Parallel Algorithm Complexity

Parallel Algorithm Complexity is the computational complexity measure that characterizes the time, processor, and communication resource requirements of a parallel algorithm as a function of input size, distinguishing it from sequential algorithm complexity by accounting for concurrent execution and inter-processor interactions in parallel computing models.

607

Publications

29.2K

Citations

1.2K

Authors

480

Institutions

Publications per year

2017–2026

67

Authors

1.2K

  1. UV
    Uzi Vishkin

    University of Maryland, College Park, United States

    8 publications · 1.5K citations

    8

    h-index

  2. GL
    Gary L. Miller

    Carnegie Mellon University, United States

    8 publications · 460 citations

    8

    h-index

  3. SG
    Selim G. Akl

    Queen's University, Canada

    8 publications · 214 citations

    8

    h-index

  4. JH
    John H. Reif

    Duke University, United States

    6 publications · 373 citations

    6

    h-index

  5. SC
    Stephen Cook

    University of Toronto, Canada

    6 publications · 368 citations

    6

    h-index

1–5 of 1.2K

Venues

  1. SIAM Journal on Computing

    39 publications · 2.4K citations

    23

    h-index

  2. IEEE Transactions on Computers

    22 publications · 1.5K citations

    16

    h-index

  3. IEEE Transactions on Parallel and Distributed Systems

    TPDS · 22 publications · 487 citations

    14

    h-index

  4. Information Processing Letters

    15 publications · 329 citations

    12

    h-index

  5. Journal of Parallel and Distributed Computing

    12 publications · 702 citations

    10

    h-index

1–5