World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
35
Citations
5373
World Ranking
11684
National Ranking
4787

Research.com Recognitions

  • 2018 - Fellow of the American Association for the Advancement of Science (AAAS)
  • 2014 - ACM Distinguished Member
  • 2012 - IEEE Fellow For contributions to thread-level speculation, parallelizing compilers, and parallel libraries

Overview

Lawrence Rauchwerger is affiliated with the University of Illinois at Urbana-Champaign in the United States. Their research expertise spans several areas within computer science, with a focus on parallel computing and optimization techniques.

Their recent scholarly output includes publications in both journals and preprint repositories. Notable papers include:

  • "Provably optimal parallel transport sweeps on semi-structured grids," 2020, Journal of Computational Physics
  • "Introduction to the Special Issue on PPoPP 2017 (Part 2)," 2020, ACM Transactions on Parallel Computing
  • "Accelerating SARS-CoV-2 low frequency variant calling on ultra deep sequencing datasets," 2021, arXiv (Cornell University)
  • "Automatic Task Parallelization of Dataflow Graphs in ML/DL models," 2023, arXiv (Cornell University)

Rauchwerger's frequent coauthors include Nancy M. Amato, Bryce Kille, Yunxi Liu, Nicolae Sapoval, and Michael Nute, indicating collaborative work across multiple research projects.

Their main fields of study concentrate on computer science, with specific subfields including hardware and architecture, molecular biology, artificial intelligence, computer networks and communications, and plant science.

The scientist's primary research topics cover:

  • Parallel Computing and Optimization Techniques
  • Genomics and Phylogenetic Studies
  • Graph Theory and Algorithms
  • Advanced Data Storage Technologies
  • Distributed and Parallel Computing Systems
  • Algorithms and Data Compression
  • Machine Learning in Bioinformatics

Rauchwerger has published predominantly in the following venues:

  • arXiv (Cornell University)
  • Journal of Computational Physics
  • ACM Transactions on Parallel Computing

Throughout their career, Lawrence Rauchwerger has been recognized with several professional honors such as being named IEEE Fellow in 2012 for contributions to thread-level speculation, parallelizing compilers, and parallel libraries, ACM Distinguished Member in 2014, and Fellow of the American Association for the Advancement of Science (AAAS) in 2018.

Best Publications

  • The LRPD test: speculative run-time parallelization of loops with privatization and reduction parallelization

    L. Rauchwerger;D.A. Padua

  • Parallel programming with Polaris

    W. Blume;R. Doallo;R. Doallo;R. Doallo;R. Eigenmann;R. Eigenmann;J. Grout

  • STAPL: an adaptive, generic parallel C++ library

    Ping An;Alin Jula;Silvius Rus;Steven Saunders

  • A framework for adaptive algorithm selection in STAPL

    Nathan Thomas;Gabriel Tanase;Olga Tkachyshyn;Jack Perdue

  • Hybrid analysis: static & dynamic memory reference analysis

    Silvius Rus;Lawrence Rauchwerger;Jay Hoeflinger

  • Polaris: The Next Generation in Parallelizing Compilers

    Bill Blume;Rudolf Eigenmann;Keith Faigin;John Grout

  • The privatizing DOALL test: a run-time technique for DOALL loop identification and array privatization

    Lawrence Rauchwerger;David Padua

  • The R-LRPD test: speculative parallelization of partially parallel loops

    F. Dang;Hao Yu;L. Rauchwerger

  • Finding strongly connected components in distributed graphs

    William McLendon;Bruce Hendrickson;Steven J. Plimpton;Lawrence Rauchwerger

  • STAPL: standard template adaptive parallel library

    Antal Buss;Harshvardhan;Ioannis Papadopoulos;Olga Pearce

  • Parallelizing while loops for multiprocessor systems

    L. Rauchwerger;D. Padua

  • Automatic Detection of Parallelism: A grand challenge for high performance computing

    W. Blume;R. Eigenmann;J. Hoeflinger;D. Padua

  • Adaptive reduction parallelization techniques

    Hao Yu;Lawrence Rauchwerger

  • Hardware for speculative run-time parallelization in distributed shared-memory multiprocessors

    Ye Zhang;L. Rauchwerger;J. Torrellas

  • Run-time methods for parallelizing partially parallel loops

    Lawrence Rauchwerger;Nancy M. Amato;David A. Padua

  • Polaris: Improving the Effectiveness of Parallelizing Compilers

    William Blume;Rudolf Eigenmann;Keith Faigin;John Grout

  • A scalable method for run-time loop parallelization

    Lawrence Rauchwerger;Nancy M. Amato;David A. Padua

  • Removing architectural bottlenecks to the scalability of speculative parallelization

    Milos Prvulovic;María Jesús Garzarán;Lawrence Rauchwerger;Josep Torrellas

  • Standard Templates Adaptive Parallel Library (STAPL)

    Lawrence Rauchwerger;Francisco Arzu;Koji Ouchi

  • The STAPL parallel container framework

    Gabriel Tanase;Antal Buss;Adam Fidel;Harshvardhan

  • Proceedings of the 12th International Workshop on High-Level Parallel Programming Models and Supportive Environments

    Arun Chauhan;Daniel G. Chavarria;Li Chen;Guang R. Gao

Frequent Co-Authors

Nancy M. Amato
Nancy M. Amato University of Illinois at Urbana-Champaign
David Padua
David Padua University of Illinois at Urbana-Champaign
Josep Torrellas
Josep Torrellas University of Illinois at Urbana-Champaign
Milos Prvulovic
Milos Prvulovic Georgia Institute of Technology
Rudolf Eigenmann
Rudolf Eigenmann University of Delaware
Bruce Hendrickson
Bruce Hendrickson Lawrence Livermore National Laboratory
Wen-mei W. Hwu
Wen-mei W. Hwu University of Illinois at Urbana-Champaign
Steven J. Plimpton
Steven J. Plimpton Sandia National Laboratories
Peter Brown
Peter Brown University of Oxford

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