World's Best Scientists 2026 revealed!

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Computer Science

D-Index
31
Citations
10633
World Ranking
13344
National Ranking
5338

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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