The Community for Technology Leaders
Proceedings of the 22nd International Conference on Parallel Architectures and Compilation Techniques (2011)
Galveston, Texas USA
Oct. 10, 2011 to Oct. 14, 2011
ISSN: 1089-795X
ISBN: 978-0-7695-4566-0
pp: 181-182
ABSTRACT
Auto-tuning has emerged as an important practical method for creating highly optimized code. However, the growing complexity of architectures and applications has resulted in a prohibitively large search space that preclude empirical auto-tuning. Here, we focus on the challenge to auto-tuning presented by applications that require auto-tuning of not just a small number of distinct kernels, but a large number of kernels that exhibit similar computation and memory access characteristics and require optimization over similar problem spaces. We propose an auto-tuning method for tensor contraction functions on GPUs, based on parameterized micro-benchmarks. Using our parameterized micro-benchmarking approach, we obtain a speedup of up to 2 over the version that used default optimizations without auto-tuning.
INDEX TERMS
auto-tuning, GPU, optimization
CITATION
Wenjing Ma, Sriram Krishnamoorthy, Gagan Agrawal, "Parameterized Micro-benchmarking: An Auto-tuning Approach for Complex Applications", Proceedings of the 22nd International Conference on Parallel Architectures and Compilation Techniques, vol. 00, no. , pp. 181-182, 2011, doi:10.1109/PACT.2011.30
160 ms
(Ver 3.3 (11022016))