MPI-RICAL: Data-Driven MPI Distributed Parallelism Assistance with Transformers-Reference-Cited by-同舟云学术

MPI-RICAL: Data-Driven MPI Distributed Parallelism Assistance with Transformers

Published:2023-11-12 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis
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Author:

Schneider Nadav¹^ORCID,Kadosh Tal¹^ORCID,Hasabnis Niranjan²^ORCID,Mattson Timothy²^ORCID,Pinter Yuval³^ORCID,Oren Gal⁴^ORCID

Affiliation:

1. Ben-Gurion University of the Negev, Israel and IAEC, Israel

2. Intel Labs, United States of America

3. Ben-Gurion University of the Negev, Israel

4. Technion - Israel Institute of Technology, Israel and NRCN, Israel

Funder

This research was supported by the Israeli Council for Higher Education (CHE) via the Data Science Research Center, Ben-Gurion University of the Negev, Israel; Intel Corporation (oneAPI CoE program); and the Lynn and William Frankel Center for Computer Science. Computational support was provided by the NegevHPC project and Intel Developer Cloud.

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3624062.3624063

Reference44 articles.

1. [n. d.]. GitHub Copilot. https://github.com/features/copilot [Online]. [n. d.]. GitHub Copilot. https://github.com/features/copilot [Online].

2. [n. d.]. ML-Enhanced Code Completion Improves Developer Productivity. https://ai.googleblog.com/2022/07/ml-enhanced-code-completion-improves.html [Online]. [n. d.]. ML-Enhanced Code Completion Improves Developer Productivity. https://ai.googleblog.com/2022/07/ml-enhanced-code-completion-improves.html [Online].

3. [n. d.]. NegevHPC Project. https://www.negevhpc.com. [Online]. [n. d.]. NegevHPC Project. https://www.negevhpc.com. [Online].

4. The Nas Parallel Benchmarks

5. Towards automatic translation of OpenMP to MPI

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