Adaptive Task Size Control on High Level Programming for GPU/CPU Work Sharing

Abstract : On the work sharing among GPUs and CPU cores on GPU equipped clusters, it is a critical issue to keep load balance among these heterogeneous computing resources. We have been developing a runtime system for this problem on PGAS language named XcalableMP- dev/StarPU [1]. Through the development, we found the necessity of adaptive load balancing for GPU/CPU work sharing to achieve the best performance for various application codes. In this paper, we enhance our language system XcalableMP-dev/StarPU to add a new feature which can control the task size to be assigned to these heterogeneous resources dynamically during application execution. As a result of performance evaluation on several benchmarks, we confirmed the proposed feature correctly works and the performance with heterogeneous work sharing provides up to about 40% higher performance than GPU-only utilization even for relatively small size of problems.
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Communication dans un congrès
The 2013 International Symposium on Advances of Distributed and Parallel Computing (ADPC 2013), Dec 2013, Vietri sul Mare, Italy. 2013
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https://hal.inria.fr/hal-00920915
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Soumis le : jeudi 19 décembre 2013 - 14:03:16
Dernière modification le : jeudi 10 septembre 2015 - 01:06:53
Document(s) archivé(s) le : jeudi 20 mars 2014 - 11:46:51

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  • HAL Id : hal-00920915, version 1

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Tetsuya Odajima, Taisuke Boku, Mitsuhisa Sato, Toshihiro Hanawa, Yuetsu Kodama, et al.. Adaptive Task Size Control on High Level Programming for GPU/CPU Work Sharing. The 2013 International Symposium on Advances of Distributed and Parallel Computing (ADPC 2013), Dec 2013, Vietri sul Mare, Italy. 2013. <hal-00920915>

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