Scalable Experimentation

Scalable Experimentation is a methodological framework within computational science that enables the systematic expansion of experimental scope and complexity through modular, reusable, and interoperable components, distinguishing it from ad hoc or monolithic approaches by its emphasis on architectural flexibility and resource-efficient adaptation across varying scales of data, computation, or deployment.

79

Publications

3.8K

Citations

300

Authors

121

Institutions

Publications per year

2017–2026

23

Authors

300

  1. YX
    Ya Xu

    LinkedIn (United States), United States

    3 publications · 665 citations

    3

    h-index

  2. SM
    Susan M. Sanchez

    Naval Postgraduate School, United States

    3 publications · 64 citations

    3

    h-index

  3. VS
    Victoria Stodden

    University of Illinois Urbana-Champaign, United States

    3 publications · 133 citations

    3

    h-index

  4. JM
    Justin M. Wozniak

    Argonne National Laboratory, United States

    2 publications · 125 citations

    2

    h-index

  5. JH
    Jan Himmelspach

    University of Rostock, Germany

    2 publications · 61 citations

    2

    h-index

1–5 of 300

Venues

  1. Journal of Physics Conference Series

    4 publications · 201 citations

    4

    h-index

  2. ACM SIGOPS Operating Systems Review

    2 publications · 187 citations

    2

    h-index

  3. 2008 Winter Simulation Conference

    Conference · 2 publications · 43 citations

    2

    h-index

  4. Winter Simulation Conference

    Conference · 2 publications · 66 citations

    2

    h-index

  5. Archives of Gerontology and Geriatrics

    1 publications · 28 citations

    1

    h-index

1–5