Intrepydd: performance, productivity, and portability for data science application kernels-Reference-Cited by-同舟云学术

Intrepydd: performance, productivity, and portability for data science application kernels

Published:2020-11-17 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2020 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software
language:
Short-container-title:

Author:

Zhou Tong¹,Shirako Jun¹,Jain Anirudh¹,Srikanth Sriseshan²,Conte Thomas M.¹,Vuduc Richard¹,Sarkar Vivek¹

Affiliation:

1. Georgia Institute of Technology, USA

2. AMD Research, USA

Funder

Defense Advanced Research Projects Agency

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3426428.3426915

Reference76 articles.

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3. 1993. The R Project for Statistical Computing. https://www.rproject.org/. 1993. The R Project for Statistical Computing. https://www.rproject.org/.

4. 2001. multiprocessing-Process-based parallelism. https://docs.python.org/3/library/multiprocessing.html. 2001. multiprocessing-Process-based parallelism. https://docs.python.org/3/library/multiprocessing.html.

5. 2001. SciPy. https://www.scipy.org/. 2001. SciPy. https://www.scipy.org/.

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