Eddy Z. Zhang
Xipeng Shen
ABSTRACT
The performance of Graphic Processing Units (GPU) is sensitive to irregular memory references. A recent study shows the promise of eliminating irregular references through runtime thread-data remapping. However, how to efficiently determine the optimal mapping is yet an open question. This paper presents some initial exploration to the question, especially in the dimension of data layout optimization. It describes three algorithms to compute or approximate optimal data layouts for GPU. These algorithms exhibit a spectrum of tradeoff among the space cost, time cost, and quality of the resulting data layouts.
- E. Zhang, Y. Jiang, Z. Guo, K. Tian, and X. Shen. On-the-fly elimination of dynamic irregularities for gpu computing. Technical Report WM-CS-2010-07, CS, College of William and Mary, 2010.Google Scholar
- E. Z. Zhang, Y. Jiang, Z. Guo, K. Tian, and X. Shen. On-the-fly elimination of dynamic irregularities for gpu computing. In ASPLOS, 2011. Google ScholarDigital Library
- Y. Zhong, M. Orlovich, X. Shen, and C. Ding. Array regrouping and structure splitting using whole-program reference affinity. In Proceedings of ACM SIGPLAN Conference on Programming Language Design and Implementation, pages 255--266, June 2004. Google ScholarDigital Library
Index Terms
- A study towards optimal data layout for GPU computing
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