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.

42

Publications

1.6K

Citations

86

Authors

51

Institutions

Publications per year

2017–2026

7

Authors

86

  1. MS
    Marc Snir

    University of Illinois Urbana-Champaign, United States

    3 publications · 348 citations

    3

    h-index

  2. CP
    Clyde P. Kruskal

    University of Maryland, College Park, United States

    2 publications · 238 citations

    2

    h-index

  3. AS
    Aaron Sidford

    Stanford University, United States

    2 publications · 22 citations

    2

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  4. SB
    Sébastien Bubeck

    Microsoft (United States), United States

    2 publications · 22 citations

    2

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  5. QJ
    Qijia Jiang

    Stanford University, United States

    2 publications · 22 citations

    2

    h-index

1–5 of 86

Venues

  1. SIAM Journal on Computing

    4 publications · 508 citations

    4

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  2. Information Processing Letters

    3 publications · 49 citations

    3

    h-index

  3. Theoretical Computer Science

    3 publications · 273 citations

    3

    h-index

  4. IEEE Transactions on Computers

    3 publications · 172 citations

    3

    h-index

  5. arXiv (Cornell University)

    Repository · 3 publications · 40 citations

    3

    h-index

1–5