Parallel Matrix Algorithms

Parallel Matrix Algorithms is a subclass of parallel algorithms that specifically encompasses computational methods designed to perform matrix operations by decomposing and distributing data and tasks across multiple processing units to achieve scalability and efficiency in solving linear algebra problems.

967

Publications

44.7K

Citations

1.7K

Authors

569

Institutions

Publications per year

2017–2026

62

Authors

1.7K

  1. JD
    Jack Dongarra

    University of Tennessee at Knoxville, United States

    42 publications · 2.2K citations

    22

    h-index

  2. RA
    Robert A. Geijn

    The University of Texas at Austin, United States

    25 publications · 1.6K citations

    21

    h-index

  3. JD
    James Demmel

    University of California, Berkeley, United States

    22 publications · 1.9K citations

    18

    h-index

  4. AS
    Ahmed Sameh

    Purdue University West Lafayette, United States

    19 publications · 1.2K citations

    16

    h-index

  5. VY
    Victor Y. Pan

    Lehman College, United States

    18 publications · 865 citations

    15

    h-index

1–5 of 1.7K

Venues

  1. SIAM Journal on Scientific Computing

    52 publications · 2.9K citations

    31

    h-index

  2. Parallel Computing

    62 publications · 3K citations

    28

    h-index

  3. ACM Transactions on Mathematical Software

    TOMS · 26 publications · 2.1K citations

    19

    h-index

  4. 19

    h-index

  5. IEEE Transactions on Computers

    23 publications · 828 citations

    17

    h-index

1–5