Nishiura, Daisuke; Furuichi, Mikito; Sakaguchi, Hide
2015-09-01
The computational performance of a smoothed particle hydrodynamics (SPH) simulation is investigated for three types of current shared-memory parallel computer devices: many integrated core (MIC) processors, graphics processing units (GPUs), and multi-core CPUs. We are especially interested in efficient shared-memory allocation methods for each chipset, because the efficient data access patterns differ between compute unified device architecture (CUDA) programming for GPUs and OpenMP programming for MIC processors and multi-core CPUs. We first introduce several parallel implementation techniques for the SPH code, and then examine these on our target computer architectures to determine the most effective algorithms for each processor unit. In addition, we evaluate the effective computing performance and power efficiency of the SPH simulation on each architecture, as these are critical metrics for overall performance in a multi-device environment. In our benchmark test, the GPU is found to produce the best arithmetic performance as a standalone device unit, and gives the most efficient power consumption. The multi-core CPU obtains the most effective computing performance. The computational speed of the MIC processor on Xeon Phi approached that of two Xeon CPUs. This indicates that using MICs is an attractive choice for existing SPH codes on multi-core CPUs parallelized by OpenMP, as it gains computational acceleration without the need for significant changes to the source code.
Parallel Computation of Unsteady Flows on a Network of Workstations
NASA Technical Reports Server (NTRS)
1997-01-01
Parallel computation of unsteady flows requires significant computational resources. The utilization of a network of workstations seems an efficient solution to the problem where large problems can be treated at a reasonable cost. This approach requires the solution of several problems: 1) the partitioning and distribution of the problem over a network of workstation, 2) efficient communication tools, 3) managing the system efficiently for a given problem. Of course, there is the question of the efficiency of any given numerical algorithm to such a computing system. NPARC code was chosen as a sample for the application. For the explicit version of the NPARC code both two- and three-dimensional problems were studied. Again both steady and unsteady problems were investigated. The issues studied as a part of the research program were: 1) how to distribute the data between the workstations, 2) how to compute and how to communicate at each node efficiently, 3) how to balance the load distribution. In the following, a summary of these activities is presented. Details of the work have been presented and published as referenced.
Parallel Computations in Insect and Mammalian Visual Motion Processing.
Clark, Damon A; Demb, Jonathan B
2016-10-24
Sensory systems use receptors to extract information from the environment and neural circuits to perform subsequent computations. These computations may be described as algorithms composed of sequential mathematical operations. Comparing these operations across taxa reveals how different neural circuits have evolved to solve the same problem, even when using different mechanisms to implement the underlying math. In this review, we compare how insect and mammalian neural circuits have solved the problem of motion estimation, focusing on the fruit fly Drosophila and the mouse retina. Although the two systems implement computations with grossly different anatomy and molecular mechanisms, the underlying circuits transform light into motion signals with strikingly similar processing steps. These similarities run from photoreceptor gain control and spatiotemporal tuning to ON and OFF pathway structures, motion detection, and computed motion signals. The parallels between the two systems suggest that a limited set of algorithms for estimating motion satisfies both the needs of sighted creatures and the constraints imposed on them by metabolism, anatomy, and the structure and regularities of the visual world.
NASA Technical Reports Server (NTRS)
Guruswamy, Guru P.; Byun, Chansup; VanDalsem, William (Technical Monitor)
1994-01-01
Aeroelasticity which involves strong coupling of fluids, structures and controls is an important element in designing an aircraft. Computational aeroelasticity using low fidelity methods such as the linear aerodynamic flow equations coupled with the modal structural equations are well advanced. Though these low fidelity approaches are computationally less intensive, they are not adequate for the analysis of modern aircraft such as High Speed Civil Transport (HSCT) and Advanced Subsonic Transport (AST) which can experience complex flow/structure interactions. HSCT can experience vortex induced aeroelastic oscillations whereas AST can experience transonic buffet associated structural oscillations. Both aircraft may experience a dip in the flutter speed at the transonic regime. For accurate aeroelastic computations at these complex fluid/structure interaction situations, high fidelity equations such as the Navier-Stokes for fluids and the finite-elements for structures are needed. Computations using these high fidelity equations require large computational resources both in memory and speed. Current conventional supercomputers have reached their limitations both in memory and speed. As a result, parallel computers have evolved to overcome the limitations of conventional computers. This paper will address the transition that is taking place in computational aeroelasticity from conventional computers to parallel computers. The paper will address special techniques needed to take advantage of the architecture of new parallel computers. Results will be illustrated from computations made on iPSC/860 and IBM SP2 computer by using ENASERO code that directly couples the Euler/Navier-Stokes flow equations with high resolution finite-element structural equations.
