title={Adaptive Task Size Control on High Level Programming for GPU/CPU Work Sharing},
author={Odajima, Tetsuya and Boku, Taisuke and Sato, Mitsuhisa and Hanawa, Toshihiro and Kodama, Yuetsu and Namyst, Raymond and Thibault, Samuel and Aumage, Olivier},
language={Anglais},
affiliation={Graduate School of Systems and Information Engineering [Tsukuba] , Center for Computational Sciences [Tsukuba] – CCS , Graduate School for Systems and Information Engineering [Tsukuba] , Laboratoire Bordelais de Recherche en Informatique – LaBRI , RUNTIME – INRIA Bordeaux – Sud-Ouest},
booktitle={The 2013 International Symposium on Advances of Distributed and Parallel Computing (ADPC 2013)},
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.
