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GPU Implementation of a Sophisticated Implicit Low-Order Finite Element Solver with FP21-32-64 Computation Using OpenACC

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Accelerator Programming Using Directives (WACCPD 2019)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 12017))

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Abstract

Accelerating applications with portability and maintainability is one of the big challenges in science and engineering. Previously, we have developed a fast implicit low-order three-dimensional finite element solver, which has a complicated algorithm including artificial intelligence and transprecision computing. In addition, all possible tunings for the target architecture were implemented; accordingly, the solver has inferior portability and maintainability. In this paper, we apply OpenACC to the solver. The directive-based implementation of OpenACC enables GPU computation to be introduced with a smaller developmental cost even for complex codes. In performance measurements on AI Bridging Cloud Infrastructure (ABCI), we evaluated that a reasonable speedup was attained on GPUs, given that the elapsed time of the entire solver was reduced to 1/14 of that on CPUs based on the original CPU implementation. Our proposed template to use transprecision computing with our custom FP21 data type is available to the public; therefore, it can provide a successful example for other scientific computing applications.

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Data Availability Statement

Summary of the Experiments Reported

We ran our built-from-scratch implicit solver for unstructured finite elements on AI bridging cloud infrastructure with PGI compiler and OpenMPI.

Artifact Availability

Software Artifact Availability: Some author-created software artifacts are NOT maintained in a public repository or are NOT available under an OSI-approved license.

Hardware Artifact Availability: There are no author-created hardware artifacts.

Data Artifact Availability: Some author-created data artifacts are NOT maintained in a public repository or are NOT available under an OSI-approved license.

Proprietary Artifacts: There are associated proprietary artifacts that are not created by the authors. Some author-created artifacts are proprietary.

List of URLs and/or DOIs where artifacts are available:

http://doi.org/10.6084/m9.figshare.11603382

http://www.data.jma.go.jp/svd/eqev/data/kyoshin/jishin/hyogo_nanbu/dat/H1171931.csv.

Details regarding baseline experimental setup, and modifications made for the paper are available at [26].

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Acknowledgement

Our results were obtained using Computational resource of AI Bridging Cloud Infrastructure (ABCI), National Institute of Advanced Industrial Science and Technology (AIST). We acknowledge support from Post K computer project (Priority Issue 3 - Development of integrated simulation systems for hazards and disasters induced by earthquakes and tsunamis), and Japan Society for the Promotion of Science (17K14719, 18H05239, 18K18873). Part of our results were obtained using the Summit at Oak Ridge Leadership Computing Facility, a US Department of Energy, Office of Science User Facility at Oak Ridge National Laboratory.

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Authors and Affiliations

  1. The University of Tokyo, Yayoi, Bunkyo, Tokyo, Japan

    Takuma Yamaguchi, Kohei Fujita, Tsuyoshi Ichimura & Maddegedara Lalith

  2. Center for Computational Science, RIKEN, Minatojima-minamimachi, Chuo, Kobe, Japan

    Kohei Fujita

  3. NVIDIA Corporation, Akasaka, Minato, Tokyo, Japan

    Akira Naruse

  4. Japan Agency for Marine-Earth Science and Technology, Kanazawa, Yokohama, Kanagawa, Japan

    Muneo Hori

Authors
  1. Takuma Yamaguchi
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  2. Kohei Fujita
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  3. Tsuyoshi Ichimura
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  4. Akira Naruse
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  5. Maddegedara Lalith
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  6. Muneo Hori
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Corresponding author

Correspondence to Takuma Yamaguchi .

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Editors and Affiliations

  1. RWTH Aachen University, Aachen, Germany

    Sandra Wienke

  2. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Sridutt Bhalachandra

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Yamaguchi, T., Fujita, K., Ichimura, T., Naruse, A., Lalith, M., Hori, M. (2020). GPU Implementation of a Sophisticated Implicit Low-Order Finite Element Solver with FP21-32-64 Computation Using OpenACC. In: Wienke, S., Bhalachandra, S. (eds) Accelerator Programming Using Directives. WACCPD 2019. Lecture Notes in Computer Science(), vol 12017. Springer, Cham. https://doi.org/10.1007/978-3-030-49943-3_1

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  • DOI: https://doi.org/10.1007/978-3-030-49943-3_1

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