With heterogeneous computing becoming mainstream, researchers and software vendors have been trying to exploit the best of the underlying architectures like GPUs or CPUs to enhance performance. Parallel programming models play a crucial role in achieving this enhancement. One such model is OpenCL, a parallel computing API for cross platform computations targeting heterogeneous architectures. However, OpenCL is a low-level programming language, therefore it can be time consuming to directly develop OpenCL code. To address this shortcoming, OpenCL has been integrated with OmpSs, a task-based programming model to provide abstraction to the user thereby reducing programmer effort. OmpSs-OpenCL programming model deals with a single OpenCL device either a CPU or a GPU. In this paper, we upgrade OmpSs-OpenCL programming model by supporting parallel execution of tasks across multiple CPU-GPU heterogeneous platforms. We discuss the design of the programming model along with its asynchronous runtime system. We investigated scalability of four OmpSs-OpenCL benchmarks across 4 GPUs gaining speedup of up to 4x. Further, in order to achieve effective utilization of the computing resources, we present static and work-stealing scheduling techniques. We show results of parallel execution of applications using OmpSs-OpenCL model and use heterogeneous workloads to evaluate our scheduling techniques on a heterogeneous CPU-GPU platform.
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