Antoine Petitet

ScaLAPACK users' guide

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The LINPACK Benchmark: past, present and future

This paper describes the LINPACK Benchmark and some of its variations commonly used to assess the performance of computer systems. Aside from the LINPACK Benchmark suite, the TOP500 and the HPL codes are presented. The latter is frequently used to obtained results for TOP500 submissions. Information is also given on how to interpret the results of the benchmark and how the results fit into the performance evaluation process. Copyright

An Updated Set of Basic Linear Algebra Subprograms (BLAS)

L. SUSAN BLACKFORD Myricom, Inc. JAMES DEMMEL University of California, Berkeley JACK DONGARRA The University of Tennessee IAIN DUFF Rutherford Appleton Laboratory and CERFACS SVEN HAMMARLING Numerical Algorithms Group, Ltd. GREG HENRY Intel Corporation MICHAEL HEROUX Sandia National Laboratories LINDA KAUFMAN William Patterson University ANDREW LUMSDAINE Indiana University ANTOINE PETITET Sun Microsystems ROLDAN POZO National Institute of Standards and Technology KARIN REMINGTON The Center for Advancement of Genomics and R. CLINT WHALEY Florida State University

Self-Adapting Linear Algebra Algorithms and Software

One of the main obstacles to the efficient solution of scientific problems is the problem of tuning software, both to the available architecture and to the user problem at hand. We describe approaches for obtaining tuned high-performance kernels and for automatically choosing suitable algorithms. Specifically, we describe the generation of dense and sparse Basic Linear Algebra Subprograms (BLAS) kernels, and the selection of linear solver algorithms. However, the ideas presented here extend beyond these areas, which can be considered proof of concept.

Design and Implementation of the ScaLAPACK LU, QR, and Cholesky Factorization Routines

This article discusses the core factorization routines included in the ScaLAPACK library. These routines allow the factorization and solution of a dense system of linear equations via LU, QR, and Cholesky. They are implemented using a block cyclic data distribution, and are built using de facto standard kernels for matrix and vector operations (BLAS and its parallel counterpart PBLAS) and message passing communication (BLACS). In implementing the ScaLAPACK routines, a major objective was to parallelize the corresponding sequential LAPACK using the BLAS, BLACS, and PBLAS as building blocks, leading to straightforward parallel implementations without a significant loss in performance. We present the details of the implementation of the ScaLAPACK factorization routines, as well as performance and scalability results on the Intel iPSC/860, Intel Touchstone Delta, and Intel Paragon System.

A Proposal for a Set of Parallel Basic Linear Algebra Subprograms

This paper describes a proposal for a set of Parallel Basic Linear Algebra Subprograms (PBLAS) for distributed memory MIMD computers. The PBLAS are targeted at distributed vector-vector, matrixvector and matrix-matrix operations with the aim of simplifying the parallelization of linear algebra codes, especially when implemented on top of the sequential BLAS.

ScaLAPACK: a portable linear algebra library for distributed memory computers — design issues and performance

This paper outlines the content and performance of ScaLAPACK, a collection of mathematical software for linear algebra computations on distributed memory computers. The importance of developing standards for computational and message passing interfaces is discussed. We present the different components and building blocks of ScaLAPACK, and indicate the difficulties inherent in producing correct codes for networks of heterogeneous processors. Finally, this paper briefly describes future directions for the ScaLAPACK library and concludes by suggesting alternative approaches to mathematical libraries, explaining how ScaLAPACK could be integrated into efficient and user-friendly distributed systems.

Numerical Libraries and the Grid

This paper describes an overall framework for the design of numerical libraries on a computational grid of processors in which the processors may be geographically distributed and under the control of a grid-based scheduling system. Experiments are presented in the context of solving systems of linear equations using routines from the ScaLAPACK software collection along with various grid service components, such as Globus, NWS, and Autopilot.

ScaLAPACK: A Portable Linear Algebra Library for Distributed Memory Computers - Design Issues and Performance

This paper outlines the content and performance of ScaLA-PACK, a collection of mathematical software for linear algebra computations on distributed memory computers. The importance of developing standards for computational and message passing interfaces is discussed. We present the different components and building blocks of ScaLAPACK. This paper outlines the difficulties inherent in producing correct codes for networks of heterogeneous processors. Finally, this paper briefly describes future directions for the ScaLAPACK library and concludes by suggesting alternative approaches to mathematical libraries, explaining how ScaLAPACK could be integrated into efficient and user-friendly distributed systems.

Numerical Libraries And The Grid: The GrADS Experiments With ScaLAPACK

This paper describes an overall framework for the design of numerical libraries on a computational Grid of processors where the processors may be geographically distributed and under the control of a Grid-based scheduling system. A set of experiments are presented in the context of solving systems of linear equations using routines from the ScaLAPACK software collection along with various grid service components, such as Globus, NWS, and Autopilot.