Performance of Hierarchical-matrix BiCGStab Solver on GPU Clusters

Ichitaro Yamazaki, Ahmad Abdelfattah, Akihiro Ida, Satoshi Ohshima, Stanimire Tomov, Rio Yokota, Jack Dongarra

    研究成果: Chapter in Book/Report/Conference proceedingConference contribution

    4 被引用数 (Scopus)

    抄録

    HACApK is a software package for solving dense linear systems of equations and is used in other software packages, like ppohBEM for solving boundary integral equations. To enable the solution of large-scale boundary value problems, HACApK hierarchically compresses the coefficient matrix and uses the BiConjugate Gradient Stabilized (BiCGStab) method for solving the linear system. To extend HACApK's capability, this paper outlines how we ported the HACApK linear solver onto GPU clusters. Though the potential of GPUS has been widely accepted in high-performance computing, it is still a challenge to utilize the GPUS for a solver, like HACApK, that requires fine-grained irregular computation and global communication. To utilize the GPUS, we integrated the variable-size batched GPU kernel that was recently released in the MAGMA software package. This is the first time the variable-size batched kernels were used in a solver or application code. We discuss several techniques to improve the performance of the batched kernel and demonstrate the effects of these techniques on two state-of-The-Art GPU clusters. For instance, with two 14-core Intel Xeon CPUs and four NVIDIA P100 GPUS per node, the GPU kernel obtained a solver speedup of 8× on one node and 4× on eight nodes. We also show that when the inter-GPU communication becomes significant, the solution time can be further reduced by a factor of 2× by carefully designing the communication layer with the underlying node architecture in mind.

    本文言語英語
    ホスト出版物のタイトルProceedings - 2018 IEEE 32nd International Parallel and Distributed Processing Symposium, IPDPS 2018
    出版社Institute of Electrical and Electronics Engineers Inc.
    ページ930-939
    ページ数10
    ISBN(印刷版)9781538643686
    DOI
    出版ステータス出版済み - 8 3 2018
    イベント32nd IEEE International Parallel and Distributed Processing Symposium, IPDPS 2018 - Vancouver, カナダ
    継続期間: 5 21 20185 25 2018

    出版物シリーズ

    名前Proceedings - 2018 IEEE 32nd International Parallel and Distributed Processing Symposium, IPDPS 2018

    その他

    その他32nd IEEE International Parallel and Distributed Processing Symposium, IPDPS 2018
    国/地域カナダ
    CityVancouver
    Period5/21/185/25/18

    All Science Journal Classification (ASJC) codes

    • 人工知能
    • コンピュータ ネットワークおよび通信
    • ハードウェアとアーキテクチャ
    • 情報システムおよび情報管理

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