From cTuning.org
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*'''2009.November.7''' - Submission deadline for [http://cTuning.org/workshop-smart10 SMART'10 workshop] has been extended until the 22nd of November, 2009. | *'''2009.November.7''' - Submission deadline for [http://cTuning.org/workshop-smart10 SMART'10 workshop] has been extended until the 22nd of November, 2009. | ||
- | *'''2009.October.2''' - We successfully passed the final [http:// | + | *'''2009.October.2''' - We successfully passed the final [http://ctuning.org/wiki/index.php/Dissemination:Projects:MILEPOST MILEPOST] review and the project is officially over. We would like to thank all the partners from the University of Edinburgh, IBM Haifa, CAPS and ARC for a great collaborative work during last 3 years and [http://cTuning.org/community cTuning community] for a very interesting feedback and extensions! We released all the tools from the project and hope to continue extending them within community-driven [http://cTuning.org cTuning.org]. This infrastructure should open up many interesting research opportunities for performance auto-tuning based on statistical and machine learning techniques so we hope to see many more interesting extensions to the MILEPOST/cTuning technology soon ;) !.. |
*'''2009.September.25''' - New CFP for [http://ctuning.org/workshop-smart10 SMART'10 workshop] co-located with HiPEAC'10 conference in Pisa, Italy is now available. [http://www.cs.rice.edu/~keith Prof. Keith Cooper] from Rice University kindly agreed to give a keynote talk. | *'''2009.September.25''' - New CFP for [http://ctuning.org/workshop-smart10 SMART'10 workshop] co-located with HiPEAC'10 conference in Pisa, Italy is now available. [http://www.cs.rice.edu/~keith Prof. Keith Cooper] from Rice University kindly agreed to give a keynote talk. | ||
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*'''2009.September.1''' - The documentation of [http://ctuning.org/milepost-gcc MILEPOST GCC]/[http://ctuning.org/ici GCC ICI] extensions by Yuanjie and Liang during GSOC'09 program is now available: [http://ctuning.org/wiki/index.php/CTools:ICI:Projects:GSOC09:Function_cloning_and_program_instrumentation Function cloning and program instrumentation] and [http://ctuning.org/wiki/index.php/CTools:ICI:Projects:GSOC09:Fine_grain_tuning Fine grain program tuning]. We would like to fully test and sync these developments with mainline GCC within next month or two. | *'''2009.September.1''' - The documentation of [http://ctuning.org/milepost-gcc MILEPOST GCC]/[http://ctuning.org/ici GCC ICI] extensions by Yuanjie and Liang during GSOC'09 program is now available: [http://ctuning.org/wiki/index.php/CTools:ICI:Projects:GSOC09:Function_cloning_and_program_instrumentation Function cloning and program instrumentation] and [http://ctuning.org/wiki/index.php/CTools:ICI:Projects:GSOC09:Fine_grain_tuning Fine grain program tuning]. We would like to fully test and sync these developments with mainline GCC within next month or two. | ||
- | *'''2009.August.05''' - The colleagues from the [http://unidapt.org UNIDAPT Group] started investigating the use of [http://ctuning.org cTuning]/[http:// | + | *'''2009.August.05''' - The colleagues from the [http://unidapt.org UNIDAPT Group] started investigating the use of [http://ctuning.org cTuning]/[http://ctuning.org/wiki/index.php/Dissemination:Projects:MILEPOST 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! | ||
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** [[CTools:CBench|Collective Benchmark/MiDataSets v1.0]] - enabling realistic program optimization research and benchmarking using multiple open-source programs/datasets.<BR><BR>We also updated [[CDatabase|Collective Optimization Database]] with various optimization cases for Intel and AMD processors and comparison of different compilers including GCC, LLVM, Open64, Intel, etc - enabling sharing and reuse of optimization knowledge.<BR><BR>We would like to thank [[Community:People|cTuning community]] for feedback, help and support! You are welcome to join this [[Community|community effort]] to automate program optimization and compiler/architecture design. | ** [[CTools:CBench|Collective Benchmark/MiDataSets v1.0]] - enabling realistic program optimization research and benchmarking using multiple open-source programs/datasets.<BR><BR>We also updated [[CDatabase|Collective Optimization Database]] with various optimization cases for Intel and AMD processors and comparison of different compilers including GCC, LLVM, Open64, Intel, etc - enabling sharing and reuse of optimization knowledge.<BR><BR>We would like to thank [[Community:People|cTuning community]] for feedback, help and support! You are welcome to join this [[Community|community effort]] to automate program optimization and compiler/architecture design. | ||
