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A compression-based memory-efficient optimization for out-of-core GPU stencil computation

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Abstract

A code for out-of-core stencil computation manages data that exceeds the memory capacity of a GPU. However, such a code necessitates frequent data transfers between the CPU and GPU, which often impede overall performance. In this work, we propose a compression-based, memory-efficient method to accelerate out-of-core stencil codes. First, an on-the-fly compression technique is integrated into the out-of-core computation to reduce CPU-GPU data transfers. Secondly, a single-working-buffer strategy is employed to reduce the GPU memory usage, enabling more data to be stored on the GPU for reuse, resulting in increased temporal blocking steps. Experimental results demonstrate that the proposed method significantly reduces the GPU memory usage by 21%, thereby creating space for doubling the number of temporal blocking steps compared to the codes without compression. Our proposed method has shown to help the high-order, data-transfer-bound stencil codes achieve speedups up to \(2.09\times \) for single-precision floating-point format and up to \(1.92\times \) for double-precision floating-point format on an NVIDIA Tesla V100 GPU in comparison with the codes without compression.

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Data Availability

Source codes of this work are available at https://github.com/mlvssc/compstencil.

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Acknowledgements

This study was supported in part by the Japan Society for the Promotion of Science KAKENHI under grant 20K21794, grants from the National Natural Science Foundation of China 61902045 and 62172067, Chongqing High-Tech Research Key Program cstc2021jcyj-msxmX0981, cstc2021jcyj-msxmX0530, and cstb2022nscq-msx0601.

Funding

KAKENHI grant 20K21794 from the Japan Society for the Promotion of Science, grants from the National Natural Science Foundation of China 61902045 and 62172067, Chongqing High-Tech Research Key Program cstc2021jcyj-msxmX0981, cstc2021jcyj-msxmX0530, and cstb2022nscq-msx0601.

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Authors and Affiliations

  1. College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, 2 Chongwen Rd., Nan’an Dist., 400065, Chongqing, China

    Jingcheng Shen, Linbo Long & Xin Deng

  2. Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka, 565-0871, Japan

    Masao Okita & Fumihiko Ino

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  1. Jingcheng Shen
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  2. Linbo Long
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  3. Xin Deng
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Contributions

JS conducted the experiments and wrote the manuscript. LL has done a considerable amount of work to evaluate the work and to help revise the manuscript. FI partially supervised this study. XD and MO gave revising suggestions. All authors have reviewed the manuscript.

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Correspondence to Linbo Long.

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Shen, J., Long, L., Deng, X. et al. A compression-based memory-efficient optimization for out-of-core GPU stencil computation. J Supercomput 79, 11055–11077 (2023). https://doi.org/10.1007/s11227-023-05103-8

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