Self-Scaling Stream Processing: a Bio-Inspired Approach to Resource Allocation through Dynamic Task Replication

被引：1

作者：

Mudry, Pierre-Andre ^{[1
]}

Tempesti, Gianluca ^{[2
]}

机构：

[1] Ecole Polytech Fed Lausanne, Stn 14, CH-1015 Lausanne, Switzerland

[2] Univ York, Dept Elect, York YO10 5DD, N Yorkshire, England

来源：

PROCEEDINGS OF THE 2009 NASA/ESA CONFERENCE ON ADAPTIVE HARDWARE AND SYSTEMS | 2009年

关键词：

Multiprocessor systems; Multiple data stream architectures (Multiprocessors); Load balancing and task assignment; Cellular architecture; Reconfigurable hardware; Adaptable architectures;

D O I：

10.1109/AHS.2009.25

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

In this article, we show how the use of a bio-inspired dynamic task replication algorithm, in the context of stream processing, can be used to significantly improve the performance of embedded programs. We also show that this programming methodology, which is not tied to a particular implementation, can also be used as an heuristic for task mapping in the context of embedded multiprocessors systems. The technique was applied to a 36-processor system implemented on a scalable mesh of FPGAS for two different case studies: for AES encryption, it resulted in a ten-fold speedup compared to a static implementation, while for MJPEG compression a throughput multiplication of 11 was obtained.

引用

页码：353 / +

页数：3

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