Berry / Gallivan / Gallopoulos High-Performance Scientific Computing

Algorithms and Applications
2012
ISBN: 978-1-4471-2437-5
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 350 Seiten, eBook

Advances in the development of parallel algorithms and system software now enable the ever-increasing power of scalable high-performance computers to be harnessed for scientific computing applications at fidelities that rival – and in many cases exceed – experimental methodologies.

This comprehensive text/reference, inspired by the visionary work of Prof. Ahmed H. Sameh, represents the state of the art in parallel numerical algorithms, applications, architectures, and system software. Articles in this collection address various challenges arising from concurrency, scale, energy efficiency, and programmability, and associated solutions that have shaped the current high-performance computing landscape. These solutions are discussed in the context of diverse applications, ranging from scientific simulations to large-scale data analysis and mining.

Topics and features: includes contributions from an international selection of world-class authorities, inspired by the work of Prof. Ahmed H. Sameh and his involvement in parallel numerical algorithm design since Illiac IV and the University of Illinois Cedar multiprocessor; examines various aspects of parallel algorithm-architecture interaction through articles on computational capacity-based codesign and automatic restructuring of programs using compilation techniques; reviews emerging applications of numerical methods in information retrieval and data mining; discusses the latest issues in dense and sparse matrix computations for modern high-performance systems, multicores, manycores and GPUs, and several perspectives on the Spike family of algorithms for solving linear systems; presents outstanding challenges and developing technologies, and puts these in their historical context.

This authoritative reference is a must-read for researchers and graduate students in disciplines as diverse as computational fluid dynamics, signal processing, and structural mechanics. Professionals involved in applications that rely on high-performance computers will also find the text an essential resource.

Zielgruppe

Research

Autoren/Hrsg.

Berry, Michael W.

Gallivan, Kyle A.

Gallopoulos, Efstratios

Grama, Ananth

Philippe, Bernard

Saad, Yousef

S, Yousef

Saied, Faisal

Inhaltsverzeichnis

Parallel Numerical Computing from Illiac IV to Exascale: The Contributions of Ahmed H. SamehK. Gallivan, E. Gallopoulos, A. Grama, B. Philippe, E. Polizzi, Y. Saad, F. Saied, and D. SorensenComputational Capacity-Based Co-design of Computer SystemsDavid J. KuckMeasuring Computer PerformanceWilliam Jalby, David C. Wong, David J. Kuck, Jean-Thomas Acquaviva, and Jean-Christophe BeylerA Compilation Framework for the Automatic Restructuring of Pointer-Linked Data StructuresHarmen L.A. van der Spek, C.W. Mattias Holm, and Harry A.G. WijshoffDense Linear Algebra on Accelerated Multicore HardwareJack Dongarra, Jakub Kurzak, Piotr Luszczek, and Stanimire TomovThe Explicit SPIKE Algorithm: Iterative Solution of the Reduced SystemCarl Christian Kjelgaard MikkelsenThe SPIKE Factorization as Domain Decomposition Method: Equivalent and Variant ApproachesVictor Eijkhout, and Robert van de GeijnParallel Solution of Sparse Linear SystemsMurat ManguogluParallel Block-Jacobi SVD MethodsMartin Becka, Gabriel Okša, and Marián VajteršicRobust and Efficient Multifrontal Solver for Large Discretized PDEsJianlin XiaA Preconditioned Scheme for Nonsymmetric Saddle-Point ProblemsAbdelkader BaggagEffect of Ordering for Iterative Solvers in Structural Mechanics ProblemsSami A. KilicScaling Hypre’s Multigrid Solvers to 100,000 CoresAllison H. Baker, Robert D. Falgout, Tzanio V. Kolev, and Ulrike Meier YangA Riemannian Dennis-Moré ConditionK.A. Gallivan, C. Qi, and P.-A. AbsilA Jump-Start of Non-Negative Least Squares SolversMu Wang, and Xiaoge WangFast Nonnegative Tensor Factorization with an Active-Set-Like MethodJingu Kim, and Haesun ParkKnowledge Discovery Using Nonnegative Tensor Factorization with Visual AnalyticsAndrey A. Puretskiy and Michael W. Berry