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CHPS: An Environment for Collaborative Execution on Heterogeneous Desktop Systems

Aleksandar Ilic, Leonel Sousa
INESC-ID, IST/TU Lisbon, Rua Alves Redol 9, Lisbon, 1000-029, Portugal
International Journal of Networking and Computing, Vol 1, No 1, 2011

@article{ilic2011chps,

   title={CHPS: An Environment for Collaborative Execution on Heterogeneous Desktop Systems},

   author={Ili{‘c}, A. and Sousa, L.},

   journal={International Journal of Networking and Computing},

   volume={1},

   number={1},

   pages={pp–96},

   year={2011}

}

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Modern commodity desktop computers equipped with multi-core Central Processing Units (CPUs) and specialized but programmable co-processors are capable of providing a remarkable computational performance. However, approaching this performance is not a trivial task as it requires the coordination of architecturally different devices for cooperative execution. Coordinating the use of the full set of processing units demands careful coalescing of diverse programing models and addressing the challenges imposed by the overall system complexity. In order to exploit the computational power of a heterogeneous desktop system, such as a platform consisting of a multi-core CPU and a Graphics Processing Unit (GPU), we propose herein a collaborative execution environment that allows to cooperatively execute a single application by exploiting both task and data parallelism. In particular, the proposed environment is able to use the different native programming models according to the device type, e.g., the application processing interfaces such as OpenMP for the CPU and Compute Unified Device Architecture (CUDA) for the GPU devices. The data and task level parallelism is exploited for both types of processors by relying on the task description scheme defined by the proposed environment. The relevance of the proposed approach is demonstrated in a heterogeneous system with a quad-core CPU and a GPU for linear algebra and digital signal processing applications. We obtain significant performance gains in comparison to both single core and multi-core executions when computing matrix multiplication and Fast Fourier Transform (FFT).
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