Data Dependency Analysis

Data Dependency Analysis is a methodological construct in computer architecture that identifies and evaluates temporal relationships between instructions in a superscalar processor to determine whether one instruction must execute before another due to shared data elements, thereby enabling efficient instruction-level parallelism while preserving program correctness.

238

Publications

10.8K

Citations

620

Authors

228

Institutions

Publications per year

2017–2026

38

Authors

620

  1. FG
    Freddy Gabbay

    Technion – Israel Institute of Technology, Israel

    5 publications · 243 citations

    5

    h-index

  2. SS
    Saurabh Sinha

    Georgia Institute of Technology, United States

    5 publications · 239 citations

    5

    h-index

  3. AM
    Avi Mendelson

    Technion – Israel Institute of Technology, Israel

    5 publications · 243 citations

    5

    h-index

  4. MJ
    Mary Jean Harrold

    Georgia Institute of Technology, United States

    5 publications · 239 citations

    5

    h-index

  5. BP
    Brajendra Panda

    University of Arkansas at Fayetteville, United States

    4 publications · 244 citations

    4

    h-index

1–5 of 620

Venues

  1. ACM SIGPLAN Notices

    6 publications · 120 citations

    6

    h-index

  2. IEEE Transactions on Parallel and Distributed Systems

    TPDS · 6 publications · 399 citations

    6

    h-index

  3. IEEE Transactions on Computers

    5 publications · 118 citations

    4

    h-index

  4. ACM SIGARCH Computer Architecture News

    4 publications · 85 citations

    4

    h-index

  5. arXiv (Cornell University)

    Repository · 3 publications · 70 citations

    3

    h-index

1–5