Parallel Matrix Computation

Parallel Matrix Computation is a computational framework within matrix computation that leverages concurrent processing across multiple processors or cores to perform matrix operations by partitioning data and distributing tasks, thereby reducing overall execution time through simultaneous arithmetic and memory access.

942

Publications

45K

Citations

1.9K

Authors

597

Institutions

Publications per year

2017–2026

87

Authors

1.9K

  1. JD
    Jack Dongarra

    University of Tennessee at Knoxville, United States

    57 publications · 4.7K citations

    26

    h-index

  2. RA
    Robert A. Geijn

    The University of Texas at Austin, United States

    28 publications · 2.8K citations

    21

    h-index

  3. JD
    James Demmel

    University of California, Berkeley, United States

    17 publications · 977 citations

    15

    h-index

  4. ST
    Stanimire Tomov

    University of Tennessee at Knoxville, United States

    17 publications · 501 citations

    14

    h-index

  5. ES
    Enrique S. Quintana–Ort́ı

    Universitat Jaume I, Spain

    14 publications · 449 citations

    13

    h-index

1–5 of 1.9K

Venues

  1. SIAM Journal on Scientific Computing

    40 publications · 2.3K citations

    28

    h-index

  2. ACM Transactions on Mathematical Software

    TOMS · 27 publications · 4.7K citations

    21

    h-index

  3. Parallel Computing

    36 publications · 1.5K citations

    20

    h-index

  4. 18

    h-index

  5. IEEE Transactions on Computers

    23 publications · 1.2K citations

    18

    h-index

1–5