Thread Parallelism
Thread Parallelism is a computational concurrency model in GPU computing where multiple threads execute the same instruction across different data elements simultaneously, distinguished by its fine-grained parallel execution managed at the hardware level through thread scheduling and resource partitioning.
480
Publications
28.1K
Citations
1.4K
Authors
352
Institutions
Publications per year
2017–2026
81
480
Analyzing CUDA workloads using a detailed GPU simulator
Ali Bakhoda, George L. Yuan, Wilson Wai Lun Fung et al. · 2009 · 1.7K citations
Dean M. Tullsen, Susan J. Eggers, Joel Emer et al. · 1996 · 782 citations
1–5 of 480
1.4K
12
h-index
10
h-index
9
h-index
9
h-index
9
h-index
1–5 of 1.4K
352
Intel (United States)
Santa Clara, United States
24 h-index
121 publications
6K citations
Universitat Politècnica de Catalunya
Barcelona, Spain
17 h-index
86 publications
3.7K citations
University of Michigan
Ann Arbor, United States
14 h-index
48 publications
3.2K citations
Stanford University
Stanford, United States
14 h-index
46 publications
3.6K citations
Georgia Institute of Technology
Atlanta, United States
13 h-index
42 publications
4K citations
University of California San Diego
San Diego, United States
12 h-index
24 publications
4K citations
Hewlett-Packard (United States)
Palo Alto, United States
11 h-index
23 publications
4.1K citations
University of Southern California
Los Angeles, United States
10 h-index
23 publications
1.2K citations
1–8 of 352
20 publications · 1.5K citations
15
h-index
TPDS · 14 publications · 617 citations
12
h-index
9 publications · 338 citations
9
h-index
5 publications · 456 citations
5
h-index
4 publications · 1.2K citations
4
h-index
1–5