Parallel Algorithmic Lower Bounds
Parallel Algorithmic Lower Bounds is a theoretical framework in computational complexity that establishes minimum resource requirements, such as time or processor bounds, for solving problems on parallel models of computation, distinguishing it from sequential lower bounds by explicitly accounting for parallelism's impact on computational limits.
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1.6K
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86
Authors
51
Institutions
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2017–2026
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University of Illinois Urbana-Champaign
Urbana, United States
3 h-index
5 publications
304 citations
The Ohio State University
Columbus, United States
3 h-index
5 publications
81 citations
University of Washington
Seattle, United States
3 h-index
3 publications
34 citations
Stanford University
Stanford, United States
3 h-index
6 publications
79 citations
ETH Zurich
Zurich, Switzerland
2 h-index
10 publications
132 citations
Seattle University
Seattle, United States
2 h-index
2 publications
22 citations

Zhejiang University
Hangzhou, China
1 h-index
4 publications
48 citations

National Center for Supercomputing Applications
Sofia, Bulgaria
1 h-index
1 publications
110 citations
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4 publications · 508 citations
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3 publications · 49 citations
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3 publications · 172 citations
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Repository · 3 publications · 40 citations
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