Low latency, high bandwidth data communications between compute nodes in a parallel computer
Archer, Charles J.; Blocksome, Michael A.; Ratterman, Joseph D.; Smith, Brian E.
2010-11-02
Methods, parallel computers, and computer program products are disclosed for low latency, high bandwidth data communications between compute nodes in a parallel computer. Embodiments include receiving, by an origin direct memory access (`DMA`) engine of an origin compute node, data for transfer to a target compute node; sending, by the origin DMA engine of the origin compute node to a target DMA engine on the target compute node, a request to send (`RTS`) message; transferring, by the origin DMA engine, a predetermined portion of the data to the target compute node using memory FIFO operation; determining, by the origin DMA engine whether an acknowledgement of the RTS message has been received from the target DMA engine; if the an acknowledgement of the RTS message has not been received, transferring, by the origin DMA engine, another predetermined portion of the data to the target compute node using a memory FIFO operation; and if the acknowledgement of the RTS message has been received by the origin DMA engine, transferring, by the origin DMA engine, any remaining portion of the data to the target compute node using a direct put operation.
Understanding the Performance and Potential of Cloud Computing for Scientific Applications
Sadooghi, Iman; Hernandez Martin, Jesus; Li, Tonglin; Brandstatter, Kevin; Zhao, Yong; Maheshwari, Ketan; Pais Pitta de Lacerda Ruivo, Tiago; Timm, Steven; Garzoglio, Gabriele; Raicu, Ioan
2015-01-01
Commercial clouds bring a great opportunity to the scientific computing area. Scientific applications usually require significant resources, however not all scientists have access to sufficient high-end computing systems, may of which can be found in the Top500 list. Cloud Computing has gained the attention of scientists as a competitive resource to run HPC applications at a potentially lower cost. But as a different infrastructure, it is unclear whether clouds are capable of running scientific applications with a reasonable performance per money spent. This work studies the performance of public clouds and places this performance in context to price. We evaluate the raw performance of different services of AWS cloud in terms of the basic resources, such as compute, memory, network and I/O. We also evaluate the performance of the scientific applications running in the cloud. This paper aims to assess the ability of the cloud to perform well, as well as to evaluate the cost of the cloud running scientific applications. We developed a full set of metrics and conducted a comprehensive performance evlauation over the Amazon cloud. We evaluated EC2, S3, EBS and DynamoDB among the many Amazon AWS services. We evaluated the memory sub-system performance with CacheBench, the network performance with iperf, processor and network performance with the HPL benchmark application, and shared storage with NFS and PVFS in addition to S3. We also evaluated a real scientific computing application through the Swift parallel scripting system at scale. Armed with both detailed benchmarks to gauge expected performance and a detailed monetary cost analysis, we expect this paper will be a recipe cookbook for scientists to help them decide where to deploy and run their scientific applications between public clouds, private clouds, or hybrid clouds.
Understanding Performance of Parallel Scientific Simulation Codes using Open|SpeedShop
Ghosh, K K
2011-11-07
Conclusions of this presentation are: (1) Open SpeedShop's (OSS) is convenient to use for large, parallel, scientific simulation codes; (2) Large codes benefit from uninstrumented execution; (3) Many experiments can be run in a short time - might need multiple shots e.g. usertime for caller-callee, hwcsamp for HW counters; (4) Decent idea of code's performance is easily obtained; (5) Statistical sampling calls for decent number of samples; and (6) HWC data is very useful for micro-analysis but can be tricky to analyze.
