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A Unifying Framework to Enable Artificial Intelligence in High-Performance Computing Workflows

Journal Article · · Computing in Science & Engineering
 [1];  [1];  [2];  [3];  [3]
  1. RIKEN Center for Computational Science, Kobe (Japan)
  2. Argonne National Laboratory (ANL), Argonne, IL (United States)
  3. Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
+ Show Author Affiliations

Current trends point to a future where large-scale scientific applications are tightly coupled high-performance computing/artificial intelligence (HPC/AI) hybrids. Hence, we urgently need to invest in creating a seamless, scalable framework where HPC and AI/machine learning can efficiently work together and adapt to novel hardware and vendor libraries without starting from scratch every few years. Finally, the current ecosystem and sparsely connected community are not sufficient to tackle these challenges, and we require a breakthrough catalyst for science similar to what PyTorch enabled for AI.

Research Organization:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC02-06CH11357
OSTI ID:
2585304
Report Number(s):
LLNL--JRNL-2002577
Journal Information:
Computing in Science & Engineering, Journal Name: Computing in Science & Engineering Journal Issue: 1 Vol. 27; ISSN 1521-9615; ISSN 1558-366X
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)Copyright Statement
Country of Publication:
United States
Language:
English

References (5)

Score-P: A Joint Performance Measurement Run-Time Infrastructure for Periscope, Scalasca, TAU, and Vampir book January 2012
The low power architecture approach towards exascale computing journal November 2013
Flash-X: A multiphysics simulation software instrument journal July 2022
LLVM: A compilation framework for lifelong program analysis & transformation conference January 2004
Productive Performance Engineering for Weather and Climate Modeling with Python conference November 2022

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Related Subjects

high-performance computing
interoperability