World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

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

Stanimire Tomov publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Stanimire Tomov sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 264 publications — 66th percentile

66% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Stanimire Tomov D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Stanimire Tomov sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 45 D-Index — 51st percentile

51% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

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
Ananias G. Tomboulides
Ananias G. Tomboulides Aristotle University of Thessaloniki
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

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring quick degrees that pay well can be ideal for those eager to fast-track their entry into the tech industry. Many online programs now offer accelerated paths tailored for students who want valuable credentials without spending years in school.

For those interested in specialized fields, a growing number of online degrees in AI provide training in one of the hottest areas in computer science. These programs often blend computer science fundamentals with machine learning, data science, and real-world projects.

Choosing the right program in college is crucial for aligning your studies with future career goals. Computer science, data analytics, and engineering all open doors to diverse, high-growth roles in technology, business, and research.

If you’re considering advanced study, look into easy masters programs to get into for a flexible and accessible path to gain new skills or transition careers. Online master’s options can fit a busy lifestyle and still boost your job prospects.

Best Scientists Citing Stanimire Tomov

Trending Scientists

Recently Published Articles