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John Mellor-Crummey

John Mellor-Crummey

D-Index & Metrics

Computer Science

D-Index
57
Citations
12201
World Ranking
3863
National Ranking
1827

Research.com Recognitions

  • 2013 - ACM Fellow For contributions to parallel and high performance computing.

Overview

John Mellor-Crummey is affiliated with Rice University in the United States and has contributed extensively to the field of computer science. Their research primarily focuses on parallel computing and optimization techniques, advanced data storage technologies, distributed and parallel computing systems, distributed systems and fault tolerance, computer graphics and visualization techniques, advanced neural network applications, and software system performance and reliability.

The scientist's publication record includes multiple papers covering these topics. Notable recent papers include:

  • An Automated Tool for Analysis and Tuning of GPU-Accelerated Code in HPC Applications, 2021, IEEE Transactions on Parallel and Distributed Systems
  • Accelerating high-order stencils on GPUs, 2021, Concurrency and Computation Practice and Experience
  • Refining HPCToolkit for application performance analysis at exascale, 2024, The International Journal of High Performance Computing Applications
  • Accelerating High-Order Stencils on GPUs, 2020, arXiv (Cornell University)
  • Parallel Binary Code Analysis, 2020, arXiv (Cornell University)

Frequently publishing in venues such as arXiv (Cornell University), IEEE Transactions on Parallel and Distributed Systems, The International Journal of High Performance Computing Applications, and Concurrency and Computation Practice and Experience, they have maintained a consistent presence in both open-access and traditional academic journals.

The scientist collaborates regularly with co-authors including Ryuichi Sai, Xiaozhu Meng, Mauricio Araya-Polo, Keren Zhou, and Jonathon Anderson.

Their areas of study are categorized under computer science with significant subfields in:

  • Computer Networks and Communications
  • Hardware and Architecture
  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Information Systems

John Mellor-Crummey has been recognized as an ACM Fellow since 2013 for contributions to parallel and high performance computing.

Best Publications

  • Algorithms for scalable synchronization on shared-memory multiprocessors

    John M. Mellor-Crummey;Michael L. Scott

  • HPCTOOLKIT: tools for performance analysis of optimized parallel programs

    Laksono Adhianto;S. Banerjee;Michael W. Fagan;Mark Krentel

  • Terascale direct numerical simulations of turbulent combustion using S3D

    J. H. Chen;A. Choudhary;B. De Supinski;M. Devries

  • The GrADS Project: Software Support for High-Level Grid Application Development

    Francine Berman;Andrew Chien;Keith Cooper;Jack Dongarra

  • Cross-architecture performance predictions for scientific applications using parameterized models

    Gabriel Marin;John Mellor-Crummey

  • Improving Memory Hierarchy Performance for Irregular Applications Using Data and Computation Reorderings

    John Mellor-Crummey;David Whalley;Ken Kennedy

  • On-the-fly detection of data races for programs with nested fork-join parallelism

    John Mellor-Crummey

  • Scheduling strategies for mapping application workflows onto the grid

    Anirban Mandal;K. Kennedy;C. Koelbel;G. Marin

  • Synchronization without contention

    John M. Mellor-Crummey;Michael L. Scott

  • An evaluation of global address space languages: co-array fortran and unified parallel C

    Cristian Coarfa;Yuri Dotsenko;John Mellor-Crummey;François Cantonnet

  • Scalable reader-writer synchronization for shared-memory multiprocessors

    John M. Mellor-Crummey;Michael L. Scott

  • New grid scheduling and rescheduling methods in the GrADS project

    F. Berman;H. Casanova;A. Chien;K. Cooper

  • An Integrated Compilation and Performance Analysis Environment for Data Parallel Programs

    Vikram S. Adve;John Mellor-Crummey;Mark Anderson;Jhy-Chun Wang

  • Toward a framework for preparing and executing adaptive grid programs

    K. Kennedy;M. Mazina;J. Mellor-Crummey;K. Cooper

  • Analyzing parallel program executions using multiple views

    Thomas J. LeBlanc;John M. Mellor-Crummey;Robert J. Fowler

  • HPCVIEW: A Tool for Top-down Analysis of Node Performance

    John Mellor-Crummey;Robert J. Fowler;Gabriel Marin;Nathan Tallent

  • The ParaScope parallel programming environment

    K.D. Cooper;M.W. Hall;R.T. Hood;K. Kennedy

  • New grid scheduling and rescheduling methods in the GrADS project

    K. Cooper;A. Dasgupta;K. Kennedy;C. Koelbel

  • Using integer sets for data-parallel program analysis and optimization

    Vikram Adve;John Mellor-Crummey

  • Analyzing lock contention in multithreaded applications

    Nathan R. Tallent;John M. Mellor-Crummey;Allan Porterfield

  • Debugging parallel programs with instant replay

    Thomas J. LeBlanc;John M. Mellor-Crummey

  • Algorithms for scalable synchronization on shared-memory multiprocessors

    John M. Mellor-Crummey;Michael L. Scott

Frequent Co-Authors

Ken Kennedy
Ken Kennedy Rice University
Keith D. Cooper
Keith D. Cooper Rice University
Cristian Coarfa
Cristian Coarfa Baylor College of Medicine
Vikram Adve
Vikram Adve University of Illinois at Urbana-Champaign
Michael L. Scott
Michael L. Scott University of Rochester
Jack Dongarra
Jack Dongarra University of Tennessee at Knoxville
Vivek Sarkar
Vivek Sarkar Georgia Institute of Technology
Mary Hall
Mary Hall University of Utah
Daniel A. Reed
Daniel A. Reed University of Utah
David Whalley
David Whalley Florida State University

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