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Michael F. P. O'Boyle

Michael F. P. O'Boyle

D-Index & Metrics

Computer Science

D-Index
54
Citations
9012
World Ranking
4656
National Ranking
282

Overview

Michael F. P. O'Boyle is affiliated with the University of Edinburgh in the United Kingdom. Their research spans multiple fields, with a primary focus on Computer Science and Social Sciences. Within these disciplines, they have contributed significantly to several subfields, including Political Science and International Relations, Artificial Intelligence, Computer Vision and Pattern Recognition, Hardware and Architecture, and Law.

Their main research topics cover a range of areas:

  • European Criminal Justice and Data Protection
  • Advanced Neural Network Applications
  • Parallel Computing and Optimization Techniques
  • European and International Law Studies
  • Software Engineering Research
  • Adversarial Robustness in Machine Learning
  • Software Testing and Debugging Techniques

O'Boyle's publication record includes work in various well-known academic venues. Frequent publication venues include:

  • arXiv (Cornell University)
  • IEEE Power and Energy Magazine
  • The Electricity Journal
  • ACM Transactions on Architecture and Code Optimization
  • Proceedings of the ACM on Programming Languages

Recent papers authored or co-authored by O'Boyle include:

  • "Carbon-Free Energy: How Much, How Soon?" (2021), IEEE Power and Energy Magazine
  • "A new era for rural electric cooperatives: New clean energy investments, supported by federal incentives, will reduce rates, emissions, and reliance on outside power" (2023), The Electricity Journal
  • "Learning C to x86 Translation: An Experiment in Neural Compilation" (2021), arXiv (Cornell University)
  • "Retrofitting Symbolic Holes to LLVM IR" (2020), arXiv (Cornell University)
  • "M3: Semantic API Migrations" (2020), arXiv (Cornell University)

Frequent collaborators in O'Boyle's research include:

  • Carla Buckley
  • Krešimir Kamber
  • ZoË Bryanston-Cross
  • Peter Cumper
  • Heather Green

In addition to journal and conference publications, O'Boyle has contributed to book literature. One notable book published with Oxford University Press is "Harris, O'Boyle, and Warbrick: Law of the European Convention on Human Rights" (2023).

Best Publications

  • Using Machine Learning to Focus Iterative Optimization

    F. Agakov;E. Bonilla;J. Cavazos;B. Franke

  • Rapidly Selecting Good Compiler Optimizations using Performance Counters

    John Cavazos;Grigori Fursin;Felix Agakov;Edwin Bonilla

  • Milepost GCC: Machine Learning Enabled Self-tuning Compiler

    Grigori Fursin;Yuriy Kashnikov;Abdul Wahid Memon;Zbigniew Chamski

  • A static task partitioning approach for heterogeneous systems using OpenCL

    Dominik Grewe;Michael F. P. O'Boyle

  • Towards a holistic approach to auto-parallelization: integrating profile-driven parallelism detection and machine-learning based mapping

    Georgios Tournavitis;Zheng Wang;Björn Franke;Michael F.P. O'Boyle

  • Mapping parallelism to multi-cores: a machine learning based approach

    Zheng Wang;Michael F.P. O'Boyle

  • Combined Selection of Tile Sizes and Unroll Factors Using Iterative Compilation

    T. Kisuki;P. M. W. Knijnenburg;M. F. P. O'Boyle

  • Machine Learning in Compiler Optimization

    Zheng Wang;Michael O'Boyle

  • Automatic feature generation for machine learning--based optimising compilation

    Hugh Leather;Edwin Bonilla;Michael O'boyle

  • Portable mapping of data parallel programs to OpenCL for heterogeneous systems

    D. Grewe;Zheng Wang;M. F. P. O'Boyle

  • Introducing SLAMBench, a performance and accuracy benchmarking methodology for SLAM

    Luigi Nardi;Bruno Bodin;M. Zeeshan Zia;John Mawer

  • Smart multi-task scheduling for OpenCL programs on CPU/GPU heterogeneous platforms

    Yuan Wen;Zheng Wang;Michael F. P. O'Boyle

  • Code Generation and Optimization, 2007. CGO '07. International Symposium on

    John Cavazos;Grigori Fursin;Felix Agakov;Edwin V. Bonilla

  • Automatic Feature Generation for Machine Learning Based Optimizing Compilation

    Hugh Leather;Edwin Bonilla;Michael O'Boyle

  • Evaluating iterative compilation

    G. G. Fursin;M. F. P. O'boyle;P. M. W. Knijnenburg

  • MILEPOST GCC: machine learning based research compiler

    Grigori Fursin;Cupertino Miranda;Olivier Temam;Mircea Namolaru

  • Method-specific dynamic compilation using logistic regression

    John Cavazos;Michael F. P. O'Boyle

  • Nonsingular Data Transformations: Definition, Validity, and Applications

    Michael F. P. O'Boyle;Peter M. W. Knijnenburg

  • Partitioning streaming parallelism for multi-cores: a machine learning based approach

    Zheng Wang;Michael F.P. O'Boyle

  • Fast compiler optimisation evaluation using code-feature based performance prediction

    Christophe Dubach;John Cavazos;Björn Franke;Grigori Fursin

  • Combined Selection of Tile Sizes and Unroll Factors Using Iterative Compilation

    Peter M. W. Knijnenburg;Toru Kisuki;Michael F. P. O'Boyle

Frequent Co-Authors

Olivier Temam
Olivier Temam DeepMind (United Kingdom)
Amos Storkey
Amos Storkey University of Edinburgh
Antonio Gonzalez
Antonio Gonzalez Universitat Politècnica de Catalunya
Andrew J. Davison
Andrew J. Davison Imperial College London
André Seznec
André Seznec French Institute for Research in Computer Science and Automation - INRIA
Keshav Pingali
Keshav Pingali The University of Texas at Austin
Steve Furber
Steve Furber University of Manchester
Ronald G. Dreslinski
Ronald G. Dreslinski University of Michigan–Ann Arbor
Elad Yom-Tov
Elad Yom-Tov Microsoft (United States)
Trevor Mudge
Trevor Mudge University of Michigan–Ann Arbor

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