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Title: Trends in data locality abstractions for HPC systems

The cost of data movement has always been an important concern in high performance computing (HPC) systems. It has now become the dominant factor in terms of both energy consumption and performance. Support for expression of data locality has been explored in the past, but those efforts have had only modest success in being adopted in HPC applications for various reasons. However, with the increasing complexity of the memory hierarchy and higher parallelism in emerging HPC systems, locality management has acquired a new urgency. Developers can no longer limit themselves to low-level solutions and ignore the potential for productivity and performance portability obtained by using locality abstractions. Fortunately, the trend emerging in recent literature on the topic alleviates many of the concerns that got in the way of their adoption by application developers. Data locality abstractions are available in the forms of libraries, data structures, languages and runtime systems; a common theme is increasing productivity without sacrificing performance. Furthermore, this paper examines these trends and identifies commonalities that can combine various locality concepts to develop a comprehensive approach to expressing and managing data locality on future large-scale high-performance computing systems.
ORCiD logo [1] ;  [2] ;  [3] ;  [4] ; ORCiD logo [5] ;  [6] ;  [7] ;  [8] ;  [9] ;  [10] ;  [11] ;  [12] ;  [13] ;  [14] ;  [15] ;  [16] ;  [9] ;  [17] ;  [18] ;  [19] more »; ORCiD logo [20] « less
  1. Koc Univ., Istanbul (Turkey)
  2. Argonne National Lab. (ANL), Lemont, IL (United States)
  3. ETH Zurich, Zurich (Switzerland)
  4. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  5. KTH Royal Institute of Technology, Solna (Sweden)
  6. Swiss National Supercomputer, Lugano (Switzerland)
  7. Cray Inc., Seattle, WA (United States)
  8. Intel Corp., Santa Clara, CA (United States)
  9. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  10. Argonne National Lab. (ANL), Argonne, IL (United States)
  11. Ludwig-Maximilians-Univ., Munich (Germany)
  12. Univ. of Erlangen-Nuremberg, Erlangen (Germany)
  13. INRIA Bordeaux Sud-Ouest, Talence (France)
  14. Univ. of Michigan, Ann Arbor, MI (United States)
  15. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
  16. Imperial College, London (United Kingdom)
  17. King Abdullah Univ. of Science and Technology, Thuwal (Saudia Arabia)
  18. RIKEN, Hyogo (Japan)
  19. Nvidia Corp., Santa Clara, CA (United States)
  20. Chalmers Univ. of Technology, Goteborg (Sweden)
Publication Date:
Report Number(s):
Journal ID: ISSN 1045-9219; 652425
Grant/Contract Number:
Accepted Manuscript
Journal Name:
IEEE Transactions on Parallel and Distributed Systems
Additional Journal Information:
Journal Volume: 28; Journal Issue: 10; Journal ID: ISSN 1045-9219
Research Org:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
Country of Publication:
United States
97 MATHEMATICS AND COMPUTING; data locality; programming abstractions; high-performance computing; data layout; locality-aware runtimes
OSTI Identifier: