Abstract
In general, GPU accelerated GPGPU application results in much higher performance than CPU application. However, to be accelerated by GPU, users should have GPGPU-enabled computation resources like recent GPU or CPU in their local machine. In this paper, we proposed selective GPGPU off-loading framework named SeloGPU. SeloGPU not only supports remote off-loading for GPGPU application but also supports target node selection among multiple GPGPU-enabled computation resources. We also proposed four optimization techniques to reduce additional overhead owing to remote execution. We implemented SeloGPU using OpenCL which is open standard heterogeneous language. The experimental result shows SeloGPU can choose best target node based on the history of execution information. The four optimization techniques reduce ~87% of network transmission overhead.
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Park, S., Ma, J., Park, C. (2013). SeloGPU: A Selective Off-Loading Framework for High Performance GPGPU Execution. In: Malyshkin, V. (eds) Parallel Computing Technologies. PaCT 2013. Lecture Notes in Computer Science, vol 7979. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39958-9_22
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DOI: https://doi.org/10.1007/978-3-642-39958-9_22
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