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Towards Utilizing Remote GPUs for CUDA Program Execution

Xiaonan Ji, Spencer Davis, Erikson Hardesty, Xu Liang, Sabuj Saha, Hai Jiang
Department of Computer Science, Ohio State University, Columbus, Ohio 43210, USA
WorldComputing, 2011

@article{ji2011towards,

   title={Towards Utilizing Remote GPUs for CUDA Program Execution},

   author={Ji, X. and Davis, S. and Erikson Hardesty, X.L. and Saha, S. and Jiang, H.},

   year={2011}

}

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The modern CPU has been designed to accelerate serial processing as much as possible. Recently, GPUs have been exploited to solve large parallelizable problems. As fast as a GPU is for general purpose massively parallel computing, some problems require an even larger scale of parallelism and pipelining. However, it has been difficult to scale algorithms beyond a local computer and distribute workloads among multiple computers housing GPUs. This paper proposes a Remote Kernel Launch (RKL) approach to transfer the kernel parts from a local machine to remote GPU servers. A lexical analyzer is used to identify and extract the kernels from local programs. The extracted kernel can then be distributed and executed on remote GPUs. A dynamic mapping scheme is explored to balance workloads among nodes. This approach allows a program to run optimally on a range of hardware configurations by eliminating the need to program for a specific environment. The experimental results demonstrate the effectiveness of RKL.
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