From cTuning.org
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| - | *'''2009.August.05''' - The colleagues from the [http://unidapt.org UNIDAPT Group] started investigating the use of [http://ctuning.org cTuning]/[http://www.milepost.eu MILEPOST] technology and the [http://ctuning.org/unidapt UNIDAPT framework] to predict good optimization and parallelization schemes for hybrid heterogeneous CPU/GPU-like architectures based on statistical analysis, machine learning, program and dataset features and run-time decision trees | + | *'''2009.August.05''' - The colleagues from the [http://unidapt.org UNIDAPT Group] started investigating the use of [http://ctuning.org cTuning]/[http://www.milepost.eu MILEPOST] technology and the [http://ctuning.org/unidapt UNIDAPT framework] to predict good optimization and parallelization schemes for hybrid heterogeneous CPU/GPU-like architectures together with [http://www.caps-entreprise.com CAPS Entreprise] based on run-time adaptation and profiling, empirical iterative compilation, statistical analysis, machine learning, program and dataset features and run-time decision trees ({{Ref|FT2009}}, {{Ref|LCWP2009}}, {{Ref|Fur2009}}, {{Ref|JGVP2009}}, {{Ref1|TWFP2009}}, {{Ref|FMTP2008}}, {{Ref|LFF2007}}, {{Ref|FCOP2005}}). They plan to add new optimization cases to the [http://ctuning.org/cdatabase Collective Optimization Database] in Autumn, 2009. |
*'''2009.July.27''' - The paper "Portable Compiler Optimization Across Embedded Programs and Microarchitectures using Machine Learning" ({{Ref|DJBP2009}}) has been accepted for the [http://www.microarch.org/micro42 42nd IEEE/ACM International Symposium on Microarchitecture (MICRO)]. The research has been led by the colleagues from the University of Edinburgh - congratulations! | *'''2009.July.27''' - The paper "Portable Compiler Optimization Across Embedded Programs and Microarchitectures using Machine Learning" ({{Ref|DJBP2009}}) has been accepted for the [http://www.microarch.org/micro42 42nd IEEE/ACM International Symposium on Microarchitecture (MICRO)]. The research has been led by the colleagues from the University of Edinburgh - congratulations! | ||
Revision as of 11:09, 7 August 2009
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Continuous Collective Compilation Framework |
| Enabling collective optimization |
Navigation: cTuning.org > CTools Continuous Collective Compilation Framework (CCC) is a collaborative modular plugin-enabled R&D infrastructure to automate program and architecture optimizations (i.e. iteratively search for good program and architecture optimizations in a feedback-directed manner) and gather various static and dynamic optimization profile data in a Collective Optimization Database. It supports both global program optimization and fine-grain procedure, loop or instruction level optimizations if compiler supports Interactive Compilation Interface. It is used to help end-users optimize their programs, libraries and whole OS automatically (improve execution time/code size, etc), test and tune compiler optimization heuristic. It also enables collaborative R&D and optimization knowledge reuse with statistical and machine learning techniques (FMTP2008, FT2009). CCC Framework is a community-driven project - you are welcome to join the project, extend it, provide smart search and data analysis plugins, leave feedback and add your optimization data to help the community. You can also communicate with cTuning community through our mailing lists.
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You are welcome to join us and participate in CCC developments, discussions, provide feedback or suggestions to extend CCC or add new functionality. The framework currently supports multiple compilers including GCC, Open64, PathScale, Intel ICC, IBM XLC and a large number of server, desktop and embedded architectures.
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You are welcome to register your interest at this page. |