Partitioning streaming parallelism for multi-cores: a machine learning based approach
University of Edinburgh, Edinburgh, United Kingdom
In Proceedings of the 19th international conference on Parallel architectures and compilation techniques (2010), PACT ’10, pp. 307-318
@conference{wang2010partitioning,
title={Partitioning streaming parallelism for multi-cores: a machine learning based approach},
author={Wang, Z. and O’Boyle, M.F.P.},
booktitle={Proceedings of the 19th international conference on Parallel architectures and compilation techniques},
pages={307–318},
year={2010},
organization={ACM}
}
Stream based languages are a popular approach to expressing parallelism in modern applications. The efficient mapping of streaming parallelism to multi-core processors is, however, highly dependent on the program and underlying architecture. We address this by developing a portable and automatic compiler-based approach to partitioning streaming programs using machine learning. Our technique predicts the ideal partition structure for a given streaming application using prior knowledge learned off-line. Using the predictor we rapidly search the program space (without executing any code) to generate and select a good partition. We applied this technique to standard StreamIt applications and compared against existing approaches. On a 4-core platform, our approach achieves 60% of the best performance found by iteratively compiling and executing over 3000 different partitions per program. We obtain, on average, a 1.90x speedup over the already tuned partitioning scheme of the StreamIt compiler. When compared against a state-of-the-art analytical, model-based approach, we achieve, on average, a 1.77x performance improvement. By porting our approach to a 8-core platform, we are able to obtain 1.8x improvement over the StreamIt default scheme, demonstrating the portability of our approach.
February 8, 2011 by hgpu
