World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
38
Citations
8070
World Ranking
10074
National Ranking
4251

Overview

Jeffrey K. Hollingsworth is affiliated with the University of Maryland, College Park in the United States. Their research contributions are documented through several scholarly publications and collaborations with other researchers in related fields.

Recent published work includes the paper titled Scientific Programming, published in 2022 in the journal Scientific Programming. This paper has accumulated 128 citations, indicating engagement by the research community.

Frequent co-authors in Jeffrey K. Hollingsworth's collaborations include:

  • Siegfried Benkner
  • Franz Franchetti
  • Hans Michael Gerndt

Scientific contributions have appeared primarily in the following publication venue:

  • Scientific Programming

Best Publications

  • The Paradyn parallel performance measurement tool

    B.P. Miller;M.D. Callaghan;J.M. Cargille;J.K. Hollingsworth

  • An API for Runtime Code Patching

    Bryan Buck;Jeffrey K. Hollingsworth

  • Instrumentation and measurement

    Jeffrey K. Hollingsworth;Bart Miller

  • Active Harmony: Towards Automated Performance Tuning

    C. Tapus;I-Hsin Chung;J.K. Hollingsworth

  • A scalable auto-tuning framework for compiler optimization

    Ananta Tiwari;Chun Chen;Jacqueline Chame;Mary Hall

  • Automatic mining of source code repositories to improve bug finding techniques

    C.C. Williams;J.K. Hollingsworth

  • Dynamic program instrumentation for scalable performance tools

    J.K. Hollingsworth;B.P. Miller;J. Cargille

  • IPS-2: the second generation of a parallel program measurement system

    B.P. Miller;M. Clark;J. Hollingsworth;S. Kierstead

  • Efficient instrumentation for code coverage testing

    Mustafa M. Tikir;Jeffrey K. Hollingsworth

  • Understanding the High-Performance-Computing Community: A Software Engineer's Perspective

    V.R. Basili;J.C. Carver;D. Cruzes;L.M. Hochstein

  • Experiences with marmoset: designing and using an advanced submission and testing system for programming courses

    Jaime Spacco;David Hovemeyer;William Pugh;Fawzi Emad

  • Parallel Programmer Productivity: A Case Study of Novice Parallel Programmers

    Lorin Hochstein;Jeff Carver;Forrest Shull;Sima Asgari

  • MDL: a language and compiler for dynamic program instrumentation

    J. K. Hollingsworth;O. Niam;B. P. Miller;Zhichen Xu

  • Tuning the performance of I/O-intensive parallel applications

    Anurag Acharya;Mustafa Uysal;Robert Bennett;Assaf Mendelson

  • Dynamic control of performance monitoring on large scale parallel systems

    Jeffrey K. Hollingsworth;Barton P. Miller

  • Automatically adapting programs for mixed-precision floating-point computation

    Michael O. Lam;Jeffrey K. Hollingsworth;Bronis R. de Supinski;Matthew P. Legendre

  • Autotuning in High-Performance Computing Applications

    Prasanna Balaprakash;Jack Dongarra;Todd Gamblin;Mary Hall

  • SIGMA: A Simulator Infrastructure to Guide Memory Analysis

    Luiz DeRose;K. Ekanadham;Jeffrey K. Hollingsworth;Simone Sbaraglia

  • Automated cluster-based Web service performance tuning

    I.-H. Chung;J.K. Hollingsworth

  • Critical path profiling of message passing and shared-memory programs

    J.K. Hollingsworth

Frequent Co-Authors

Barton P. Miller
Barton P. Miller University of Wisconsin–Madison
Mary Hall
Mary Hall University of Utah
Victor R. Basili
Victor R. Basili University of Maryland, College Park
Jeffrey C. Carver
Jeffrey C. Carver University of Alabama
Jack Dongarra
Jack Dongarra University of Tennessee at Knoxville
Marvin V. Zelkowitz
Marvin V. Zelkowitz University of Maryland, College Park
Forrest Shull
Forrest Shull Carnegie Mellon University
Ethan L. Miller
Ethan L. Miller University of California, Santa Cruz
Bronis R. de Supinski
Bronis R. de Supinski Lawrence Livermore National Laboratory
Allan Snavely
Allan Snavely University of California, San Diego

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