A shared compilation stack for distributed-memory parallelism in stencil DSLs-Reference-Cited by-同舟云学术

A shared compilation stack for distributed-memory parallelism in stencil DSLs

Published:2024-04-27 Issue: Volume: Page:38-56
ISSN:
Container-title:Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 3
language:
Short-container-title:

Author:

Bisbas George¹²^ORCID,Lydike Anton³^ORCID,Bauer Emilien³^ORCID,Brown Nick⁴^ORCID,Fehr Mathieu³^ORCID,Mitchell Lawrence⁵^ORCID,Rodriguez-Canal Gabriel⁴^ORCID,Jamieson Maurice⁴^ORCID,Kelly Paul H. J.¹^ORCID,Steuwer Michel⁶^ORCID,Grosser Tobias⁷^ORCID

Affiliation:

1. Department of Computing, Imperial College London, London, United Kingdom

2. Department of Earth Science and Engineering, Imperial College London, London, United Kingdom

3. School of Informatics, The University of Edinburgh, Edinburgh, United Kingdom

4. EPCC, The University of Edinburgh, Edinburgh, United Kingdom

5. Unaffiliated, Edinburgh, United Kingdom

6. Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, Germany

7. Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom

Funder

Engineering and Physical Sciences Research Council

EPSRC

Publisher

ACM

Reference81 articles.

1. Martín Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S. Corrado Andy Davis Jeffrey Dean Matthieu Devin Sanjay Ghemawat Ian Goodfellow Andrew Harp Geoffrey Irving Michael Isard Yangqing Jia Rafal Jozefowicz Lukasz Kaiser Manjunath Kudlur Josh Levenberg Dandelion Mané Rajat Monga Sherry Moore Derek Murray Chris Olah Mike Schuster Jonathon Shlens Benoit Steiner Ilya Sutskever Kunal Talwar Paul Tucker Vincent Vanhoucke Vijay Vasudevan Fernanda Viégas Oriol Vinyals Pete Warden Martin Wattenberg Martin Wicke Yuan Yu and Xiaoqiang Zheng. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. (2015). https://www.tensorflow.org/ Software available from tensorflow.org.

2. LFRic: Meeting the challenges of scalability and performance portability in Weather and Climate models

3. GridTools: A framework for portable weather and climate applications

4. UFL: a finite element form language