
Data-Parallel Programming on MIMD Computers
MIT Press
Published on 30. December 1991
Book
Hardback
250 pages
978-0-262-08205-1 (ISBN)
Description
Data-Parallel Programming demonstrates that architecture-independent parallel programming is possible by describing in detail how programs written in a high-level SIMD programming language may be compiled and efficiently executed-on both shared-memory multiprocessors and distributed-memory multicomputers.
MIMD computers are notoriously difficult to program. Data-Parallel Programming demonstrates that architecture-independent parallel programming is possible by describing in detail how programs written in a high-level SIMD programming language may be compiled and efficiently executed-on both shared-memory multiprocessors and distributed-memory multicomputers. The authors provide enough data so that the reader can decide the feasibility of architecture-independent programming in a data-parallel language. For each benchmark program they give the source code listing, absolute execution time on both a multiprocessor and a multicomputer, and a speedup relative to a sequential program. And they often present multiple solutions to the same problem, to better illustrate the strengths and weaknesses of these compilers. The language presented is Dataparallel C, a variant of the original C* language developed by Thinking Machines Corporation for its Connection Machine processor array. Separate chapters describe the compilation of Dataparallel C programs for execution on the Sequent multiprocessor and the Intel and nCUBE hypercubes, respectively. The authors document the performance of these compilers on a variety of benchmark programs and present several case studies.
Contents
Introduction Dataparallel C Programming Language Description Design of a Multicomputer Dataparallel C Compiler Design of a Multiprocessor Dataparallel C Compiler Writing Efficient Programs Benchmarking the Compilers Case Studies Conclusions
MIMD computers are notoriously difficult to program. Data-Parallel Programming demonstrates that architecture-independent parallel programming is possible by describing in detail how programs written in a high-level SIMD programming language may be compiled and efficiently executed-on both shared-memory multiprocessors and distributed-memory multicomputers. The authors provide enough data so that the reader can decide the feasibility of architecture-independent programming in a data-parallel language. For each benchmark program they give the source code listing, absolute execution time on both a multiprocessor and a multicomputer, and a speedup relative to a sequential program. And they often present multiple solutions to the same problem, to better illustrate the strengths and weaknesses of these compilers. The language presented is Dataparallel C, a variant of the original C* language developed by Thinking Machines Corporation for its Connection Machine processor array. Separate chapters describe the compilation of Dataparallel C programs for execution on the Sequent multiprocessor and the Intel and nCUBE hypercubes, respectively. The authors document the performance of these compilers on a variety of benchmark programs and present several case studies.
Contents
Introduction Dataparallel C Programming Language Description Design of a Multicomputer Dataparallel C Compiler Design of a Multiprocessor Dataparallel C Compiler Writing Efficient Programs Benchmarking the Compilers Case Studies Conclusions
More details
Series
Language
English
Place of publication
Cambridge, Mass.
United States
Publishing group
MIT Press Ltd
Target group
College/higher education
Professional and scholarly
Interest Age: From 18 years
Dimensions
Height: 231 mm
Width: 180 mm
Thickness: 20 mm
Weight
612 gr
ISBN-13
978-0-262-08205-1 (9780262082051)
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Schweitzer Classification
Persons
Michael J. Quinn is Dean of the College of Science and Engineering atSeattle University.