Scientific Computing with Multicore and Accelerators

1. Auflage 2017
ISBN: 978-1-138-11332-9
Verlag: Taylor & Francis Ltd

Buch, Englisch, 514 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 703 g

Reihe: Chapman & Hall/CRC Computational Science

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.

Zielgruppe

Researchers in high performance computing, parallel computing, and supercomputing; software developers and programmers; researchers in computational engineering, fluid dynamics, physics, and biology.

Autoren/Hrsg.

Kurzak, Jakub

Bader, David A.

Dongarra, Jack

Fachgebiete

• Mathematik | Informatik EDV | Informatik EDV & Informatik Allgemein
• Mathematik | Informatik Mathematik Mathematik Allgemein Zahlensysteme
• Mathematik | Informatik Mathematik Algebra Zahlentheorie

Inhaltsverzeichnis

Dense Linear Algebra. Sparse Linear Algebra. Multigrid Methods. Fast Fourier Transforms. Combinatorial Algorithms. Stencil Algorithms. Bioinformatics. Molecular Modeling. Complementary Topics. Index.

Über Autor(innen)

Jakub Kurzak is a research director in the Innovative Computing Laboratory in the Department of Electrical Engineering and Computer Science at the University of Tennessee. Dr. Kurzak is a program committee member for several international conferences and a reviewer for a number of top-ranking journals. His research focuses on utilizing multicore systems and accelerators for scientific computing.

David A. Bader is a professor in the School of Computational Science and Engineering, College of Computing, and executive director for High Performance Computing at the Georgia Institute of Technology. He is a lead scientist in the DARPA Ubiquitous High Performance Computing (UHPC) program, an associate editor for several high-impact journals, and editor of the book Petascale Computing: Algorithms and Applications (CRC Press, 2008). An IEEE Fellow and member of the ACM, Dr. Bader has been an NSF CAREER Award recipient and has received awards from IBM, NVIDIA, Intel, Sun Microsystems, and Microsoft Research. His main areas of research are in parallel algorithms, combinatorial optimization, and computational biology and genomics.

Jack Dongarra is a University Distinguished Professor of Electrical Engineering and Computer Science at the University of Tennessee, where he is the director of the Innovative Computing Laboratory and the director of the Center for Information Technology Research. He also is a member of the Distinguished Research Staff in the Computer Science and Mathematics Division at Oak Ridge National Laboratory, a Turing Fellow at the University of Manchester, and an adjunct professor in the Department of Computer Science at Rice University. A Fellow of the AAAS, ACM, IEEE, and SIAM, Dr. Dongarra has received numerous awards, including the first SIAM Special Interest Group on Supercomputing award for Career Achievement, the first IEEE Medal of Excellence in Scalable Computing, and the IEEE Sidney Fernbach Award. His research encompasses numerical algorithms in linear algebra, parallel computing, the use of advanced computer architectures, programming methodology, and tools for parallel computers.