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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 an open 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 end users and researchers to improve execution time, code size, 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. Collective Optimization Database is a public repository to continuously collect a large number of optimization cases from the community to correlate program features, program and system behavior and good optimizations between multiple programs, datasets, compilers, operating systems and architectures. This repository can also improve the quality of academic research by avoiding costly duplicate experiments and providing reproducible results.

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 help automate code, compiler and architecture design and optimization, boost innovation and research.

Note: cTuning is an ongoing evolving project - please be patient and tolerant to the community and help us with this collaborative effort!

Current design of our Collective Optimization Framework:

We are participating in the following collaborative activities:

Develop common open-source tools with unified APIs (universal compilers adaptable to any heterogeneous multi-core architecture, computer architecture simulators, adaptive run-time systems) to optimize programs and architectures collectively using iterative compilation, statistical and machine learning techniques.
More information is available at cTools page.
Share interesting optimization cases from the community for programs/libraries/OS (compiler optimizations/architecture configurations to improve execution time, code size, architecture size, power consumption, etc) in the Collective Optimization Database to help users optimize their systems, enable replicable collaborative research and enable further analysis using statistical and machine learning techniques.
More information is available at cDatabase page.
Enable collaborative research using cTools to automate and simplify the process of developing and optimizing new computer architectures, compilers, operating systems and programming environments using statistical analysis, machine learning, dynamic adaptation and bio-inspired techniques. We believe that our adaptive approaches are critical to overcome the complexity of computing systems and improve their performance, power consumption, system size and fault-tolerance automatically while reducing their cost and time to market.
More information is available at cResearch page.

Motivation example:

Example of complex optimization search spaces for susan_c from Collective Benchmark after using CCC framework 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.

News

2010.February.10 - Accepted papers for PLDI'10 are now available on-line.

2010.January.28 - Proceedings and slides from GROW'10 and SMART'10 are now available online.

2010.January.25 - We added Speedup Test beta plugin to cTuning collective optimization database to enable precise and rigorous statistical analysis of the performance of benchmarks.

2010.January.4 - Call for participation: GROW'10 and SMART'10 workshops will be held on the 23rd and 24th of January in Pisa, Italy co-located with the HiPEAC conference. Preliminary programs are available: SMART'10 program and GROW'10 program.

2010.January.1 - We wish you all a very happy and prosperous New Year with lots of exciting achievements maybe even related to cTuning technology ;) !

2009.November.11 - Small CCC analysis plugins update available at SVN to speedup queries when dealing with large amount of optimization data in cDatabases (gigabytes of data).

2009.November.7 - Submission deadline for SMART'10 workshop has been extended until the 22nd of November, 2009.

2009.October.2 - We successfully passed the final 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 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 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 SMART'10 workshop co-located with HiPEAC'10 conference in Pisa, Italy is now available. 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 MILEPOST GCC/GCC ICI extensions by Yuanjie and Liang during GSOC'09 program is now available: Function cloning and program instrumentation and 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 UNIDAPT Group started investigating the use of cTuning/MILEPOST technology and the UNIDAPT framework to predict good optimization and parallelization schemes for hybrid heterogeneous CPU/GPU-like architectures together with 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 (FT2009, LCWP2009, Fur2009, JGVP2009, TWFP2009, FMTP2008, LFF2007, FCOP2005). They plan to add new optimization cases to the Collective Optimization Database in Autumn, 2009.

2009.July.27 - The paper "Portable Compiler Optimization Across Embedded Programs and Microarchitectures using Machine Learning" (DJBP2009) has been accepted for the 42nd IEEE/ACM International Symposium on Microarchitecture (MICRO). The research has been led by the colleagues from the University of Edinburgh - congratulations!

2009.July.22 - cTuning-related CFP: SMART'10 and GROW'10 workshops will be co-located with HiPEAC'10 conference in Pisa, Italy at the end of January, 2009. You are warmly invited to submit your novel research results and developments to our workshops! At the websites of these workshops you can find all information about topics, PC, deadlines and submission procedures.

2009.July.15 - New optimization cases to improve execution time, code size and compilation time are now available in Collective Optimization Database for cBench, EEMBC, SPEC and other applications with multiple datasets using MILEPOST GCC 4.4.0 and 2 architectures (Intel and AMD) from GRID5000 (infrastructure for large scale parallel and distributed computing research).

2009.July.08 - The preprint of a paper "Collective Tuning Initiative: automating and accelerating development and optimization of computing systems" describing cTuning infrastructure is now available online (Fur2009).

2009.June.29 - Stable MILEPOST GCC 4.4.0 has been released. Follow further community developments at cTuning GCC ICI page and cTuning development mailing list.
Next, we plan to use MILEPOST/cTuning technology to enable realistic adaptive parallelization, data partitioning and scheduling for heterogeneous multi-core systems using statistical and machine learning techniques.

2009.June.26 - The pdf of the paper that describes Collective Tuning Infrastructure and cTuning concept (presented at the GCC Summit'09) will be available in a few weeks here.

2009.June.17 - We participated in discussions to include plugin system similar to ICI to mainline GCC for a long time and finally GCC 4.5 will feature a low-level plugin system. We are now synchronizing high-level ICI/MILEPOST with the mainline to be able to reuse all our available plugins. We also develop several new plugins within Google Summer of Code'2009 to enable XML representation of the compilation flow, fine-grain program optimizations and instrumentation, automatic tuning of optimization heuristic based on machine learning, and function-level run-time adaptation. Comparison of GCC low-level and high-level ICI plugins is available here. The ICI development and discussions mailing list is available here.

