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Computer Science > Distributed, Parallel, and Cluster Computing

arXiv:2204.11315 (cs)
[Submitted on 24 Apr 2022]

Title:Compression-Based Optimizations for Out-of-Core GPU Stencil Computation

Authors:Jingcheng Shen, Xin Deng, Yifan Wu, Masao Okita, Fumihiko Ino
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Abstract:An out-of-core stencil computation code handles large data whose size is beyond the capacity of GPU memory. Whereas, such an code requires streaming data to and from the GPU frequently. As a result, data movement between the CPU and GPU usually limits the performance. In this work, compression-based optimizations are proposed. First, an on-the-fly compression technique is applied to an out-of-core stencil code, reducing the CPU-GPU memory copy. Secondly, a single working buffer technique is used to reduce GPU memory consumption. Experimental results show that the stencil code using the proposed techniques achieved 1.1x speed and reduced GPU memory consumption by 33.0\% on an NVIDIA Tesla V100 GPU.
Comments: 15 pages, 10 figures
Subjects: Distributed, Parallel, and Cluster Computing (cs.DC)
Cite as: arXiv:2204.11315 [cs.DC]
  (or arXiv:2204.11315v1 [cs.DC] for this version)
  https://doi.org/10.48550/arXiv.2204.11315

Submission history

From: Jingcheng Shen [view email]
[v1] Sun, 24 Apr 2022 16:14:42 UTC (3,556 KB)
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