Êtes-vous un étudiant de l'EPFL à la recherche d'un projet de semestre?
Travaillez avec nous sur des projets en science des données et en visualisation, et déployez votre projet sous forme d'application sur GraphSearch.
Concept
Collective Tuning Initiative
Résumé
The Collective Tuning Initiative is a community-driven initiative started by Grigori Fursin to develop free collaborative open-source research tools with unified API for code and architecture characterization, optimization and co-design. This enables the sharing of benchmarks, data sets and optimization cases from the community in the open optimization repository through unified web services to predict better optimizations or architecture designs (provided there is enough information collected in the repository from multiple users). Using common research-and-development tools should help to improve the quality and reproducibility of research into code, architecture design and optimization, encouraging innovation in this area. This approach helped establish Artifact Evaluation at several ACM-sponsored conferences to encourage sharing of artifacts and validation of experimental results from accepted papers.
The tools and repository include:
Collective Optimization Database: Open repository to share optimization cases from the community, provide web services and plugins to analyze collective optimization data and predict program optimizations based on statistical and machine-learning techniques and improve the quality and reproducibility of the compiler (and architecture research)
Online machine learning-based program optimization predictor: Suggests optimization-improving factors such as execution time, code size and compilation time, based on similarities between programs (program features)
Continuous Collective Compilation Framework: Automates and distribute iterative feedback-directed exploration of large optimization spaces by multiple users
Interactive Compilation Interface: Opens and transforms production compilers into stable interactive research tool sets using an event-driven plugin system to avoid the development of new research compilers from scratch
Collective benchmark with multiple data sets: Enables realistic benchmarking and research on iterative compilation and run-time adaptation.
Source officielle
À propos de ce résultat
Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.