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Abstract: It is well-known that mixing task and data parallelism to solve large computational applications often yields better speedups compared to either applying pure task parallelism or pure data parallelism. Typically, the applications are modeled in terms of a dependence graph of coarse-grain data-parallel tasks, called a data-parallel task graph. In this paper we present a new compile-time heuristic, named Critical Path Reduction (CPR), for scheduling data-parallel task graphs. Experimental results ... (Update)
Context of citations to this paper: More
.... Skeletons are embedded in a sequential language, hence being the only source of parallelism in such a program.However, SRB97,SY00,RNvGP01] showed that exploiting both task and data parallelism in a program yields better speedups than either pure task or pure data...
.... both task and data parallelism yields better speedups compared to either pure task parallelism or pure data parallelism [2, 7, 21, 18, 27]. In large applications, pure data parallelism is relatively limited, especially for irregular applications. Task parallelism, on the...
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3: Algorithmic Skeletons: Structured Management of Parallel Computation (context) - Cole - 1989
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BibTeX entry: (Update)
A. Radulescu, C. Nicolescu, A. van Gemund, and P.P.Jonker. Cpr: Mixed task and data parallel scheduling for distributed systems. In CDROM proceedings of The 15th International Parallel and Distributed Symposium. San Francisco, California, 2001. Best Paper Award. http://citeseer.ist.psu.edu/radulescu01cpr.html More
@inproceedings{ radulescucpr,
author = "Andrei Radulescu and Cristina Nicolescu and Arjan J. C. van Gemund and Pieter P. Jonker",
title = "{CPR}: Mixed Task and Data Parallel Scheduling for Distributed Systems",
pages = "39--41",
url = "citeseer.ist.psu.edu/radulescu01cpr.html" }
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http://www.ampl.com/cm/
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