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Grigori Fursin
Researcher at French Institute for Research in Computer Science and Automation
Publications - 72
Citations - 2585
Grigori Fursin is an academic researcher from French Institute for Research in Computer Science and Automation. The author has contributed to research in topics: Compiler & Optimizing compiler. The author has an hindex of 23, co-authored 71 publications receiving 2430 citations. Previous affiliations of Grigori Fursin include University of Paris-Sud & University of Edinburgh.
Papers
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Proceedings ArticleDOI
Using Machine Learning to Focus Iterative Optimization
Felix Agakov,Edwin V. Bonilla,John Cavazos,Björn Franke,Grigori Fursin,Michael O'Boyle,John Thomson,Marc Toussaint,Christopher Williams +8 more
TL;DR: A new methodology is developed that uses predictive modelling from the domain of machine learning to automatically focus search on those areas likely to give greatest performance, independent of search algorithm, search space or compiler infrastructure and scales gracefully with the compiler optimization space size.
Proceedings ArticleDOI
Rapidly Selecting Good Compiler Optimizations using Performance Counters
TL;DR: This paper proposes a different approach using performance counters as a means of determining good compiler optimization settings by learning a model off-line which can then be used to determine good settings for any new program.
Journal ArticleDOI
Milepost GCC: Machine Learning Enabled Self-tuning Compiler
Grigori Fursin,Yuriy Kashnikov,Abdul Wahid Memon,Zbigniew Chamski,Olivier Temam,Mircea Namolaru,Elad Yom-Tov,Bilha Mendelson,Ayal Zaks,Eric Courtois,François Bodin,Phil Barnard,Elton Ashton,Edwin V. Bonilla,John Thomson,Christopher Williams,Michael O'Boyle +16 more
TL;DR: Milepost GCC is described, the first publicly-available open-source machine learning-based compiler that automatically adapts the internal optimization heuristic at function-level granularity to improve execution time, code size and compilation time of a new program on a given architecture.
Book ChapterDOI
Predictive Runtime Code Scheduling for Heterogeneous Architectures
TL;DR: It is demonstrated that a novel predictive user-level scheduler based on past performance history for heterogeneous systems allows multiple applications to fully utilize all available processing resources in CPU/GPU-like systems and consistently achieve speedups ranging from 30% to 40% compared to just using the GPU in a single application mode.
MILEPOST GCC: machine learning based research compiler
Grigori Fursin,Cupertino Miranda,Olivier Temam,Mircea Namolaru,Elad Yom-Tov,Ayal Zaks,Bilha Mendelson,Edwin V. Bonilla,John Thomson,Hugh Leather,Christopher Williams,Michael O'Boyle,Phil Barnard,Elton Ashton,Eric Courtois,François Bodin +15 more
TL;DR: MILEPOST 1 GCC is described, a machine-learning-based compiler that automatically adjusts its optimization heuristics to improve the execution time, code size, or compilation time of specific programs on different architectures.