GINKGO: A Modern Linear Operator Algebra Framework for High Performance Computing

被引:33
|
作者
Anzt, Hartwig [1 ,2 ]
Cojean, Terry [1 ]
Flegar, Goran [3 ]
Gobel, Fritz [1 ]
Grutzmacher, Thomas [1 ]
Nayak, Pratik [1 ]
Ribizel, Tobias [1 ]
Tsai, Yuhsiang Mike [1 ]
Quintana-Orti, Enrique S. [4 ]
机构
[1] Karlsruhe Inst Technol, Hermann von Helmholtz Pl 1, D-76344 Eggenstein Leopoldshafen, Germany
[2] Univ Tennessee, Innovat Comp Lab, Knoxville, TN 37996 USA
[3] Univ Jaume 1, Av Vicent Sos Baynat, Castellon De La Plana 12071, Spain
[4] Univ Politecn Valencia, Camino Vera, Valencia 46022, Spain
来源
ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE | 2022年 / 48卷 / 01期
基金
欧盟地平线“2020”;
关键词
High performance computing; healthy software lifecycle; multi-core and manycore architectures;
D O I
10.1145/3480935
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
In this article, we present GINKGO, a modern C++ math library for scientific high performance computing. While classical linear algebra libraries act on matrix and vector objects, Gnswo's design principle abstracts all functionality as linear operators," motivating the notation of a "linear operator algebra library" GINKGO'S current focus is oriented toward providing sparse linear algebra functionality for high performance graphics processing unit (GPU) architectures, but given the library design, this focus can be easily extended to accommodate other algorithms and hardware architectures. We introduce this sophisticated software architecture that separates core algorithms from architecture-specific backends and provide details on extensibility and sustainability measures. We also demonstrate GINKGO'S usability by providing examples on how to use its functionality inside the MFEM and deal.ii finite element ecosystems. Finally, we offer a practical demonstration of GINKGO'S high performance on state-of-the-art GPU architectures.
引用
收藏
页数:33
相关论文
共 50 条
  • [21] A high-performance computing framework for Monte Carlo ocean color simulations
    Kajiyama, Tamito
    D'Alimonte, Davide
    Cunha, Jose C.
    CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2017, 29 (04):
  • [22] An Efficient Energy Consumption Prediction Framework for High Performance Computing Cluster Jobs
    Lou, Yantao
    Wang, Jibin
    Feng, Shoupeng
    Yu, Xian
    2024 5TH INTERNATIONAL CONFERENCE ON COMPUTER ENGINEERING AND APPLICATION, ICCEA 2024, 2024, : 259 - 265
  • [23] A Scalable Runtime Fault Localization Framework for High-Performance Computing Systems
    Gao, Jian
    Wei, Hongmei
    Yu, Kang
    Qing, Peng
    INTERNATIONAL JOURNAL OF PARALLEL PROGRAMMING, 2018, 46 (04) : 749 - 761
  • [24] A Scalable Runtime Fault Localization Framework for High-Performance Computing Systems
    Jian Gao
    Hongmei Wei
    Kang Yu
    Peng Qing
    International Journal of Parallel Programming, 2018, 46 : 749 - 761
  • [25] High Performance Computing on the Cloud via HPC plus Cloud software framework
    Balakrishnan, Suresh Reuben
    Veeramanii, Shanmugam
    Leong, John Alan
    Murray, Lain
    Sidhu, Amandeep S.
    2016 FIFTH INTERNATIONAL CONFERENCE ON ECO-FRIENDLY COMPUTING AND COMMUNICATION SYSTEMS (ICECCS), 2016, : 48 - 52
  • [26] Power grid simulation applications developed using the GridPACK™ high performance computing framework
    Jin, Shuangshuang
    Chen, Yousu
    Diao, Ruisheng
    Huang, Zhenyu
    Perkins, William
    Palmer, Bruce
    ELECTRIC POWER SYSTEMS RESEARCH, 2016, 141 : 22 - 30
  • [27] Distributed High-Performance Computing Framework for Modeling and Inversion of Geophysical Well Logs
    Polyakov, V.
    Kocian, R.
    Omeragic, D.
    Habashy, T.
    PROCEEDINGS OF THE FIRST INTERNATIONAL CONFERENCE ON PARALLEL, DISTRIBUTED AND GRID COMPUTING FOR ENGINEERING, 2009, (90): : 374 - 392
  • [28] GridPACKTM: A framework for developing power grid simulations on high-performance computing platforms
    Palmer, Bruce
    Perkins, William
    Chen, Yousu
    Jin, Shuangshuang
    Callahan, David
    Glass, Kevin
    Diao, Ruisheng
    Rice, Mark
    Elbert, Stephen
    Vallem, Mallikarjuna
    Huang, Zhenyu
    INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS, 2016, 30 (02): : 223 - 240
  • [29] A Framework for Multitasking Data-Intensive Management Services in High Performance Computing Environments
    Kulasekaran, Sivakumar
    Esteva, Maria
    Trelogan, Jessica
    Liu, Si
    2015 IEEE FIRST INTERNATIONAL CONFERENCE ON BIG DATA COMPUTING SERVICE AND APPLICATIONS (BIGDATASERVICE 2015), 2015, : 333 - 340
  • [30] A Distributed Cloud Resource Management Framework for High-Performance Computing (HPC) Applications
    Govindarajan, Kannan
    Kumar, Vivekanandan Suresh
    Somasundaram, Thamarai Selvi
    2016 EIGHTH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING (ICOAC), 2017, : 1 - 6