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2011 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
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.
auto-tuning, GPU, optimization

G. Agrawal, S. Krishnamoorthy and W. Ma, "Parameterized Micro-benchmarking: An Auto-tuning Approach for Complex Applications," 2011 International Conference on Parallel Architectures and Compilation Techniques(PACT), Galveston, Texas USA, 2011, pp. 181-182.
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