“It looks like you’re writing a parallel loop”: a machine learning based parallelization assistant-Reference-Cited by-同舟云学术

“It looks like you’re writing a parallel loop”: a machine learning based parallelization assistant

Published:2019 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 6th ACM SIGPLAN International Workshop on AI-Inspired and Empirical Methods for Software Engineering on Parallel Computing Systems - AI-SEPS 2019
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Author:

Maramzin Aleksandr¹,Vasiladiotis Christos¹,Lozano Roberto Castañeda¹,Cole Murray¹,Franke Björn¹

Affiliation:

1. University of Edinburgh, UK

Funder

Engineering and Physical Sciences Research Council

Publisher

ACM Press

Reference23 articles.

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2. Amir H. Ashouri, Andrea Bignoli, Gianluca Palermo, Cristina Silvano, Sameer Kulkarni, and John Cavazos. 2017. MiCOMP: Mitigating the Compiler Phase-Ordering Problem Using Optimization Sub-Sequences and Machine Learning. ACM Trans. Archit. Code Optim. 14, 3, Article 29 (Sept. 2017), 28 pages.

3. J. Cavazos and M. F. P. O’Boyle. 2005. Automatic Tuning of Inlining Heuristics. In SC ’05: Proceedings of the 2005 ACM/IEEE Conference on Supercomputing. IEEE Computer Society, Washington, DC, USA, 14–14.

4. Keith D. Cooper, Alexander Grosul, Timothy J. Harvey, Steven Reeves, Devika Subramanian, Linda Torczon, and Todd Waterman. 2005. ACME: Adaptive Compilation Made Efficient. In Proceedings of the 2005 ACM SIGPLAN/SIGBED Conference on Languages, Compilers, and Tools for Embedded Systems (LCTES ’05). ACM, New York, NY, USA, 69–77.

5. Leonardo Dagum and Ramesh Menon. 1998. OpenMP: An IndustryStandard API for Shared-Memory Programming. IEEE Comput. Sci. Eng. 5, 1 (Jan. 1998), 46–55.

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