From cTuning.org
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+ | *'''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.22''' - Congratulations to Dr.Christophe Dubach who has won a presitigious BCS/CPHC Distinguished Dissertation Award for his thesis "Using Machine-Learning to Efficiently Explore the Architecture/Compiler Co-Design Space" supervised by Prof. Michael O'Boyle. This topic is related to cTuning technology! | ||
*'''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://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.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. |
Revision as of 14:52, 27 September 2009
It may not always be visible to the IT users, but developing and optimizing current and emerging computing systems using available technology is too time consuming and costly. cTuning.org is a community-driven collaborative wiki-based portal that brings together academia, industry and end-users to develop intelligent collective tuning technology that automates and simplifies compiler, program and architecture design and optimization. This technology minimizes repetitive time consuming tasks and human intervention using collective optimization, run-time adaptation, statistical and machine learning techniques. It can already help to improve performance, power consumption, reliability and other important characteristics of the available computing systems automatically (ranging from supercomputers to embedded systems) and should eventually enable development of the emerging intelligent self-tuning adaptive computing systems.
cTuning concept, collaborative R&D tools, cTuning website and optimization prediction services have been originally developed and coordinated by Dr. Grigori Fursin (UNIDAPT Group, INRIA, France) and extended during the MILEPOST project. Since 2009, cTuning.org is a open community-driven project to develop self-tuning computing systems. If you have any questions about cTuning.org, don't hesitate to contact us or use cTuning mailing lists. If you need to reference cTuning project, you can reference cTuning website or the following papers FT2009,Fur2009,FMTP2008. We are very grateful to all our colleagues and users for providing valuable feedback or contributing to the project. You are warmly welcome to join this community-driven collaborative effort to automate code, compiler and architecture design and optimization and boost innovation and research to enable future self-tuning adaptive computing systems based on heterogeneous, reconfigurable multi-core architectures. Note: cTuning is an ongoing evolving project - please be patient and tolerant to the community and help us with this collaborative effort! ![]() Typical non-trivial distribution of optimization points in the 2D space of speedups vs code size of a susan_corners program on AMD Athlon64 3700+ architecture with GCC 4.2.2 during automatic program optimization using ccc-run-glob-flags-rnd-uniform plugin from CCC framework with uniform random combinations of more than 100 global compiler flags (each flag has 50% probability to be selected for a given combination of optimizations). Similar data for other benchmark, datasets and architectures is available in the Collective Optimization Database. cTuning technology helps users find or predict optimial optimization points in such complex spaces using "one-button" approach. ![]() We are participating in the following collaborative activities:
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