Scientific Computing with Multicore and Accelerators

Scientific Computing with Multicore and Accelerators

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Features

  • Focuses on the latest microarchitectures, including the STI Cell BE, Intel Nehalem, AMD Barcelona, and NVIDIA GTX 285
  • Covers applications in high-performance multimedia and gaming, solid mechanics, fluid dynamics, molecular modeling, computational biology, drug design, and biomedicine
  • Examines the role of FFTs and combinatorial algorithms in multicore architectures
  • Includes many real-world examples and case studies
  • Offers code for download on authors’ websites

Summary

The hybrid/heterogeneous nature of future microprocessors and large high-performance computing systems will result in a reliance on two major types of components: multicore/manycore central processing units and special purpose hardware/massively parallel accelerators. While these technologies have numerous benefits, they also pose substantial performance challenges for developers, including scalability, software tuning, and programming issues.

Researchers at the Forefront Reveal Results from Their Own State-of-the-Art Work
Edited by some of the top researchers in the field and with contributions from a variety of international experts, Scientific Computing with Multicore and Accelerators focuses on the architectural design and implementation of multicore and manycore processors and accelerators, including graphics processing units (GPUs) and the Sony Toshiba IBM (STI) Cell Broadband Engine (BE) currently used in the Sony PlayStation 3. The book explains how numerical libraries, such as LAPACK, help solve computational science problems; explores the emerging area of hardware-oriented numerics; and presents the design of a fast Fourier transform (FFT) and a parallel list ranking algorithm for the Cell BE. It covers stencil computations, auto-tuning, optimizations of a computational kernel, sequence alignment and homology, and pairwise computations. The book also evaluates the portability of drug design applications to the Cell BE and illustrates how to successfully exploit the computational capabilities of GPUs for scientific applications. It concludes with chapters on dataflow frameworks, the Charm++ programming model, scan algorithms, and a portable intracore communication framework.

Explores the New Computational Landscape of Hybrid Processors
By offering insight into the process of constructing and effectively using the technology, this volume provides a thorough and practical introduction to the area of hybrid computing. It discusses introductory concepts and simple examples of parallel computing, logical and performance debugging for parallel computing, and advanced topics and issues related to the use and building of many applications.

Table of Contents

Dense Linear Algebra
Implementing Matrix Multiplication on the Cell B.E, Wesley Alvaro, Jakub Kurzak, and Jack Dongarra

Implementing Matrix Factorizations on the Cell BE, Jakub Kurzak and Jack Dongarra

Dense Linear Algebra for Hybrid GPU-Based Systems, Stanimire Tomov and Jack Dongarra

BLAS for GPUs, Rajib Nath, Stanimire Tomov, and Jack Dongarra

Sparse Linear Algebra
Sparse Matrix-Vector Multiplication on Multicore and Accelerators, Samuel Williams, Nathan Bell, Jee Whan Choi, Michael Garland, Leonid Oliker, and Richard Vuduc

Multigrid Methods
Hardware-Oriented Multigrid Finite Element Solvers on GPU-Accelerated Clusters, Stefan Turek, Dominik Göddeke, Sven H.M. Buijssen, and Hilmar Wobker

Mixed-Precision GPU-Multigrid Solvers with Strong Smoothers, Dominik Göddeke and Robert Strzodka

Fast Fourier Transforms
Designing Fast Fourier Transform (FFT) for the IBM Cell BE, Virat Agarwal and David A. Bader

Implementing FFTs on Multicore Architectures, Alex Chunghen Chow, Gordon C. Fossum, and Daniel A. Brokenshire

Combinatorial Algorithms
Combinatorial Algorithm Design on the Cell/BE Processor, David A. Bader, Virat Agarwal, Kamesh Madduri, and Fabrizio Petrini

Stencil Algorithms
Auto-Tuning Stencil Computations on Multicore and Accelerators, Kaushik Datta, Samuel Williams, Vasily Volkov, Jonathan Carter, Leonid Oliker, John Shalf, and Katherine Yelick

Manycore Stencil Computations in Hyperthermia Applications, Matthias Christen, Olaf Schenk, Esra Neufeld, Maarten Paulides, and Helmar Burkhart

Bioinformatics
Enabling Bioinformatics Algorithms on the Cell/BE Processor, Vipin Sachdeva, Michael Kistler, and Tzy-Hwa Kathy Tzeng

Pairwise Computations on the Cell Processor, Abhinav Sarje, Jaroslaw Zola, and Srinivas Aluru

Molecular Modeling
Drug Design on the Cell BE, Cecilia González-Álvarez, Harald Servat, Daniel Cabrera-Benítez, Xavier Aguilar, Carles Pons, Juan Fernández-Recio, and Daniel Jiménez-González

GPU Algorithms for Molecular Modeling, John E. Stone, David J. Hardy, Barry Isralewitz, and Klaus Schulten

Complementary Topics
Dataflow Frameworks for Emerging Heterogeneous Architectures and Their Application to Biomedicine, Umit V. Catalyurek, Renato Ferreira, Timothy D.R. Hartley, George Teodoro, and Rafael Sachetto

Accelerator Support in the Charm++ Parallel Programming Model, Laxmikant V. Kalé, David M. Kunzman, and Lukasz Wesolowski

Efficient Parallel Scan Algorithms for Manycore GPUs, Shubhabrata Sengupta, Mark Harris, Michael Garland, and John D. Owens

High Performance Topology-Aware Communication in Multicore Processors, Hari Subramoni, Fabrizio Petrini, Virat Agarwal, and Davide Pasetto

Index

Author Bio(s)

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