Myths and legends in high-performance computing

被引:3
作者
Matsuoka, Satoshi [1 ]
Domke, Jens [2 ]
Wahib, Mohamed [3 ]
Drozd, Aleksandr [3 ]
Hoefler, Torsten [4 ]
机构
[1] RIKEN Ctr Computat Sci, Kobe, Hyogo, Japan
[2] RIKEN Ctr Computat Sci, Supercomp Performance Res Team, Kobe, Hyogo, Japan
[3] RIKEN Ctr Computat Sci, High Performance Artificial Intelligence Syst Res, Kobe, Hyogo, Japan
[4] ETH, Comp Sci, Zurich, Switzerland
关键词
Quantum; zettascale; deep learning; clouds; HPC myths;
D O I
10.1177/10943420231166608
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
In this thought-provoking article, we discuss certain myths and legends that are folklore among members of the high-performance computing community. We gathered these myths from conversations at conferences and meetings, product advertisements, papers, and other communications such as tweets, blogs, and news articles within and beyond our community. We believe they represent the zeitgeist of the current era of massive change, driven by the end of many scaling laws such as Dennard scaling and Moore's law. While some laws end, new directions are emerging, such as algorithmic scaling or novel architecture research. Nevertheless, these myths are rarely based on scientific facts, but rather on some evidence or argumentation. In fact, we believe that this is the very reason for the existence of many myths and why they cannot be answered clearly. While it feels like there should be clear answers for each, some may remain endless philosophical debates, such as whether Beethoven was better than Mozart. We would like to see our collection of myths as a discussion of possible new directions for research and industry investment.
引用
收藏
页码:245 / 259
页数:15
相关论文
共 59 条
  • [11] Beverland ME, 2022, ASSESSING REQUIREMEN, DOI [10. 48550/arXiv.2211.07629, DOI 10.48550/ARXIV.2211.07629]
  • [12] BHARATHI S, 2008, SC 08, P11, DOI DOI 10.1109/SC.2008.5217932
  • [13] Bi K., 2022, PANGU WEATHER 3D HIG
  • [14] Prognostic Validation of a Neural Network Unified Physics Parameterization
    Brenowitz, N. D.
    Bretherton, C. S.
    [J]. GEOPHYSICAL RESEARCH LETTERS, 2018, 45 (12) : 6289 - 6298
  • [15] Lightweight Requirements Engineering for Exascale Co-design
    Calotoiu, Alexandru
    Graf, Alexander
    Hoefler, Torsten
    Lorenz, Daniel
    Rinke, Sebastian
    Wolf, Felix
    [J]. 2018 IEEE INTERNATIONAL CONFERENCE ON CLUSTER COMPUTING (CLUSTER), 2018, : 201 - 211
  • [16] Cutress I, 2022, KEY INTELS ZF ZETTAF
  • [17] Cutress I, 2022, COMMUNICATION
  • [18] Noise in the Clouds: Influence of Network Performance Variability on Application Scalability
    De Sensi, Daniele
    De Matteis, Tiziano
    Taranov, Konstantin
    Di Girolamo, Salvatore
    Rahn, Tobias
    Hoefler, Torsten
    [J]. PROCEEDINGS OF THE ACM ON MEASUREMENT AND ANALYSIS OF COMPUTING SYSTEMS, 2022, 6 (03)
  • [19] Domke J, 2022, LOCUS PERFORMANCE CA
  • [20] Matrix Engines for High Performance Computing: A Paragon of Performance or Grasping at Straws?
    Domke, Jens
    Vatai, Emil
    Drozd, Aleksandr
    Chen, Peng
    Oyama, Yosuke
    Zhang, Lingqi
    Salaria, Shweta
    Mukunoki, Daichi
    Podobas, Artur
    Wahib, Mohamed
    Matsuoka, Satoshi
    [J]. 2021 IEEE 35TH INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM (IPDPS), 2021, : 1056 - 1065