World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
45
Citations
8720
World Ranking
7174
National Ranking
3140

Overview

Stanimire Tomov is affiliated with the University of Tennessee at Knoxville in the United States. Their primary field of study is Computer Science, with a focus on Computational Theory and Mathematics, Hardware and Architecture, Computer Networks and Communications, Computational Mechanics, and Artificial Intelligence.

The scientist has contributed to multiple main topics of research, including:

  • Parallel Computing and Optimization Techniques
  • Numerical Methods and Algorithms
  • Matrix Theory and Algorithms
  • Advanced Data Storage Technologies
  • Advanced Numerical Methods in Computational Mathematics
  • Distributed and Parallel Computing Systems
  • Polynomial and Algebraic Computation

Recent publications by Stanimire Tomov include:

  • "A survey of numerical linear algebra methods utilizing mixed-precision arithmetic," 2021, The International Journal of High Performance Computing Applications
  • "Mixed-precision iterative refinement using tensor cores on GPUs to accelerate solution of linear systems," 2020, Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences
  • "Efficient exascale discretizations: High-order finite element methods," 2021, The International Journal of High Performance Computing Applications
  • "Load-balancing Sparse Matrix Vector Product Kernels on GPUs," 2020, ACM Transactions on Parallel Computing
  • "A Set of Batched Basic Linear Algebra Subprograms and LAPACK Routines," 2021, ACM Transactions on Mathematical Software

Frequent co-authors collaborating with Stanimire Tomov include Ahmad Abdelfattah, Jack Dongarra, Tzanio Kolev, Natalie Beams, and Jed Brown.

The scientist publishes regularly in several venues, among which the most frequent are:

  • Zenodo (CERN European Organization for Nuclear Research)
  • The International Journal of High Performance Computing Applications
  • arXiv (Cornell University)
  • Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences
  • ACM Transactions on Parallel Computing

Best Publications

  • Towards dense linear algebra for hybrid GPU accelerated manycore systems

    Stanimire Tomov;Jack Dongarra;Marc Baboulin

  • Numerical linear algebra on emerging architectures: The PLASMA and MAGMA projects

    Emmanuel Agullo;Jim Demmel;Jack Dongarra;Bilel Hadri

  • From CUDA to OpenCL: Towards a performance-portable solution for multi-platform GPU programming

    Peng Du;Rick Weber;Piotr Luszczek;Stanimire Tomov

  • Dense linear algebra solvers for multicore with GPU accelerators

    Stanimire Tomov;Rajib Nath;Hatem Ltaief;Jack Dongarra

  • An Improved Magma Gemm For Fermi Graphics Processing Units

    Rajib Nath;Stanimire Tomov;Jack Dongarra

  • Accelerating Scientific Computations with Mixed Precision Algorithms

    Marc Baboulin;Alfredo Buttari;Jack J. Dongarra;Jack J. Dongarra;Jack J. Dongarra;Jakub Kurzak

  • A Note on Auto-tuning GEMM for GPUs

    Yinan Li;Jack Dongarra;Stanimire Tomov

  • Harnessing GPU tensor cores for fast FP16 arithmetic to speed up mixed-precision iterative refinement solvers

    Azzam Haidar;Stanimire Tomov;Jack Dongarra;Nicholas J. Higham

  • The impact of multicore on math software

    Alfredo Buttari;Jack Dongarra;Jakub Kurzak;Julien Langou

  • Autotuning GEMM Kernels for the Fermi GPU

    J. Kurzak;S. Tomov;J. Dongarra

  • A Hybridization Methodology for High-Performance Linear Algebra Software for GPUs

    Emmanuel Agullo;Cédric Augonnet;Jack Dongarra;Hatem Ltaief

  • QR Factorization on a Multicore Node Enhanced with Multiple GPU Accelerators

    Emmanuel Agullo;Cedric Augonnet;Jack Dongarra;Mathieu Faverge

  • Accelerating Numerical Dense Linear Algebra Calculations with GPUs

    Jack J. Dongarra;Jack J. Dongarra;Jack J. Dongarra;Mark Gates;Azzam Haidar;Jakub Kurzak

  • Using Mixed Precision for Sparse Matrix Computations to Enhance the Performance while Achieving 64-bit Accuracy

    Alfredo Buttari;Jack Dongarra;Jakub Kurzak;Piotr Luszczek

  • Performance, Design, and Autotuning of Batched GEMM for GPUs

    Ahmad Abdelfattah;Azzam Haidar;Stanimire Tomov;Jack J. Dongarra;Jack J. Dongarra;Jack J. Dongarra

  • A survey of numerical linear algebra methods utilizing mixed-precision arithmetic:

    Ahmad Abdelfattah;Hartwig Anzt;Hartwig Anzt;Erik G. Boman;Erin C. Carson

  • Enabling and scaling matrix computations on heterogeneous multi-core and multi-GPU systems

    Fengguang Song;Stanimire Tomov;Jack Dongarra

  • Parallel Performance Measurement of Heterogeneous Parallel Systems with GPUs

    Allen D. Malony;Scott Biersdorff;Sameer Shende;Heike Jagode

  • Clinical proteomics and biomarker discovery

    Donald J. Johann;Michael D. Mcguigan;Amit R. Patel;Stanimire Tomov

  • Power Aware Computing on GPUs

    Kiran Kasichayanula;Dan Terpstra;Piotr Luszczek;Stan Tomov

  • Application of interactive parallel visualization for commodity-based clusters using visualization APIs

    Stanimire Tomov;Robert Bennett;Michael D. McGuigan;Arnold M. Peskin

Frequent Co-Authors

Jack Dongarra
Jack Dongarra University of Tennessee at Knoxville
Piotr Luszczek
Piotr Luszczek University of Tennessee at Knoxville
Jakub Kurzak
Jakub Kurzak Advanced Micro Devices (Canada)
Paul Fischer
Paul Fischer University of Illinois at Urbana-Champaign
George Bosilca
George Bosilca University of Tennessee at Knoxville
Lin-Wang Wang
Lin-Wang Wang Lawrence Berkeley National Laboratory
Nicholas J. Higham
Nicholas J. Higham University of Manchester
Tim Warburton
Tim Warburton Virginia Tech
Raytcho Lazarov
Raytcho Lazarov Texas A&M University
Thomas Herault
Thomas Herault University of Tennessee at Knoxville

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