"Intrepydd: performance, productivity, and portability for data science ..."

Tong Zhou et al. (2020)

> Home

Details and statistics

DOI: 10.1145/3426428.3426915

access: closed

type: Conference or Workshop Paper

metadata version: 2024-10-06

- view
  - electronic edition via DOI
  - unpaywalled version
  authority control:
- export record
  dblp key:
  - conf/oopsla/ZhouSJSCVS20
- ask others
- share record
  persistent URL:
  - https://dblp.org/rec/conf/oopsla/ZhouSJSCVS20
Tong Zhou, Jun Shirako, Anirudh Jain, Sriseshan Srikanth, Thomas M. Conte, Richard W. Vuduc, Vivek Sarkar:
Intrepydd: performance, productivity, and portability for data science application kernels. Onward! 2020: 65-83

manage site settings

To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.