GPU Debugging is a systematic methodology for identifying, analyzing, and resolving defects in programs executing on graphics processing units, distinguished by its focus on the unique parallel architectures, memory hierarchies, and execution models inherent to GPU computing environments.
13
Publications
474
Citations
36
Authors
16
Institutions
Publications per year
2017–2026
3
Publications
13
Guodong Li, Peng Li, Geof Sawaya et al. · 2012 · 127 citations
Seyong Lee, Jeffrey S. Vetter · 2014 · 68 citations
Guodong Li, Peng Li, Geof Sawaya et al. · ACM SIGPLAN Notices · 2012 · 37 citations
Hadi Jooybar, Wilson Wai Lun Fung, Mike O’Connor et al. · 2013 · 27 citations
1–5 of 13
Authors
36
- SL
2
h-index
- JS
2
h-index
- GG
2
h-index
- SP
2
h-index
- IG
2
h-index
1–5 of 36
Institutions
16
University of Utah
Salt Lake City, United States
2 h-index
6 publications
492 citations
Oak Ridge National Laboratory
Oak Ridge, United States
2 h-index
4 publications
156 citations
Georgia Institute of Technology
Atlanta, United States
2 h-index
2 publications
21 citations
Fujitsu (United States)
Sunnyvale, United States
2 h-index
6 publications
492 citations
Forschungszentrum Jülich
Jülich, Germany
1 h-index
2 publications
38 citations
Stony Brook University
Stony Brook, United States
1 h-index
2 publications
30 citations
Zhejiang University
Hangzhou, China
1 h-index
1 publications
15 citations
Tsinghua University
Beijing, China
1 h-index
2 publications
30 citations
1–8 of 16
Venues
- ACM Transactions on Graphics
TOG · 1 publications · 15 citations
1
h-index
- arXiv (Cornell University)
Repository · 1 publications · 21 citations
1
h-index
- Supercomputing Frontiers and Innovations
1 publications · 19 citations
1
h-index
- ACM SIGPLAN Notices
1 publications · 37 citations
1
h-index
1–4