Grasso, IvanKofler, KlausCosenza, BiagioFahringer, Thomas2017-10-242017-10-242013978-1-4503-1922-50362-1340https://depositonce.tu-berlin.de/handle/11303/6927http://dx.doi.org/10.14279/depositonce-6266In 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.en004 Datenverarbeitung; Informatikcode analysiscompilersgpuheterogeneous computingmachine learningruntime systemtask partitioningArticle