Parallel processing using an optical delay-based reservoir computer
NASA Astrophysics Data System (ADS)
Van der Sande, Guy; Nguimdo, Romain Modeste; Verschaffelt, Guy
2016-04-01
Delay systems subject to delayed optical feedback have recently shown great potential in solving computationally hard tasks. By implementing a neuro-inspired computational scheme relying on the transient response to optical data injection, high processing speeds have been demonstrated. However, reservoir computing systems based on delay dynamics discussed in the literature are designed by coupling many different stand-alone components which lead to bulky, lack of long-term stability, non-monolithic systems. Here we numerically investigate the possibility of implementing reservoir computing schemes based on semiconductor ring lasers. Semiconductor ring lasers are semiconductor lasers where the laser cavity consists of a ring-shaped waveguide. SRLs are highly integrable and scalable, making them ideal candidates for key components in photonic integrated circuits. SRLs can generate light in two counterpropagating directions between which bistability has been demonstrated. We demonstrate that two independent machine learning tasks , even with different nature of inputs with different input data signals can be simultaneously computed using a single photonic nonlinear node relying on the parallelism offered by photonics. We illustrate the performance on simultaneous chaotic time series prediction and a classification of the Nonlinear Channel Equalization. We take advantage of different directional modes to process individual tasks. Each directional mode processes one individual task to mitigate possible crosstalk between the tasks. Our results indicate that prediction/classification with errors comparable to the state-of-the-art performance can be obtained even with noise despite the two tasks being computed simultaneously. We also find that a good performance is obtained for both tasks for a broad range of the parameters. The results are discussed in detail in [Nguimdo et al., IEEE Trans. Neural Netw. Learn. Syst. 26, pp. 3301-3307, 2015
Cloud identification using genetic algorithms and massively parallel computation
NASA Technical Reports Server (NTRS)
Buckles, Bill P.; Petry, Frederick E.
1996-01-01
As a Guest Computational Investigator under the NASA administered component of the High Performance Computing and Communication Program, we implemented a massively parallel genetic algorithm on the MasPar SIMD computer. Experiments were conducted using Earth Science data in the domains of meteorology and oceanography. Results obtained in these domains are competitive with, and in most cases better than, similar problems solved using other methods. In the meteorological domain, we chose to identify clouds using AVHRR spectral data. Four cloud speciations were used although most researchers settle for three. Results were remarkedly consistent across all tests (91% accuracy). Refinements of this method may lead to more timely and complete information for Global Circulation Models (GCMS) that are prevalent in weather forecasting and global environment studies. In the oceanographic domain, we chose to identify ocean currents from a spectrometer having similar characteristics to AVHRR. Here the results were mixed (60% to 80% accuracy). Given that one is willing to run the experiment several times (say 10), then it is acceptable to claim the higher accuracy rating. This problem has never been successfully automated. Therefore, these results are encouraging even though less impressive than the cloud experiment. Successful conclusion of an automated ocean current detection system would impact coastal fishing, naval tactics, and the study of micro-climates. Finally we contributed to the basic knowledge of GA (genetic algorithm) behavior in parallel environments. We developed better knowledge of the use of subpopulations in the context of shared breeding pools and the migration of individuals. Rigorous experiments were conducted based on quantifiable performance criteria. While much of the work confirmed current wisdom, for the first time we were able to submit conclusive evidence. The software developed under this grant was placed in the public domain. An extensive user
Seismic imaging using finite-differences and parallel computers
Ober, C.C.
1997-12-31
A key to reducing the risks and costs of associated with oil and gas exploration is the fast, accurate imaging of complex geologies, such as salt domes in the Gulf of Mexico and overthrust regions in US onshore regions. Prestack depth migration generally yields the most accurate images, and one approach to this is to solve the scalar wave equation using finite differences. As part of an ongoing ACTI project funded by the US Department of Energy, a finite difference, 3-D prestack, depth migration code has been developed. The goal of this work is to demonstrate that massively parallel computers can be used efficiently for seismic imaging, and that sufficient computing power exists (or soon will exist) to make finite difference, prestack, depth migration practical for oil and gas exploration. Several problems had to be addressed to get an efficient code for the Intel Paragon. These include efficient I/O, efficient parallel tridiagonal solves, and high single-node performance. Furthermore, to provide portable code the author has been restricted to the use of high-level programming languages (C and Fortran) and interprocessor communications using MPI. He has been using the SUNMOS operating system, which has affected many of his programming decisions. He will present images created from two verification datasets (the Marmousi Model and the SEG/EAEG 3D Salt Model). Also, he will show recent images from real datasets, and point out locations of improved imaging. Finally, he will discuss areas of current research which will hopefully improve the image quality and reduce computational costs.