2009.June.10 - Extended version of the "Collective Optimization" paper (FT2009) describing collective tuning concept has been accepted for ACM Transactions on Architecture and Code Optimization (TACO).

2009.June.03-10 - We gave several talks/demos/tutorials about cTuning at the HiPEAC Computing week (Infineon, Munich, Germany) and GCC Summit (Montreal, Canada).

2009.June.01 - After nearly 1 year of developments we released/updated all our open-source collaborative R&D tools:
- fully redesigned and documented Interactive Compilation Interface v2.0 for GCC 4.4.0 synchronized with the official plugin GCC branch - transforming compilers into plugin-enabled research toolsets
- MILEPOST GCC 4.4.0 pre-release version at SVN - automating program optimization and compiler optimization heuristic tuning using machine learning
- Continuous Collective Compilation Framework v2.0 - enabling automatic collaborative program optimization based on statistical and machine learning techniques
- Collective Benchmark/MiDataSets v1.0 - enabling realistic program optimization research and benchmarking using multiple open-source programs/datasets.
  
  We also updated 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.
  
  We would like to thank cTuning community for feedback, help and support! You are welcome to join this 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 Collective Tuning Initiative and tools and MILEPOST project ("Collective Optimization, run-time adaptation and machine learning"). Presentation is available here. We would like to thank all the UIUC colleagues for a very interesting and useful feedback.

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 on-line. It is an on-going project, so please be patient. Comments are welcome!

News archive

cTuning concept:		cTuning friends:

@@ Line 1: / Line 1: @@
-''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 an open 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 end users and researchers to improve execution time, code size, 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. [http://cTuning.org/cdatabase Collective Optimization Database] is intended to improve the quality of academic research by avoiding costly duplicate experiments and providing reproducible results.''<BR>
+''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 an open 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 end users and researchers to improve execution time, code size, 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. [http://cTuning.org/cdatabase Collective Optimization Database] is a public repository to continuously collect a large number of optimization cases from the community to correlate program features, program and system behavior and good optimizations between multiple programs, datasets, compilers, operating systems and architectures. This repository can also improve the quality of academic research by avoiding costly duplicate experiments and providing reproducible results.''<BR>
 {|border="0" cellpadding="15" cellspacing="0"
@@ Line 9: / Line 9: @@
 '''''Note:''' cTuning is an ongoing evolving project - please be patient and tolerant to the community and help us with this collaborative effort!''
+'''Current design of our Collective Optimization Framework''':
 <div align="center">http://ctuning.org/wiki/images/ctuning.gif</div>
-----
+'''We are participating in the following collaborative activities:'''
-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 4.4 during automatic program optimization using ''ccc-run-glob-flags-rnd-uniform'' plugin from [[CTools:CCC|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 program optimization/characterization data for other benchmarks, datasets and architectures is collected continuously in the public [[CDatabase|Collective Optimization Database]].<BR>
-<div align="center">http://cTuning.org/wiki/images/fig_opt_case_susan_c_1.gif http://cTuning.org/wiki/images/fig_opt_case_susan_c_2.gif</div>
-Example of program similarities based on [http://ctuning.org/wiki/index.php/CTools:MilepostGCC:StaticFeatures static program features] and based on best found program optimizations from the [http://cTuning.org/cdatabase cTuning optimization repository] that improve execution time:
-<div align="center">http://cTuning.org/wiki/images/img_influence_features.gif http://cTuning.org/wiki/images/img_influence_optimizations.gif</div>.
-[http://cTuning.org/milepost-gcc 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.
-----
-'''We are participating in the following collaborative activities:''' <br>
 * Develop common open-source tools with unified APIs (universal compilers adaptable to any heterogeneous multi-core architecture, computer architecture simulators, adaptive run-time systems) to optimize programs and architectures collectively using iterative compilation, statistical and machine learning techniques. <div align="right">''More information is available at [[CTools|cTools page]].''</div>
 * Share interesting optimization cases from the community for programs/libraries/OS (compiler optimizations/architecture configurations to improve execution time, code size, architecture size, power consumption, etc) in the Collective Optimization Database to help users optimize their systems, enable replicable collaborative research and enable further analysis using statistical and machine learning techniques.<br><div align="right">''More information is available at [[CDatabase|cDatabase page]].''</div>
 * Enable collaborative research using cTools to automate and simplify the process of developing and optimizing new computer architectures, compilers, operating systems and programming environments using statistical analysis, machine learning, dynamic adaptation and bio-inspired techniques. We believe that our adaptive approaches are critical to overcome the complexity of computing systems and improve their performance, power consumption, system size and fault-tolerance automatically while reducing their cost and time to market. <br><div align="right">''More information is available at [[CResearch|cResearch page]].''</div>
+'''Motivation example:'''
+Example of complex optimization search spaces for susan_c from [[CTools:CBench|Collective Benchmark]] after using [[CTools:CCC|CCC framework]] and [[CTools:MilepostGCC|MILEPOST GCC 4.4.0]]:
+<div align="center">http://unidapt.org/images/fig_opt_case_susan_c_1.gif http://ctuning.org/wiki/images/fig_opt_case_susan_c_2.gif</div>
+Example of program similarities based on [[CTools:MilepostGCC:StaticFeatures|static program features]] and based on best found program optimizations continuously collected in the [[CDatabase|cTuning optimization repository]] that improve execution time (though it can be any combination of execution time, code size, compilation time, etc):
+<div align="center">http://unidapt.org/images/img_influence_features.gif http://unidapt.org/images/img_influence_optimizations.gif</div>.
+[[CTools:MilepostGCC|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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