World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
33
Citations
9396
World Ranking
12387
National Ranking
5019

Overview

Changkyu Kim is affiliated with Facebook in the United States, where they conduct research primarily in the field of Computer Science. Their work focuses on domains such as Computer Networks and Communications, Information Systems, Artificial Intelligence, Computer Vision and Pattern Recognition, and Hardware and Architecture.

Their research addresses a range of topics, including:

  • Advanced Data Storage Technologies
  • Recommender Systems and Techniques
  • Caching and Content Delivery
  • Parallel Computing and Optimization Techniques
  • Advanced Neural Network Applications
  • Stochastic Gradient Optimization Techniques
  • Machine Learning and Data Classification

Changkyu Kim has contributed to several publications, a selection of which includes:

  • Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systems, 2020, arXiv (Cornell University)
  • First-Generation Inference Accelerator Deployment at Facebook, 2021, arXiv (Cornell University)
  • Supporting Massive DLRM Inference through Software Defined Memory, 2022, 2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)
  • Low-Precision Hardware Architectures Meet Recommendation Model Inference at Scale, 2021, IEEE Micro
  • Low-Precision Hardware Architectures Meet Recommendation Model Inference at Scale, 2021, arXiv (Cornell University)

The frequent venues where Changkyu Kim publishes include:

  • arXiv (Cornell University)
  • 2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)
  • IEEE Micro

They have collaborated frequently with several co-authors, including:

  • Hector Yuen
  • Maxim Naumov
  • Mikhail Smelyanskiy
  • Ehsan K. Ardestani
  • Dheevatsa Mudigere

Best Publications

  • Debunking the 100X GPU vs. CPU myth: an evaluation of throughput computing on CPU and GPU

    Victor W. Lee;Changkyu Kim;Jatin Chhugani;Michael Deisher

  • An adaptive, non-uniform cache structure for wire-delay dominated on-chip caches

    Changkyu Kim;Doug Burger;Stephen W. Keckler

  • Exploiting ILP, TLP, and DLP with the polymorphous trips architecture

    K. Sankaralingam;R. Nagarajan;Haiming Liu;Changkyu Kim

  • A NUCA Substrate for Flexible CMP Cache Sharing

    J. Jaehyuk Huh;C. Changkyu Kim;H. Shafi;L. Lixin Zhang

  • FAST: fast architecture sensitive tree search on modern CPUs and GPUs

    Changkyu Kim;Jatin Chhugani;Nadathur Satish;Eric Sedlar

  • Sort vs. Hash revisited: fast join implementation on modern multi-core CPUs

    Changkyu Kim;Tim Kaldewey;Victor W. Lee;Eric Sedlar

  • ClearPath: highly parallel collision avoidance for multi-agent simulation

    Stephen. J. Guy;Jatin Chhugani;Changkyu Kim;Nadathur Satish

  • 3.5-D Blocking Optimization for Stencil Computations on Modern CPUs and GPUs

    Anthony Nguyen;Nadathur Satish;Jatin Chhugani;Changkyu Kim

  • Fast sort on CPUs and GPUs: a case for bandwidth oblivious SIMD sort

    Nadathur Satish;Changkyu Kim;Jatin Chhugani;Anthony D. Nguyen

  • DySER: Unifying Functionality and Parallelism Specialization for Energy-Efficient Computing

    V. Govindaraju;Chen-Han Ho;T. Nowatzki;J. Chhugani

  • Second Life and the New Generation of Virtual Worlds

    S. Kumar;J. Chhugani;Changkyu Kim;Daehyun Kim

  • Implementation and Evaluation of On-Chip Network Architectures

    P. Gratz;Changkyu Kim;R. McDonald;S.W. Keckler

  • Composable Lightweight Processors

    Changkyu Kim;S. Sethumadhavan;D. Gulati;D. Burger

  • Distributed Microarchitectural Protocols in the TRIPS Prototype Processor

    Karthikeyan Sankaralingam;Ramadass Nagarajan;Robert McDonald;Rajagopalan Desikan

  • Fast updates on read-optimized databases using multi-core CPUs

    Jens Krueger;Changkyu Kim;Martin Grund;Nadathur Satish

  • PALM: parallel architecture-friendly latch-free modifications to B+ trees on many-core processors

    Jason Sewall;Jatin Chhugani;Changkyu Kim;Nadathur Satish

  • Nonuniform cache architectures for wire-delay dominated on-chip caches

    Changkyu Kim;D. Burger;S.W. Keckler

  • On-Chip Interconnection Networks of the TRIPS Chip

    P. Gratz;Changkyu Kim;K. Sankaralingam;H. Hanson

  • Can traditional programming bridge the Ninja performance gap for parallel computing applications

    Nadathur Satish;Changkyu Kim;Jatin Chhugani;Hideki Saito

  • TRIPS: A polymorphous architecture for exploiting ILP, TLP, and DLP

    Karthikeyan Sankaralingam;Ramadass Nagarajan;Haiming Liu;Changkyu Kim

  • Fast and Efficient Graph Traversal Algorithm for CPUs: Maximizing Single-Node Efficiency

    Jatin Chhugani;Nadathur Satish;Changkyu Kim;Jason Sewall

Frequent Co-Authors

Nadathur Satish
Nadathur Satish Facebook (United States)
Pradeep Dubey
Pradeep Dubey Intel (United States)
Doug Burger
Doug Burger Microsoft (United States)
Stephen W. Keckler
Stephen W. Keckler Nvidia (United States)
Mikhail Smelyanskiy
Mikhail Smelyanskiy Nvidia (United States)
Yen-Kuang Chen
Yen-Kuang Chen Alibaba Group (China)
Karthikeyan Sankaralingam
Karthikeyan Sankaralingam University of Wisconsin–Madison
Eric Sedlar
Eric Sedlar Oracle (United States)
Lixin Zhang
Lixin Zhang East China University of Science and Technology
Scott A. Brandt
Scott A. Brandt University of California, Santa Cruz

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

Studying Computer Science in the USA opens up a wide range of opportunities, especially with the growth of online programs. An online computer science degree is ideal for students seeking flexibility or wishing to accelerate their studies. Many of these programs are designed for working professionals or those wanting to balance other commitments.

If you’re worried about your academic history, don’t fret—there are best colleges for low gpa applicants, making it possible to pursue quality education even if your grades aren’t perfect.

Looking beyond computer science, related fields like environmental science and engineering offer exciting career options. You might be curious about what can you get with an environmental science degree. Graduates can work in research, policy, consultancy, and more. Meanwhile, those interested in sustainable technology and infrastructure should consider the online environmental engineering degree pathways, which are both accessible and often affordable.

Exploring these related programs can help you find the right fit for your interests and long-term goals in technology and science.

Best Scientists Citing Changkyu Kim

Trending Scientists