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
  • [1] Reproduced Computational Results Report for "Ginkgo: A Modern Linear Operator Algebra Framework for High Performance Computing"
    Balos, Cody J.
    ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE, 2022, 48 (01):
  • [2] Chemora: A PDE-Solving Framework for Modern High-Performance Computing Architectures
    Schnetter, Erik
    Blazewicz, Marek
    Brandt, Steven R.
    Koppelman, David M.
    Loeffier, Frank
    COMPUTING IN SCIENCE & ENGINEERING, 2015, 17 (02) : 53 - 64
  • [3] Accelerating R with high performance linear algebra libraries
    Oancea, Bogdan
    Andrei, Tudorel
    Dragoescu, Raluca Mariana
    ROMANIAN STATISTICAL REVIEW, 2015, (03) : 109 - 117
  • [4] A High Performance Computing Framework for Data Mining
    Goyal, Navneet
    Balasubramaniam, Sundar
    Goyal, Poonam
    Islam, Saiyedul
    Sati, Mohit
    2016 23RD IEEE INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE COMPUTING WORKSHOPS (HIPCW 2016), 2016, : 11 - 18
  • [5] A framework for comparing high performance computing technologies
    Duran, Randall E.
    Chen, Ding
    Saraswat, Rishi
    Hallmark, Aaron
    INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING, 2014, 9 (1-2) : 119 - 129
  • [6] Middleware in modern high performance computing system architectures
    Engelmann, Christian
    Ong, Hong
    Scott, Stephen L.
    COMPUTATIONAL SCIENCE - ICCS 2007, PT 2, PROCEEDINGS, 2007, 4488 : 784 - +
  • [7] Lightweight distributed computing framework for orchestrating high performance computing and big data
    Ince, Muhammed Numan
    Gunay, Melih
    Ledet, Joseph
    TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, 2022, 30 (04) : 1571 - 1585
  • [8] The Design and Performance of Batched BLAS on Modern High-Performance Computing Systems
    Dongarra, Jack
    Hammarling, Sven
    Higham, Nicholas J.
    Relton, Samuel D.
    Valero-Lara, Pedro
    Zounon, Mawussi
    INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE (ICCS 2017), 2017, 108 : 495 - 504
  • [9] A Grid Computing Framework for High-Performance Medical Imaging
    Manana Guichon, Gabriel
    Romero Castro, Eduardo
    IX INTERNATIONAL SEMINAR ON MEDICAL INFORMATION PROCESSING AND ANALYSIS, 2013, 8922
  • [10] Introducing a Reliability Analysis Framework for High Performance Computing Environments
    Sharma, S.
    Clark, A. D.
    2018 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM), 2018, : 1131 - 1138