World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
56
Citations
27176
World Ranking
3942
National Ranking
1874

Michael Garland 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 Michael Garland 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: 140 publications — 23rd percentile

23% of scientists in this discipline score the same or lower.

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

Michael Garland 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 Michael Garland 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: 56 D-Index — 72nd percentile

72% of scientists in this discipline score the same or lower.

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

Overview

Michael Garland is affiliated with Nvidia in the United States and conducts research primarily in the field of Computer Science, with a focus on Artificial Intelligence, Hardware and Architecture, and Computer Networks and Communications. Their work spans several subfields, including Computer Vision and Pattern Recognition and Computational Mechanics.

The main topics of Garland's research include:

  • Parallel Computing and Optimization Techniques
  • Machine Learning and Data Classification
  • Advanced Neural Network Applications
  • Tensor decomposition and applications
  • Computational Physics and Python Applications
  • Distributed and Parallel Computing Systems
  • Algorithms and Data Compression

Garland has contributed to various publication venues. The most frequent include:

  • arXiv (Cornell University)
  • IEEE Micro
  • ACM Transactions on Architecture and Code Optimization
  • Computing in Science & Engineering
  • Proceedings on Privacy Enhancing Technologies

Among recent scholarly works involving Garland or related closely to their research network, notable papers include:

  • "A Programmable Approach to Neural Network Compression," 2020, IEEE Micro
  • "Supercomputing in Python With Legate," 2021, Computing in Science & Engineering
  • "Stream-K: Work-centric Parallel Decomposition for Dense Matrix-Matrix Multiplication on the GPU," 2023, arXiv (Cornell University)
  • "Exploring Data Layout for Sparse Tensor Times Dense Matrix on GPUs," 2023, ACM Transactions on Architecture and Code Optimization
  • "GPU-Initiated On-Demand High-Throughput Storage Access in the BaM System Architecture," 2022, arXiv (Cornell University)

The network of frequent coauthors in Garland's research includes:

  • V. Roshan Joseph
  • Saurav Muralidharan
  • Michael Bauer
  • Charles Gouert
  • Steven Dalton

Best Publications

  • Surface simplification using quadric error metrics

    Michael Garland;Paul S. Heckbert

  • Scalable Parallel Programming with CUDA: Is CUDA the parallel programming model that application developers have been waiting for?

    John Nickolls;Ian Buck;Michael Garland;Kevin Skadron

  • Scalable parallel programming with CUDA

    John Nickolls;Ian Buck;Michael Garland;Kevin Skadron

  • Implementing sparse matrix-vector multiplication on throughput-oriented processors

    Nathan Bell;Michael Garland

  • Ecient Sparse Matrix-Vector Multiplication on CUDA

    Nathan Bell;Michael Garland

  • Designing efficient sorting algorithms for manycore GPUs

    Nadathur Satish;Mark Harris;Michael Garland

  • GPUs and the Future of Parallel Computing

    S. W. Keckler;W. J. Dally;B. Khailany;M. Garland

  • Survey of Polygonal Surface Simplification Algorithms

    Paul S. Heckbert;Michael Garland

  • Parallel Computing Experiences with CUDA

    M. Garland;S. Le Grand;J. Nickolls;J. Anderson

  • Simplifying surfaces with color and texture using quadric error metrics

    Michael Garland;Paul S. Heckbert

  • Scalable GPU graph traversal

    Duane Merrill;Michael Garland;Andrew Grimshaw

  • Fast BVH Construction on GPUs

    Christian Lauterbach;Michael Garland;Shubhabrata Sengupta;David P. Luebke

  • Hierarchical face clustering on polygonal surfaces

    Michael Garland;Andrew Willmott;Paul S. Heckbert

  • Multiresolution Modeling: Survey and Future Opportunities

    Michael Garland

  • Spectral surface quadrangulation

    Shen Dong;Peer-Timo Bremer;Michael Garland;Valerio Pascucci

  • Quadric-based polygonal surface simplification

    Michael Garland;Paul Heckbert

  • Fast Polygonal Approximation of Terrains and Height Fields

    Michael Garland

  • Multiresolution Modeling for Fast Rendering

    Paul S. Heckbert;Michael Garland

  • Optimal triangulation and quadric-based surface simplification

    Paul S. Heckbert;Michael Garland

  • Copperhead: compiling an embedded data parallel language

    Bryan Catanzaro;Michael Garland;Kurt Keutzer

  • Fair morse functions for extracting the topological structure of a surface mesh

    Xinlai Ni;Michael Garland;John C. Hart

Frequent Co-Authors

Paul S. Heckbert
Paul S. Heckbert Carnegie Mellon University
John Hart
John Hart University of Illinois at Urbana-Champaign
David Luebke
David Luebke Nvidia (United States)
Timo Aila
Timo Aila Aalto University
Mary Hall
Mary Hall University of Utah
Ganesh Gopalakrishnan
Ganesh Gopalakrishnan University of Utah
Samuli Laine
Samuli Laine Nvidia (United States)
Bryan Catanzaro
Bryan Catanzaro Nvidia (United States)
Andrew S. Grimshaw
Andrew S. Grimshaw University of Virginia
John D. Owens
John D. Owens University of California, Davis

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