2013Procedia Computer ScienceOpen access

Throughput Constrained Parallelism Reduction in Cyclo-static Dataflow Applications

Sergiu Carpov, Loïc Cudennec, Renaud Sirdey

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Abstract

This paper deals with semantics-preserving parallelism reduction methods for cyclo-static dataflow applications. Parallelism reduction is the process of equivalent actors fusioning. The principal objectives of parallelism reduction are to decrease the memory footprint of an application and to increase its execution performance. We focus on parallelism reduction methodolo- gies constrained by application throughput. A generic parallelism reduction methodology is introduced. Experimental results are provided for asserting the performance of the proposed method.

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What this paper is about

This paper deals with semantics-preserving parallelism reduction methods for cyclo-static dataflow applications. Parallelism reduction is the process of equivalent actors fusioning. The principal objectives of parallelism reduction are to decrease the memory footprint of an application and to increase its execution performance. We focus on parallelism reduction methodolo- gies constrained by application throughput. A generic parallelism reduction methodology is introduced. Experimental results are provided for asserting the performance of the proposed method.

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Available abstract

This paper deals with semantics-preserving parallelism reduction methods for cyclo-static dataflow applications. Parallelism reduction is the process of equivalent actors fusioning. The principal objectives of parallelism reduction are to decrease the memory footprint of an application and to increase its execution performance. We focus on parallelism reduction methodolo- gies constrained by application throughput. A generic parallelism reduction methodology is introduced. Experimental results are provided for asserting the performance of the proposed method.

Key concepts: Computer science, Dataflow, Parallel computing, Throughput, Parallelism (grammar), Reduction (mathematics), Data parallelism, Task parallelism

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