Programming heterogeneous parallel machines using refactoring and Monte–Carlo tree search.
Brown, Christopher; Janjic, Vladimir; Goli, M.; McCall, J.
This paper presents a new technique for introducing and tuning parallelism for heterogeneous shared-memory systems (comprising a mixture of CPUs and GPUs), using a combination of algorithmic skeletons (such as farms and pipelines), Monte–Carlo tree search for deriving mappings of tasks to available hardware resources, and refactoring tool support for applying the patterns and mappings in an easy and effective way. Using our approach, we demonstrate easily obtainable, significant and scalable speedups on a number of case studies showing speedups of up to 41 over the sequential code on a 24-core machine with one GPU. We also demonstrate that the speedups obtained by mappings derived by the MCTS algorithm are within 5–15% of the best-obtained manual parallelisation.
|Journal Article Type||Article|
|Journal||International journal of parallel programming|
|Peer Reviewed||Peer Reviewed|
|Institution Citation||BROWN, C. JANJIC, V., GOLI, M. and MCCALL, J. 2020. Programming heterogeneous parallel machines using refactoring and Monte–Carlo tree search. International journal of parallel programming [online], Online First. Available from: https://doi.org/10.1007/s10766-020-00665-z|
|Keywords||Heterogeneous parallel computing; Monte–Carlo tree search; Optimisations|
BROWN 2020 Programming heterogeneous
You might also like
Evolving interval-based representation for multiple classifier fusion.
WEC: weighted ensemble of text classifiers.
Multi-layer heterogeneous ensemble with classifier and feature selection.
Evolved ensemble of detectors for gross error detection.