A Survey of General-purpose Polyhedral Compilers
Author:
Affiliation:
1. ICube, University of Strasbourg, Strasbourg, France
2. CAMUS, Inria Research Centre Nancy Grand Est, Villers-les-Nancy France
3. ICube, University of Strasbourg, Strasbourg France
Abstract
Publisher
Association for Computing Machinery (ACM)
Link
https://dl.acm.org/doi/pdf/10.1145/3674735
Reference60 articles.
1. 2014. Candl - Data Dependence Analysis Tool in the Polyhedral Model. https://github.com/periscop/candl.
2. Empirically Examining the Parallelizability of Open Source Software System
3. Matthew Arnold, Michael Hind, and Barbara G. Ryder. 2001. An Empirical Study of Selective Optimization. In Languages and Compilers for Parallel Computing, Samuel P. Midkiff, José E. Moreira, Manish Gupta, Siddhartha Chatterjee, Jeanne Ferrante, Jan Prins, William Pugh, and Chau-Wen Tseng (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 49–67.
4. Amir H. Ashouri William Killian John Cavazos Gianluca Palermo and Cristina Silvano. 2018. A Survey on Compiler Autotuning Using Machine Learning. ACM Comput. Surv. (2018) 42 pages. https://doi.org/10.1145/3197978
5. Compiler transformations for high-performance computing
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