Introduction to Parallel Computing From Algorithms to Programming on State-of-the-Art Platforms için kapak resmi
Introduction to Parallel Computing From Algorithms to Programming on State-of-the-Art Platforms
Başlık:
Introduction to Parallel Computing From Algorithms to Programming on State-of-the-Art Platforms
Yazar:
Trobec, Roman. author.
ISBN:
9783319988337
Edisyon:
1st ed. 2018.
Fiziksel Niteleme:
XII, 256 p. 86 illus., 7 illus. in color. online resource.
Seri:
Undergraduate Topics in Computer Science,
İçindekiler:
Part I: Foundations -- Why Do We Need Parallel Programming -- Overview of Parallel Systems -- Part II: Programming -- Programming Multi-Core and Shared Memory Multiprocessors Using OpenMP -- MPI Processes and Messaging -- OpenCL for Massively Parallel Graphic Processors -- Part III: Engineering -- Engineering: Parallel Computation of the Number π -- Engineering: Parallel Solution of 1-D Heat Equation -- Engineering: Parallel Implementation of Seam Carving -- Final Remarks and Perspectives -- Appendix A: Hints for Making Your Computer a Parallel Machine.
Özet:
Advancements in microprocessor architecture, interconnection technology, and software development have fueled rapid growth in parallel and distributed computing. However, this development is only of practical benefit if it is accompanied by progress in the design, analysis and programming of parallel algorithms. This concise textbook provides, in one place, three mainstream parallelization approaches, Open MPP, MPI and OpenCL, for multicore computers, interconnected computers and graphical processing units. An overview of practical parallel computing and principles will enable the reader to design efficient parallel programs for solving various computational problems on state-of-the-art personal computers and computing clusters. Topics covered range from parallel algorithms, programming tools, OpenMP, MPI and OpenCL, followed by experimental measurements of parallel programs’ run-times, and by engineering analysis of obtained results for improved parallel execution performances. Many examples and exercises support the exposition.