Parallel Performance Optimization
Parallel Performance Optimization is a methodological framework within parallel computing that systematically enhances the efficiency and scalability of parallel algorithms by minimizing execution time, resource contention, and communication overhead through targeted modifications to task decomposition, load balancing, and data distribution strategies.
883
Publications
36.2K
Citations
2.6K
Authors
689
Institutions
Publications per year
2017–2026
122
883
Kay Ousterhout, Patrick Wendell, Matei Zaharia et al. · 2013 · 582 citations · Full text
GraphLab: A New Framework For Parallel Machine Learning
Yucheng Low, Joseph E. Gonzalez, Aapo Kyrola et al. · arXiv (Cornell University) · 2014 · 441 citations · Full text
1–5 of 883
2.6K
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University of Illinois Urbana-Champaign, United States
8 publications · 422 citations
8
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7
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1–5 of 2.6K
689
University of Illinois Urbana-Champaign
Urbana, United States
20 h-index
95 publications
5.8K citations
Argonne National Laboratory
Lemont, United States
19 h-index
61 publications
1.6K citations
Microsoft (United States)
Redmond, United States
17 h-index
45 publications
6.5K citations
Intel (United States)
Santa Clara, United States
17 h-index
55 publications
1.8K citations
IBM (United States)
Armonk, United States
16 h-index
55 publications
3.1K citations
University of Maryland, College Park
College Park, United States
15 h-index
45 publications
2.1K citations
The Ohio State University
Columbus, United States
15 h-index
66 publications
5.1K citations
Carnegie Mellon University
Pittsburgh, United States
15 h-index
42 publications
5.1K citations
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TPDS · 43 publications · 1.6K citations
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16 publications · 714 citations
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13 publications · 282 citations
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13 publications · 1.8K citations
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11 publications · 488 citations
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