Parallelization of sequential programs: distribution of arrays among processors and structurization of communications

Data distribution functions are introduced. They are matched with scheduling functions. The processors and iterations are determined that use an array element at its fixed position in a statement. This makes it possible to obtain the initial data distribution and also information on the data volume...

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Vydáno v:Cybernetics and systems analysis Ročník 48; číslo 1; s. 122 - 137
Hlavní autoři: Adutskevich, E. V., Likhoded, N. A., Sikorsky, A. O.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Boston Springer US 01.01.2012
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Springer Nature B.V
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ISSN:1060-0396, 1573-8337
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Abstract Data distribution functions are introduced. They are matched with scheduling functions. The processors and iterations are determined that use an array element at its fixed position in a statement. This makes it possible to obtain the initial data distribution and also information on the data volume for every processor and on the structure of required communications.
AbstractList Data distribution functions are introduced. They are matched with scheduling functions. The processors and iterations are determined that use an array element at its fixed position in a statement. This makes it possible to obtain the initial data distribution and also information on the data volume for every processor and on the structure of required communications.
Data distribution functions are introduced. They are matched with scheduling functions. The processors and iterations are determined that use an array element at its fixed position in a statement. This makes it possible to obtain the initial data distribution and also information on the data volume for every processor and on the structure of required communications. Keywords: parallel computing, parallelization of algorithms, affine loop nest, parallel computer with distributed memory, optimization of communications.
Data distribution functions are introduced. They are matched with scheduling functions. The processors and iterations are determined that use an array element at its fixed position in a statement. This makes it possible to obtain the initial data distribution and also information on the data volume for every processor and on the structure of required communications.[PUBLICATION ABSTRACT]
Audience Academic
Author Adutskevich, E. V.
Likhoded, N. A.
Sikorsky, A. O.
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Cites_doi 10.1016/S0167-8191(96)00049-X
10.1016/0167-9260(95)00022-4
10.1006/jpdc.1996.0139
10.1007/BF01407835
10.1023/A:1025773814022
10.1007/s10559-006-0066-7
10.1007/978-3-540-78791-4_9
10.1142/S0129626494000259
10.1109/12.123395
10.1515/rnam.1995.10.3.279
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Keywords parallel computer with distributed memory
optimization of communications
parallel computing
affine loop nest
parallelization of algorithms
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References_xml – reference: DarteARobertYOn the alignment problemParallel Processing Letters199443259270143708210.1142/S0129626494000259
– reference: ShangWFortesJABIndependent partitioning of algorithms with uniform dependenciesIEEE Trans. on Computers199241219020610.1109/12.123395
– reference: E. V. Adutskevich and S. V. Bakhanovich, “Adaptation of algorithms for realization on distributed memory systems: Space-time localization and data distribution,” in: Proc. Intern. Sci. Conf. “Supercomputer systems and their application” (SSA’2004), Minsk, Republic of Belarus (2004), pp. 165–169.
– reference: A. W. Lim and M. S. Lam, “Communication-free parallelization via affine transformation,” in: Proc. 7th Workshop on Languages and Compilers for Parallel Computing, Springer (1994), pp. 92–106.
– reference: BondhugulaUBaskaranMKrishnamoorthySRamanujamJRountevASadayappanPAutomatic transformations for communication-minimized parallelization and locality optimization in the polyhedral modelLecture Notes in Computer Science2008495913214610.1007/978-3-540-78791-4_9
– reference: D. Bau, I. Kodukula, V. Kotluar, K. Pingali, and P. Stodghill, “Solving alignment using elementary linear algebra,” in: Proc. 7th Workshop on Languages and Compilers for Parallel Computing, Springer (1994), pp. 46–60.
– reference: A. W. Lim and M. S. Lam, “An affine partitioning algorithm to maximize parallelism and minimize communication,” in: Proc. 1st ACM SIGARCH Intern. Conf. on Supercomputing (1999), pp. 228–237.
– reference: J. Garcia, E. Ayguade, and J. Labarta, “A framework for integrating data alignment, distribution, and redistribution in distributed memory multiprocessors,” in: IEEE Transactions on Parallel and Distributed Systems, 12, No. 4, 416–430 (2001).
– reference: L. Pan, J. Xue, M. K. Lai, M. B. Dillencourt, and L. F. Bic, “Toward automatic data distribution for migrating computations,” in: Int. Conf. on Parallel Processing, Xian, China (2007).
– reference: FeautrierPSome efficient solutions to the affine scheduling problem. Part 1Intern. Journ. of Parallel Programming199221531334812374820783.9005010.1007/BF01407835
– reference: DionMRobertYMapping affine loop nestsParallel Computing199622101373139714235600875.6818710.1016/S0167-8191(96)00049-X
– reference: DionMRandriamaroCRobertYCompiling affine nested loops: How to optimize the residual communications after the alignment phase?J. Parallel and Distrib. Computing199630217618710.1006/jpdc.1996.0139
– reference: VoevodinVVInformation structure of sequential programsRuss. J. of Numerical Analysis and Math. Modeling199510327928613434770838.6801210.1515/rnam.1995.10.3.279
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SubjectTerms Algorithms
Analysis
Arrays
Artificial Intelligence
Computer memory
Control
Cybernetics
Data exchange
Distribution functions
Expenditures
Investigations
Mathematics
Mathematics and Statistics
Optimization
Parallel processing
Processor Architectures
Processors
Scheduling
Software Engineering/Programming and Operating Systems
Software–Hardware Systems
Studies
Systems analysis
Systems Theory
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