Runtime Optimization Feedback

Runtime Optimization Feedback is a runtime optimization mechanism in Just-In-Time Compilation that dynamically refines code execution performance by incorporating profiling data and execution context gathered during program execution to guide adaptive recompilation and optimization decisions.

15

Publications

602

Citations

45

Authors

28

Institutions

Publications per year

2017–2026

2

Authors

45

  1. MC
    Milind Chabbi

    William & Mary, United States

    3 publications · 67 citations

    3

    h-index

  2. XL
    Xu Liu

    William & Mary, United States

    2 publications · 30 citations

    2

    h-index

  3. MA
    Matthew Arnold

    IBM (United States), United States

    2 publications · 90 citations

    2

    h-index

  4. BC
    Brad Calder

    University of California San Diego, United States

    2 publications · 90 citations

    2

    h-index

  5. JL
    Jeremy Lau

    University of California San Diego, United States

    2 publications · 90 citations

    2

    h-index

1–5 of 45

Venues

  1. Proceedings of the VLDB Endowment

    PVLDB · 1 publications · 41 citations

    1

    h-index

  2. 1

    h-index

  3. ACM SIGPLAN Notices

    1 publications · 57 citations

    1

    h-index

  4. ACM SIGSOFT Software Engineering Notes

    SEN · 1 publications · 32 citations

    1

    h-index

1–4