- | * '''2009.April.27''' - We gave a talk at the University of Illinois at Urbana-Champaign about [http://ctuning.org Collective Tuning Initiative and tools] and [http:// | + | * '''2009.April.27''' - We gave a talk at the University of Illinois at Urbana-Champaign about [http://ctuning.org Collective Tuning Initiative and tools] and [http://ctuning.org/wiki/index.php/Dissemination:Projects:MILEPOST MILEPOST project] ("Collective Optimization, run-time adaptation and machine learning"). Presentation is available [http://unidapt.org/presentations/presentation_fursin_uiuc2009.pdf here]. We would like to thank all the UIUC colleagues for a very interesting and useful feedback.<br> |
* '''2009.April.23''' - Preview version of the optimization predictor based on static program features and machine learning (to improve program execution time, code size, etc) is now available [http://ctuning.org/wiki/index.php/Special:CPredict on-line]. It is an on-going project, so please be patient. Comments are welcome! | * '''2009.April.23''' - Preview version of the optimization predictor based on static program features and machine learning (to improve program execution time, code size, etc) is now available [http://ctuning.org/wiki/index.php/Special:CPredict on-line]. It is an on-going project, so please be patient. Comments are welcome! |
Revision as of 19:17, 12 May 2010
It may not always be visible to the IT users, but developing and optimizing current and emerging computing systems using available technology is excessively inefficient, time consuming and costly. During last several decades, many research papers have been published with suggestions about how to easily solve all these problems! Unfortunately, we often struggled replicating those results in realistic environments or finding stable practical tools based on these research papers. Therefore, we decided to create this collaborative Collective Tuning wiki-based portal to develop common practical open-source extensible collaborative infrastructure, benchmarks and collective optimization repository based on multiple research techniques and production tools to parametrize, automate and simplify program, compiler and architecture design and optimization using collective tuning, run-time adaptation, statistical and machine learning techniques. This technology minimizes repetitive time consuming tasks and human intervention: even though there is still a lot to be done, we are glad to see it helping several companies, end users and researchers to improve execution time, code size, power consumption, reliability and other important characteristics of the available computing systems ranging from supercomputers to mobile systems automatically.
We developed Collective Optimization Database to continuously collect a large number of optimization cases from the community to learn how to correlate program features, program and system behavior and good optimizations between multiple programs, datasets, compilers, operating systems and architectures. This repository is also intended to improve the quality of academic research by avoiding costly duplicate experiments and providing reproducible results. cTuning open-source infrastructure is still far from solving all optimization problems but we hope that it already opens up some interesting collaborative R&D opportunities to the community to develop intelligent self-tuning adaptive computing systems. We hope that cTuning-like technology will one day eventually improve production compilers that we use including GCC, LLVM, Rose source-to-source tool, Open64, IBM XL and Testarossa, HMPP directive based compiler for hybrid multicore systems, Intel compiler suites, and operating systems including Moblin, Android, standard desktop/server Linux distributions, Windows, cloud/distributed operating systems and so on. We would like to thank all cTuning colleagues and users who are or have been helping with this project. Note: cTuning is an ongoing evolving project - please be patient and tolerant to the community. You are warmly welcome to join cTuning community to help us parametrize and automate code, compiler and architecture design and optimization! Current design of our Collective Optimization Framework: ![]() We are participating in the following collaborative activities:
Motivation example: Example of complex optimization search spaces for susan_c from Collective Benchmark after using CCC framework (that has some similarities with the useful ACOVEA tool but also allows automatic sharing of optimization knowledge with the community in the Collective Optimization Database and uses plugins to implement various search techniques besides genetic algorithms) and MILEPOST GCC 4.4.0: ![]() ![]() Example of program similarities based on static program features and based on best found program optimizations continuously collected in the cTuning optimization repository that improve execution time (though it can be any combination of execution time, code size, compilation time, etc): ![]() ![]() Milepost GCC combined with cTuning technology helps to correlate program features and optimizations using various machine learning techniques to quickly predict good optimizations for a previously unseen program. |
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