World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
76
Citations
22688
World Ranking
1346
National Ranking
705

Overview

Kunle Olukotun is affiliated with Stanford University in the United States and has contributed extensively to the field of computer science. Their body of work spans multiple subfields including hardware and architecture, computer networks and communications, artificial intelligence, electrical and electronic engineering, and management science and operations research.

The scientist has focused research efforts on several main topics, including parallel computing and optimization techniques, embedded systems design techniques, interconnection networks and systems, distributed and parallel computing systems, machine learning and data classification, advanced bandit algorithms research, and cloud computing and resource management.

Olukotun has published in a variety of venues, frequently contributing to arXiv (Cornell University) with 14 publications. Other notable venues include the Proceedings of the ACM on Programming Languages with two publications, Lecture Notes in Computer Science, IEEE Computer Architecture Letters, and the 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW).

Recent papers authored or co-authored by Olukotun include:

  • Compilation of sparse array programming models, 2021, Proceedings of the ACM on Programming Languages
  • Mosaic: An Interoperable Compiler for Tensor Algebra, 2023, Proceedings of the ACM on Programming Languages
  • Stardust: Compiling Sparse Tensor Algebra to a Reconfigurable Dataflow Architecture, 2022, arXiv (Cornell University)
  • Implementing and Optimizing the Scaled Dot-Product Attention on Streaming Dataflow, 2024, arXiv (Cornell University)
  • Bayesian Optimization with a Prior for the Optimum, 2021, Lecture Notes in Computer Science

Frequent collaborators of Olukotun include Olivia Hsu, Fredrik Kjølstad, Raghu Prabhakar, Yaqi Zhang, and Luigi Nardi, illustrating a network of co-authorship across various projects and publications.

Best Publications

  • Map-Reduce for Machine Learning on Multicore

    Cheng-tao Chu;Sang K. Kim;Yi-an Lin;Yuanyuan Yu

  • Niagara: a 32-way multithreaded Sparc processor

    P. Kongetira;K. Aingaran;K. Olukotun

  • STAMP: Stanford Transactional Applications for Multi-Processing

    Chi Cao Minh;JaeWoong Chung;C. Kozyrakis;K. Olukotun

  • The case for a single-chip multiprocessor

    Kunle Olukotun;Basem A. Nayfeh;Lance Hammond;Ken Wilson

  • Transactional Memory Coherence and Consistency

    Lance Hammond;Vicky Wong;Mike Chen;Brian D. Carlstrom

  • A single-chip multiprocessor

    B.A. Nayfeh;K. Olukotun

  • Data speculation support for a chip multiprocessor

    Lance Hammond;Mark Willey;Kunle Olukotun

  • Accelerating CUDA graph algorithms at maximum warp

    Sungpack Hong;Sang Kyun Kim;Tayo Oguntebi;Kunle Olukotun

  • An effective hybrid transactional memory system with strong isolation guarantees

    Chi Cao Minh;Martin Trautmann;JaeWoong Chung;Austen McDonald

  • Efficient Parallel Graph Exploration on Multi-Core CPU and GPU

    Sungpack Hong;Tayo Oguntebi;Kunle Olukotun

  • The Future of Microprocessors: Chip multiprocessors’ promise of huge performance gains is now a reality.

    Kunle Olukotun;Lance Hammond

  • Green-Marl: a DSL for easy and efficient graph analysis

    Sungpack Hong;Hassan Chafi;Edic Sedlar;Kunle Olukotun

  • REMARC : Reconfigurable Multimedia Array Coprocessor

    Takashi Miyamori;Kunle Olukotun

  • Architectural Semantics for Practical Transactional Memory

    Austen McDonald;JaeWoong Chung;Brian D. Carlstrom;Chi Cao Minh

  • A practical concurrent binary search tree

    Nathan G. Bronson;Jared Casper;Hassan Chafi;Kunle Olukotun

  • Multi-core multi-thread processor

    Leslie D. Kohn;Kunle A. Olukotun;Michael K. Wong

  • OptiML: An Implicitly Parallel Domain-Specific Language for Machine Learning

    Arvind Sujeeth;Hyoukjoong Lee;Kevin Brown;Tiark Rompf

  • A Heterogeneous Parallel Framework for Domain-Specific Languages

    Kevin J. Brown;Arvind K. Sujeeth;Hyouk Joong Lee;Tiark Rompf

  • Delite: A Compiler Architecture for Performance-Oriented Embedded Domain-Specific Languages

    Arvind K. Sujeeth;Kevin J. Brown;Hyoukjoong Lee;Tiark Rompf

  • Plasticine: A Reconfigurable Architecture For Parallel Paterns

    Raghu Prabhakar;Yaqi Zhang;David Koeplinger;Matt Feldman

  • EmptyHeaded: A Relational Engine for Graph Processing

    Christopher R. Aberger;Andrew Lamb;Susan Tu;Andres Nötzli

Frequent Co-Authors

Christos Kozyrakis
Christos Kozyrakis Stanford University
Hassan Chafi
Hassan Chafi Oracle (US)
Christopher Ré
Christopher Ré Stanford University
Tiark Rompf
Tiark Rompf Purdue University West Lafayette
Martin Odersky
Martin Odersky École Polytechnique Fédérale de Lausanne
Peter Bailis
Peter Bailis Stanford University
Ce Zhang
Ce Zhang ETH Zurich
Trevor Mudge
Trevor Mudge University of Michigan–Ann Arbor
Matei Zaharia
Matei Zaharia University of California, Berkeley
Jaswinder Pal Singh
Jaswinder Pal Singh Princeton University

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