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Performance on HPC Platforms Is Possible Without C++

Journal Article · · Computing in Science and Engineering
 [1];  [2];  [3];  [2];  [4]
  1. Argonne National Laboratory (ANL), Argonne, IL (United States)
  2. Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
  3. Hewlett Packard Enterprise, Seattle, WA (United States)
  4. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
+ Show Author Affiliations
Computing at large scales has become extremely challenging due to increasing heterogeneity in both hardware and software. More and more scientific workflows must tackle a range of scales and use machine learning and AI intertwined with more traditional numerical modeling methods, placing more demands on computational platforms. These constraints indicate a need to fundamentally rethink the way computational science is done and the tools that are needed to enable these complex workflows. The current set of C++-based solutions may not suffice, and relying exclusively upon C++ may not be the best option, especially because several newer languages and boutique solutions offer more robust design features to tackle the challenges of heterogeneity. In June 2023, we held a mini symposium that explored the use of newer languages and heterogeneity solutions that are not tied to C++ and that offer options beyond template metaprogramming and Parallel. For for performance and portability. In conclusion, we describe some of the presentations and discussion from the mini symposium in this article.
Research Organization:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
Grant/Contract Number:
AC02-05CH11231
OSTI ID:
2483660
Journal Information:
Computing in Science and Engineering, Journal Name: Computing in Science and Engineering Journal Issue: 5 Vol. 25; ISSN 1521-9615
Publisher:
IEEE Computer SocietyCopyright Statement
Country of Publication:
United States
Language:
English

References (3)

A tool and a methodology to use macros for abstracting variations in code for different computational demands journal July 2023
Caffeine: CoArray Fortran Framework of Efficient Interfaces to Network Environments conference November 2022
Stateful dataflow multigraphs: a data-centric model for performance portability on heterogeneous architectures
  • Ben-Nun, Tal; de Fine Licht, Johannes; Ziogas, Alexandros N.
  • SC '19: The International Conference for High Performance Computing, Networking, Storage, and Analysis, Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis https://doi.org/10.1145/3295500.3356173
conference November 2019

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Related Subjects

97 MATHEMATICS AND COMPUTING
Advanced Features
Application Programming Interface
C++ languages
Computational Platform
Computational modeling
Computer Science
Data Packets
Debugging
Flow Control
Hardware
High-performance Computing
Interoperability
Load Data
Machine Learning
Machine learning
Message Passing Interface
Numerical models
Object-oriented
Parallelization
Programming Language
Programming Paradigm
Range Of Learning
Scientific computing
Semantic
Software
State Machine