A Data-Centric Directive-Based Framework to Accelerate Out-of-Core Stencil Computation on a GPU-Reference-Cited by-同舟云学术

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A Data-Centric Directive-Based Framework to Accelerate Out-of-Core Stencil Computation on a GPU

Published:2020-12-01 Issue:12 Volume:E103.D Page:2421-2434
ISSN:0916-8532
Container-title:IEICE Transactions on Information and Systems
language:en
Short-container-title:IEICE Trans. Inf. & Syst.

Author:

SHEN Jingcheng¹,INO Fumihiko¹,FARRÉS Albert²,HANZICH Mauricio²

Affiliation:

1. Graduate School of Information Science and Technology, Osaka University

2. Barcelona Supercomputing Center

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

https://www.jstage.jst.go.jp/article/transinf/E103.D/12/E103.D_2020PAP0014/_pdf

Reference38 articles.

1. [1] M. Serpa, E. Cruz, M. Diener, A. Krause, A. Farrés, C. Rosas, J. Panetta, M. Hanzich, and P. Navaux, “Strategies to Improve the Performance of a Geophysics Model for Different Manycore Systems,” Proc. 2017 International Symposium on Computer Architecture and High Performance Computing Workshops (SBAC-PADW), pp.49-54, Campinas, 2017. 10.1109/sbac-padw.2017.17

2. [2] A. Farres, C. Rosas, M. Hanzich, M. Jordà, and A. Peña, “Performance Evaluation of Fully Anisotropic Elastic Wave Propagation on NVIDIA Volta GPUs,” Proc. 81st EAGE Conference and Exhibition, 2019. 10.3997/2214-4609.201901307

3. [3] S. Adams, J. Payne, and R. Boppana, “Finite Difference Time Domain (FTDT) Simulations Using Graphics Processors,” Proc. High Performance Computing Modernization Program Users Group Conf. (HPCMP-UGC), pp.334-338, Pittsburgh, PA, 2007. 10.1109/hpcmp-ugc.2007.34

4. [4] K. Ikeda, F. Ino, and K. Hagihara, “Efficient Acceleration of Mutual Information Computation for Nonrigid Registration using CUDA,” IEEE J. Biomed. Health Inform., vol.18, no.3, pp.956-968, 2014. 10.1109/jbhi.2014.2310745

5. [5] S. Tabik, M. Peemen, and L. Romero, “A tuning approach for iterative multiple 3d stencil pipeline on GPUs: Anisotropic Nonlinear Diffusion algorithm as case study,” The Journal of Supercomputing, vol.74, no.4, pp.1580-1608, 2018. 10.1007/s11227-017-2184-6

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A compression-based memory-efficient optimization for out-of-core GPU stencil computation;The Journal of Supercomputing;2023-02-20

2. Accelerating Imbalanced Many-to-Many Communication with Systematic Delay Insertion;Parallel and Distributed Computing, Applications and Technologies;2023

3. Accelerating GPU-Based Out-of-Core Stencil Computation with On-the-Fly Compression;Parallel and Distributed Computing, Applications and Technologies;2022

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