A bibliography on parallel and vector numerical algorithms

This is a bibliography of numerical methods. It also includes a number of other references on machine architecture, programming language, and other topics of interest to scientific computing. Certain conference proceedings and anthologies which have been published in book form are listed also

Machine characterization and benchmark performance prediction

From runs of standard benchmarks or benchmark suites, it is not possible to characterize the machine nor to predict the run time of other benchmarks which have not been run. A new approach to benchmarking and machine characterization is reported. The creation and use of a machine analyzer is described, which measures the performance of a given machine on FORTRAN source language constructs. The machine analyzer yields a set of parameters which characterize the machine and spotlight its strong and weak points. Also described is a program analyzer, which analyzes FORTRAN programs and determines the frequency of execution of each of the same set of source language operations. It is then shown that by combining a machine characterization and a program characterization, we are able to predict with good accuracy the run time of a given benchmark on a given machine. Characterizations are provided for the Cray-X-MP/48, Cyber 205, IBM 3090/200, Amdahl 5840, Convex C-1, VAX 8600, VAX 11/785, VAX 11/780, SUN 3/50, and IBM RT-PC/125, and for the following benchmark programs or suites: Los Alamos (BMK8A1), Baskett, Linpack, Livermore Loops, Madelbrot Set, NAS Kernels, Shell Sort, Smith, Whetstone and Sieve of Erathostenes

Solution of partial differential equations on vector and parallel computers

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

Development of a Navier-Stokes algorithm for parallel-processing supercomputers

An explicit flow solver, applicable to the hierarchy of model equations ranging from Euler to full Navier-Stokes, is combined with several techniques designed to reduce computational expense. The computational domain consists of local grid refinements embedded in a global coarse mesh, where the locations of these refinements are defined by the physics of the flow. Flow characteristics are also used to determine which set of model equations is appropriate for solution in each region, thereby reducing not only the number of grid points at which the solution must be obtained, but also the computational effort required to get that solution. Acceleration to steady-state is achieved by applying multigrid on each of the subgrids, regardless of the particular model equations being solved. Since each of these components is explicit, advantage can readily be taken of the vector- and parallel-processing capabilities of machines such as the Cray X-MP and Cray-2

Performance Evaluation of Plasma and Astrophysics Applications on Modern Parallel Vector Systems

Abstract. The last decade has witnessed a rapid proliferation of superscalar cache-based microprocessors to build high-end computing (HEC) platforms, primarily because of their generality, scalability, and cost effectiveness. However, the growing gap between sustained and peak performance for full-scale scientific applications on such platforms has become major concern in high performance computing. The latest generation of custom-built parallel vector systems have the potential to address this concern for numerical algorithms with sufficient regularity in their computational structure. In this work, we explore two and three dimensional implementations of a plasma physics application, as well as a leading astrophysics package on some of today's most powerful supercomputing platforms. Results compare performance between the the vector-based Cray X1, Earth Simulator, and newly-released NEC SX-8, with the commodity-based superscalar platforms of the IBM Power3, Intel Itanium2, and AMD Opteron. Overall results show that the SX-8 attains unprecedented aggregate performance across our evaluated applications

Performance Evaluation of Plasma and Astrophysics Applications onModern Parallel Vector Systems

The last decade has witnessed a rapid proliferation ofsuperscalar cache-based microprocessors to build high-endcomputing (HEC)platforms, primarily because of their generality,scalability, and costeffectiveness. However, the growing gap between sustained and peakperformance for full-scale scientific applications on such platforms hasbecome major concern in highperformance computing. The latest generationof custom-built parallel vector systems have the potential to addressthis concern for numerical algorithms with sufficient regularity in theircomputational structure. In this work, we explore two and threedimensional implementations of a plasma physics application, as well as aleading astrophysics package on some of today's most powerfulsupercomputing platforms. Results compare performance between the thevector-based Cray X1, EarthSimulator, and newly-released NEC SX- 8, withthe commodity-based superscalar platforms of the IBM Power3, IntelItanium2, and AMDOpteron. Overall results show that the SX-8 attainsunprecedented aggregate performance across our evaluatedapplications

Relevance of accurate Monte Carlo modeling in nuclear medical imaging

Monte Carlo techniques have become popular in different areas of medical physics with advantage of powerful computing systems. In particular, they have been extensively applied to simulate processes involving random behavior and to quantify physical parameters that are difficult or even impossible to calculate by experimental measurements. Recent nuclear medical imaging innovations such as single-photon emission computed tomography (SPECT), positron emission tomography (PET), and multiple emission tomography (MET) are ideal for Monte Carlo modeling techniques because of the stochastic nature of radiation emission, transport and detection processes. Factors which have contributed to the wider use include improved models of radiation transport processes, the practicality of application with the development of acceleration schemes and the improved speed of computers. This paper presents derivation and methodological basis for this approach and critically reviews their areas of application in nuclear imaging. An overview of existing simulation programs is provided and illustrated with examples of some useful features of such sophisticated tools in connection with common computing facilities and more powerful multiple-processor parallel processing systems. Current and future trends in the field are also discussed