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Synopsis
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
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From the Back Cover
MIMD computers are notoriously difficult to program. Data-Parallel Programming on MIMD Computers 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.
About the Author
Philip J. Hatcher is Assistant Professor in the Department of Computer Science at the University of New Hampshire.
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- PublisherMit Pr
- Publication date1991
- ISBN 10 0262082055
- ISBN 13 9780262082051
- BindingHardcover
- LanguageEnglish
- Edition number1
- Number of pages231
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Data-Parallel Programming on MIMD Computers (Scientific and Engineering Computation)
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Condition: as new. Cambridge, MA: The MIT Press, 1991. Hardcover. Dustjacket. 231 pp.(Scientific and Engineering Computation). - 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.Philip J. Hatcher is Assistant Professor in the Department of Computer Science at the University of New Hampshire. Michael J. Quinn is Associate Professor of Computer Science at Oregon State University.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. English text. Condition : as new. Condition : as new copy. ISBN 9780262082051. Keywords : , Seller Inventory # 263635
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