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GStream: A General-Purpose Data Streaming Framework on GPU Clusters

Yongpeng Zhang, Frank Mueller
Dept. of Computer Science, North Carolina State University, Raleigh, NC 27695-7534
International Conference on Parallel Processing (ICPP), 2011

@inproceedings{zhang2011gstream,

   title={GStream: A General-Purpose Data Streaming Framework on GPU Clusters},

   author={Zhang, Y. and Mueller, F.},

   booktitle={Parallel Processing (ICPP), 2011 International Conference on},

   pages={245–254},

   year={2011},

   organization={IEEE}

}

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Emerging accelerating architectures, such as GPUs, have proved successful in providing significant performance gains to various application domains. However, their viability to operate on general streaming data is still ambiguous. In this paper, we propose GStream, a general-purpose, scalable data streaming framework on GPUs. The contributions of GStream are as follows: (1) We provide powerful, yet concise language abstractions suitable to describe conventional algorithms as streaming problems. (2)We project these abstractions onto GPUs to fully exploit their inherent massive data parallelism.(3) We demonstrate the viability of streaming on accelerators. Experiments show that the proposed framework provides flexibility, programmability and performance gains for various benchmarks from a collection of domains, including but not limited to data streaming, data parallel problems and numerical codes.
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