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D-Index & Metrics

Computer Science

D-Index
36
Citations
13976
World Ranking
10967
National Ranking
4560

Overview

Samuel Williams is affiliated with the Lawrence Berkeley National Laboratory in the United States. Their research primarily spans the field of Computer Science with a focus on several subfields including Computer Networks and Communications, Hardware and Architecture, Information Systems, Artificial Intelligence, and Nuclear and High Energy Physics.

The scope of their work covers multiple main topics such as Parallel Computing and Optimization Techniques, Advanced Data Storage Technologies, Cloud Computing and Resource Management, Magnetic confinement fusion research, Fusion materials and technologies, Advanced Neural Network Applications, and Distributed and Parallel Computing Systems.

Williams has contributed to numerous publications, with frequent appearances in respected venues. These venues include:

  • arXiv (Cornell University)
  • Concurrency and Computation Practice and Experience
  • Research Square (Research Square)
  • SSRN Electronic Journal
  • Abstracts with programs - Geological Society of America

Their recent papers demonstrate an emphasis on machine learning applications in plasma physics, FPGA design optimization, and GPU performance modeling. Some of these papers include:

  • Application of machine learning and artificial intelligence to extend EFIT equilibrium reconstruction, 2022, Plasma Physics and Controlled Fusion
  • A Comprehensive Methodology to Optimize FPGA Designs via the Roofline Model, 2021, IEEE Transactions on Computers
  • FPGA-based HPC accelerators: An evaluation on performance and energy efficiency, 2021, Concurrency and Computation Practice and Experience
  • Instruction Roofline: An insightful visual performance model for GPUs, 2021, Concurrency and Computation Practice and Experience
  • Large scale multi-GPU based parallel traffic simulation for accelerated traffic assignment and propagation, 2024, Transportation Research Part C Emerging Technologies

Williams has also co-authored work with several frequent collaborators, including:

  • Leonid Oliker
  • Nicholas J. Wright
  • Charlene Yang
  • Yunsong Wang
  • Nan Ding

Beyond journal articles and conference papers, they have contributed to book publications as well. Notably, Williams has a publication titled Before the War, and After the Union, released in 2021 under Liverpool University Press.

Best Publications

  • The Landscape of Parallel Computing Research: A View from Berkeley

    Krste Asanovic;Ras Bodik;Bryan Christopher Catanzaro;Joseph James Gebis

  • Roofline: an insightful visual performance model for multicore architectures

    Samuel Williams;Andrew Waterman;David Patterson

  • Optimization of sparse matrix-vector multiplication on emerging multicore platforms

    Samuel Williams;Leonid Oliker;Richard Vuduc;John Shalf

  • Optimization of sparse matrix-vector multiplication on emerging multicore platforms

    Samuel Williams;Leonid Oliker;Richard Vuduc;John Shalf

  • Stencil computation optimization and auto-tuning on state-of-the-art multicore architectures

    Kaushik Datta;Mark Murphy;Vasily Volkov;Samuel Williams

  • The potential of the cell processor for scientific computing

    Samuel Williams;John Shalf;Leonid Oliker;Shoaib Kamil

  • AMReX: a framework for block-structured adaptive mesh refinement

    Weiqun Zhang;Ann S. Almgren;Vincent E. Beckner;John B. Bell

  • Optimization and Performance Modeling of Stencil Computations on Modern Microprocessors

    Kaushik Datta;Shoaib Kamil;Samuel Williams;Leonid Oliker

  • An auto-tuning framework for parallel multicore stencil computations

    Shoaib Kamil;Cy Chan;Leonid Oliker;John Shalf

  • Implicit and explicit optimizations for stencil computations

    Shoaib Kamil;Kaushik Datta;Samuel Williams;Leonid Oliker

  • An Efficient Multicore Implementation of a Novel HSS-Structured Multifrontal Solver Using Randomized Sampling

    Pieter Ghysels;Xiaoye S. Li;Francois Henry Rouet;Samuel Williams

  • Reduced-Bandwidth Multithreaded Algorithms for Sparse Matrix-Vector Multiplication

    Aydin Buluç;Samuel Williams;Leonid Oliker;James Demmel

  • Scientific Computing Kernels on the Cell Processor

    Samuel W. Williams;John Shalf;Leonid Oliker;Shoaib Kamil

  • Lattice Boltzmann simulation optimization on leading multicore platforms

    S. Williams;J. Carter;L. Oliker;J. Shalf

  • Auto-tuning performance on multicore computers

    David A. Patterson;Samuel Webb Williams

  • Roofline Model Toolkit: A Practical Tool for Architectural and Program Analysis

    Yu Jung Lo;Samuel Williams;Brian Van Straalen;Terry J. Ligocki

  • Exploiting Multiple Levels of Parallelism in Sparse Matrix-Matrix Multiplication

    Ariful Azad;Grey Ballard;Aydin Buluç;James Demmel

  • Optimizing Sparse Matrix-Multiple Vectors Multiplication for Nuclear Configuration Interaction Calculations

    Hasan Metin Aktulga;Aydin Buluç;Samuel Williams;Chao Yang

  • Optimizing and tuning the fast multipole method for state-of-the-art multicore architectures

    Aparna Chandramowlishwaran;Samuel Williams;Leonid Oliker;Ilya Lashuk

  • Optimization of geometric multigrid for emerging multi- and manycore processors

    Samuel Williams;Dhiraj D. Kalamkar;Amik Singh;Anand M. Deshpande

  • Sparse Matrix-Vector Multiplication on Multicore and Accelerators

    Samuel Williams;Nathan Bell;Jee Whan Choi;Michael Garland

  • An efficient multi-core implementation of a novel HSS-structured multifrontal solver using randomized sampling

    Pieter Ghysels;Xiaoye S. Li;Francois-Henry Rouet;Samuel Williams

Frequent Co-Authors

Leonid Oliker
Leonid Oliker Lawrence Berkeley National Laboratory
John Shalf
John Shalf Lawrence Berkeley National Laboratory
Katherine Yelick
Katherine Yelick University of California, Berkeley
Shoaib Kamil
Shoaib Kamil Adobe Systems (United States)
James Demmel
James Demmel University of California, Berkeley
Mary Hall
Mary Hall University of Utah
Aydin Buluc
Aydin Buluc Lawrence Berkeley National Laboratory
Xiaoye S. Li
Xiaoye S. Li Lawrence Berkeley National Laboratory
David A. Patterson
David A. Patterson University of California, Berkeley
Armando Fox
Armando Fox University of California, Berkeley

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