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Methods for Partitioning Data to Improve Parallel Execution Time for Sorting on Heterogeneous Clusters

Christophe Cérin 1 Jean-Christophe Dubacq 1, * Jean-Louis Roch 2
* Corresponding author
2 MOAIS - PrograMming and scheduling design fOr Applications in Interactive Simulation
ID-IMAG - Informatique et Distribution, Inria Grenoble - Rhône-Alpes, UJF - Université Joseph Fourier - Grenoble 1
Abstract : The aim of the paper is to introduce general techniques in order to optimize the parallel execution time of sorting on a distributed architectures with processors of various speeds. Such an application requires a partitioning step. For uniformly related processors (processors speeds are related by a constant factor), we develop a constant time technique for mastering processor load and execution time in an heterogeneous environment and also a technique to deal with unknown cost functions. For non uniformly related processors, we use a technique based on dynamic programming. Most of the time, the solutions are in O(p) (p is the number of processors), independent of the problem size n. Consequently, there is a small overhead regarding the problem we deal with but it is inherently limited by the knowing of time complexity of the portion of code following the partitioning.
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https://hal.archives-ouvertes.fr/hal-00084822
Contributor : Jean-Christophe Dubacq <>
Submitted on : Monday, July 10, 2006 - 4:39:53 PM
Last modification on : Monday, December 14, 2020 - 4:34:17 PM
Long-term archiving on: : Monday, April 5, 2010 - 10:12:05 PM

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Christophe Cérin, Jean-Christophe Dubacq, Jean-Louis Roch. Methods for Partitioning Data to Improve Parallel Execution Time for Sorting on Heterogeneous Clusters. GPC 2006, 2006, France. pp.175-186. ⟨hal-00084822⟩

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