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Automatic problem size sensitive task partitioning on heterogeneous parallel systems
Grasso, Ivan; Kofler, Klaus; Cosenza, Biagio; Fahringer, Thomas
In this paper we propose a novel approach which automatizes task partitioning in heterogeneous systems. Our framework is based on the Insieme Compiler and Runtime infrastructure. The compiler translates a single-device OpenCL program into a multi-device OpenCL program. The runtime system then performs dynamic task partitioning based on an offline-generated prediction model. In order to derive the prediction model, we use a machine learning approach that incorporates static program features as well as dynamic, input sensitive features. Our approach has been evaluated over a suite of 23 programs and achieves performance improvements compared to an execution of the benchmarks on a single CPU and a single GPU only.
Published in: ACM SIGPLAN Notices, 10.1145/2442516.2442545, Association for Computing Machinery (ACM)
