World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
31
Citations
10633
World Ranking
13341
National Ranking
5337

Kim Hazelwood 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 Kim Hazelwood 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: 76 publications — 3rd percentile

3% of scientists in this discipline score the same or lower.

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

Kim Hazelwood 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 Kim Hazelwood 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: 31 D-Index — 6th percentile

6% of scientists in this discipline score the same or lower.

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

Overview

Kim Hazelwood is affiliated with Facebook in the United States and has contributed extensively to research in computer science, particularly focusing on efficiency and sustainability in artificial intelligence and hardware systems.

The scientist's research spans several subfields including:

  • Artificial Intelligence
  • Information Systems
  • Hardware and Architecture
  • Electrical and Electronic Engineering
  • Computer Networks and Communications

Kim Hazelwood's work frequently addresses topics such as:

  • Parallel Computing and Optimization Techniques
  • Ferroelectric and Negative Capacitance Devices
  • Green IT and Sustainability
  • Mobile Crowdsensing and Crowdsourcing
  • Software Engineering Research
  • Software Testing and Debugging Techniques
  • Recommender Systems and Techniques

The scientist has published in several prominent venues, with multiple contributions to:

  • arXiv (Cornell University)
  • IEEE Micro
  • ACM Transactions on Architecture and Code Optimization
  • TIB Data Manager

Recent papers include:

  • Sustainable AI: Environmental Implications, Challenges and Opportunities (2021, arXiv (Cornell University))
  • Beyond Efficiency: Scaling AI Sustainably (2024, IEEE Micro)
  • Datacenter-Scale Analysis and Optimization of GPU Machine Learning Workloads (2021, IEEE Micro)
  • Large Language Models for Compiler Optimization (2023, arXiv (Cornell University))
  • Understanding Training Efficiency of Deep Learning Recommendation Models at Scale (2020, arXiv (Cornell University))

Frequent collaborators in research include:

  • Carole-Jean Wu
  • Bilge Acun
  • Hugh Leather
  • Ramya Raghavendra
  • Chris Cummins

Best Publications

  • Pin: building customized program analysis tools with dynamic instrumentation

    Chi-Keung Luk;Robert Cohn;Robert Muth;Harish Patil

  • Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective

    Kim Hazelwood;Sarah Bird;David Brooks;Soumith Chintala

  • Profiling a Warehouse-Scale Computer

    Svilen Kanev;Juan Pablo Darago;Kim Hazelwood;Parthasarathy Ranganathan

  • Machine Learning at Facebook: Understanding Inference at the Edge

    Carole-Jean Wu;David Brooks;Kevin Chen;Douglas Chen

  • Where is the data? Why you cannot debate CPU vs. GPU performance without the answer

    Chris Gregg;Kim Hazelwood

  • Sustainable AI: Environmental Implications, Challenges and Opportunities.

    Carole-Jean Wu;Ramya Raghavendra;Udit Gupta;Bilge Acun

  • The Architectural Implications of Facebook's DNN-Based Personalized Recommendation

    Udit Gupta;Carole-Jean Wu;Xiaodong Wang;Maxim Naumov

  • RecNMP: accelerating personalized recommendation with near-memory processing

    Liu Ke;Udit Gupta;Benjamin Youngjae Cho;David Brooks

  • MLPerf Training Benchmark.

    Peter Mattson;Christine Cheng;Cody Coleman;Greg Diamos

  • Reducing DRAM footprint with NVM in Facebook

    Assaf Eisenman;Darryl Gardner;Islam AbdelRahman;Jens Axboe

  • Analyzing Parallel Programs with Pin

    M. Bach;M. Charney;R. Cohn;E. Demikhovsky

  • Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications

    Jongsoo Park;Maxim Naumov;Protonu Basu;Summer Deng

  • MLPerf Training Benchmark

    Peter Mattson;Christine Cheng;Gregory F. Diamos;Cody Coleman

  • SuperPin: Parallelizing Dynamic Instrumentation for Real-Time Performance

    Steven Wallace;Kim Hazelwood

  • Fine-grained resource sharing for concurrent GPGPU kernels

    Chris Gregg;Jonathan Dorn;Kim Hazelwood;Kevin Skadron

  • A dynamic binary instrumentation engine for the ARM architecture

    Kim Hazelwood;Artur Klauser

  • Tradeoffs between power management and tail latency in warehouse-scale applications

    Svilen Kanev;Kim Hazelwood;Gu-Yeon Wei;David Brooks

  • Adaptive online context-sensitive inlining

    Kim Hazelwood;David Grove

  • Understanding Training Efficiency of Deep Learning Recommendation Models at Scale

    Bilge Acun;Matthew Murphy;Xiaodong Wang;Jade Nie

  • Dynamic Heterogeneous Scheduling Decisions Using Historical Runtime Data

    Chris Gregg;Michael Boyer;Kim Hazelwood;Kevin Skadron

  • The Architectural Implications of Facebook's DNN-based Personalized Recommendation

    Udit Gupta;Carole-Jean Wu;Xiaodong Wang;Maxim Naumov

Frequent Co-Authors

David Brooks
David Brooks Harvard University
Carole-Jean Wu
Carole-Jean Wu Meta Platforms, Inc.
Gu-Yeon Wei
Gu-Yeon Wei Harvard University
Mary Lou Soffa
Mary Lou Soffa University of Virginia
Hsien-Hsin S. Lee
Hsien-Hsin S. Lee Intel (United States)
Mikhail Smelyanskiy
Mikhail Smelyanskiy Nvidia (United States)
Vijay Janapa Reddi
Vijay Janapa Reddi Harvard University
Matei Zaharia
Matei Zaharia University of California, Berkeley
Sungjoo Yoo
Sungjoo Yoo Seoul National University
Sachin Katti
Sachin Katti Stanford University

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