ALPyNA: acceleration of loops in Python for novel architectures

Author:

Jacob Dejice1,Singer Jeremy1

Affiliation:

1. University of Glasgow, UK

Funder

Engineering and Physical Sciences Research Council

Publisher

ACM

Reference21 articles.

1. Automatic program parallelization

2. End-user software engineering

3. Full runtime polyhedral optimizing loop transformations with the generation, instantiation, and scheduling of code-bones

4. Stephen Cass and Parthasaradhi Bulusu. 2018. IEEE Spectrum Top Programming Languages Survey. https://spectrum.ieee.org/static/ interactive-the-top-programming-languages-2018 . Accessed: 2019-04-03. Stephen Cass and Parthasaradhi Bulusu. 2018. IEEE Spectrum Top Programming Languages Survey. https://spectrum.ieee.org/static/ interactive-the-top-programming-languages-2018 . Accessed: 2019-04-03.

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3. Py2Cy;Proceedings of the Genetic and Evolutionary Computation Conference Companion;2022-07-09

4. Pricing Python parallelism: a dynamic language cost model for heterogeneous platforms;Proceedings of the 16th ACM SIGPLAN International Symposium on Dynamic Languages;2020-11-15

5. Python programmers have GPUs too: automatic Python loop parallelization with staged dependence analysis;Proceedings of the 15th ACM SIGPLAN International Symposium on Dynamic Languages;2019-10-20

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