Computers have been moving toward a multicore paradigm for the last several years. As a result of the recent multicore paradigm shift, software developers must design applications that exploit the inherent parallelism...
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ISBN:
(纸本)9781479956180
Computers have been moving toward a multicore paradigm for the last several years. As a result of the recent multicore paradigm shift, software developers must design applications that exploit the inherent parallelism of modern computing architectures. One of the areas of research to simplify this shift is the development of dynamic scheduling utilities that allow the developer to specify serial code that can be parallelized using a library or compiler technology. While these tools certainly increase the developer's productivity, they can obfuscate performance bottlenecks. For this reason, it is important to evaluate algorithm performance in order to ensure that the performance of a given algorithm is being realized using dynamic scheduling utilities. This paper presents the methodology and results of a new performance analysis tool that aims to accurately simulate the performance of various superscalar schedulers, including OmpSs, StarPU, and QUARK. The process begins with careful timing of each of the computational routines that make up the algorithm. The simulation tool then uses the timing of the computational kernels in conjunction with the dependency management provided by the superscalar scheduler in order to simulate the execution time of the algorithm. This tool demonstrates that simulation results of various algorithms can accurately predict the performance of a complex dynamic scheduling system.
This book is a guide to understanding and using the software package ARPACK to solve large algebraic eigenvalue problems. The software described is based on the implicitly restarted Arnoldi method, which has been hera...
详细信息
ISBN:
(数字)9780898719628
ISBN:
(纸本)9780898714074
This book is a guide to understanding and using the software package ARPACK to solve large algebraic eigenvalue problems. The software described is based on the implicitly restarted Arnoldi method, which has been heralded as one of the three most important advances in large scale eigenanalysis in the past ten years. The book explains the acquisition, installation, capabilities, and detailed use of the software for computing a desired subset of the eigenvalues and eigenvectors of large (sparse) standard or generalized eigenproblems. It also discusses the underlying theory and algorithmic background at a level that is accessible to the general practitioner.
Other important topics covered include
* Treatment of the non-Hermitian problem,
* Explanation of the theory behind Krylov subspace projection methods, implicit restarting, and spectral transformation,
* Explanation of the implicitly restarted Arnoldi method (IRAM),
* Descriptions of the various templates (driver routines) to interface an application with ARPACK to solve a wide variety of problems.
ARPACK is a collection of Fortran 77 subroutines designed to solve large-scale eigenvalue problems. It provides state-of-the-art software for solving large (sparse) Hermitian, non-Hermitian, standard, or generalized eigenvalue problems from significant application areas. It is one of the few software packages to successfully address the non-Hermitian problem. Practitioners will be able to better understand the full capabilities of ARPACK (ARnoldi PACKage) and grasp the underlying theory more thoroughly with this book.
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