Mapping parallelism to multi-cores: a machine learning based approach

The efficient mapping of program parallelism to multi-core processors is highly dependent on the underlying architecture. This paper proposes a portable and automatic compiler-based approach to mapping such parallelism using machine learning. It develops two predictors: a data sensitive and a data insensitive predictor to select the best mapping for parallel programs. They predict the number of threads and the scheduling policy for any given program using a model learnt off-line. By using low-cost profiling runs, they predict the mapping for a new unseen program across multiple input data sets. We evaluate our approach by selecting parallelism mapping configurations for OpenMP programs on two representative but different multi-core platforms (the Intel Xeon and the Cell processors). Performance of our technique is stable across programs and architectures. On average, it delivers above 96% performance of the maximum available on both platforms. It achieve, on average, a 37% (up to 17.5...

• Type: Conference
• Year: 2009
• Where: PPOPP
• Authors: Zheng Wang, Michael F. P. O'Boyle
• Keywords: OpenMP Runtime Default, Parallel Computing, Parallelism Mapping Configurations, Performance Keywords Compiler, PPOPP 2009