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Automatic Mapping for OpenCL-Programs on CPU/GPU Heterogeneous Platforms

 
: Moren, Konrad; Göhringer, Diana

:

Shi, Y.:
Computational Science – ICCS 2018 : 18th International Conference, Wuxi, China, June 11-13, 2018, Proceedings, Part II
Cham: Springer International Publishing, 2018 (Lecture Notes in Computer Science 10861)
ISBN: 978-3-319-93701-4
ISBN: 978-3-319-93700-7
S.301-314
International Conference on Computational Science (ICCS) <18, 2018, Wuxi>
Englisch
Konferenzbeitrag
Fraunhofer IOSB ()
Code analysis; Compilers; Heterogeneous computing; Machine learning; OpenCL; Workload scheduling

Abstract
Heterogeneous computing systems with multiple CPUs and GPUs are increasingly popular. Today, heterogeneous platforms are deployed in many setups, ranging from low-power mobile systems to high performance computing systems. Such platforms are usually programmed using OpenCL which allows to execute the same program on different types of device. Nevertheless, programming such platforms is a challenging job for most non-expert programmers. To enable an efficient application runtime on heterogeneous platforms, programmers require an efficient workload distribution to the available compute devices. The decision how the application should be mapped is non-trivial. In this paper, we present a new approach to build accurate predictive-models for OpenCL programs. We use a machine learning-based predictive model to estimate which device allows best application speed-up. With the LLVM compiler framework we develop a tool for dynamic code-feature extraction. We demonstrate the effectiveness of our novel approach by applying it to different prediction schemes. Using our dynamic feature extraction techniques, we are able to build accurate predictive models, with accuracies varying between 77% and 90%, depending on the prediction mechanism and the scenario. We evaluated our method on an extensive set of parallel applications. One of our findings is that dynamically extracted code features improve the accuracy of the predictive-models by 6.1% on average (maximum 9.5%) as compared to the state of the art.

: http://publica.fraunhofer.de/dokumente/N-510135.html