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cTuning.org - free, open source, collaborative repository and tools for program and architecture characterization and optimization.
- ANNOUNCEMENT: After the release of the cTuning CC (MILEPOST GCC) and a relatively quiet summer and autumn 2010 spent mostly on thinking ;), we plan to resume our open collaborative activities in 2011 and will work on a new collaborative platform and a repository cTuning2. Currently, we are looking for additional funding to keep it running and to access various new hardware (embedded or HPC systems, GPUs, etc) to perform empirical collective learning and optimization to suggest better program optimizations or hardware designs based on multi-objective criteria (execution time/power/architecture size/code size/compilation time/etc) (ref1, ref2, ref3), so if you are interested to support cTuning initiative in 2011, please don't hesitate to contact Dr. Grigori Fursin (cTuning founder and R&D coordinator). More information will be available at the beginning of 2011.
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 free common open-source extensible collaborative infrastructure, benchmarks and collective optimization repository based on multiple research techniques and production tools to parametrize, abstract, automate, simplify and systematize program, compiler and architecture design and optimization using collective tuning, run-time adaptation, statistical and machine learning techniques. It enables sharing of benchmarks, datasets, optimization cases through unified web-services and tools with common APIs to be able to predict better optimizations or architecture designs provided there is enough information collected in the repository from multiple users. 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.
cTuning web-site and infrastructure is totally free and is now maintained and extended collaboratively and voluntarily by its users (basically by you). It is an on-going project, so please be patient or join the effort by sharing optimization data, extending tools, exchanging ideas, referencing this work, etc. We created cTuning.org because we believe in collaborative and public nature of R&D and because we would like to change trends in academic IT research intended to publish myriads of similar papers often non-reproducible results and without releasing tools to avoid competition.
Current design of our Collective Optimization Framework:
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!
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.
- 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.
- 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.
Motivation example:
Example of complex optimization search spaces for susan_c (including optimization space frontier for multi-objective optimizations) 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 cTuning CC/MILEPOST GCC 4.4.x:
Example of program similarities using static program features and based on best found program optimizations continuously collected in the cTuning optimization repository that improve execution time (as well as code size, compilation time, etc):
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cTuning CC/MILEPOST GCC 4.4.x 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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- 2010.October.31 - Paper about practical aggregation of semantical program properties for machine learning based optimization by M.Namolaru et al from CASES'10 is now available on-line here. It describes mechanisms of feature extraction inside MILEPOST GCC/cTuning CC.
- 2010.October.26 - SMART'11 will be co-located with the CGO'11 conference. More information will be following soon!
- 2010.August.16 - Submissions are now open for CGO 2011.
- 2010.June.30 - Call for papers: CGO'11.
- 2010.June.9 - Finally, we recovered all cTuning website and services after physical hard drive failure. However, if you still experience some problems or abnormal behavior, please report that to the cTuning discussions mailing list! Thanks and sorry for any inconvenience!
- 2010.May.22 - Pre-release of cTuning CC V2.5 is now available. cTuning CC is a free, open source compiler collection that combines multiple tools and techniques including MILEPOST GCC, ICI, CCC framework, cTuning web-services and Collective Optimization Database and cBench as the first practical step toward self-tuning, adaptive computing systems based on industrial tools, empirical techniques, transparent collective optimization, statistical analysis and machine learning. cTuning CC is a wrapper around any compiler such as GCC, LLVM, Open64, Path64, etc that can transparently invoke machine learning mode to correlate program features of a compiled program with the ones stored in the Collective Optimization Database and suggest better optimizations for multi-objective criteria such as improving execution time, compilation time, code size, etc (using optimization space frontier detection).
- 2010.April.28 - List of all projects accepted for Google Summer of Code 2010 is now available on-line. There are many projects related to GCC, LLVM, MONO, etc.
Discussion page about GCC as a research compiler has been updated.
Diego Novillo started a proposal to modularize GCC.
- 2010.April.14 - Long awaited GCC 4.5 has been released! It features new plugin framework with some parts of ICI to continue GCC modularization and parametrization, simplify pass manipulation and reordering, and enable better integration with cTuning/MILEPOST tools to automate optimization space exploration and prediction of profitable combinations of program transformations during multi-objective optimizations (balancing execution time, code size, compilation time, etc) for a given program/dataset/architecture.
- 2010.March.17 - We pre-released all tools including MILEPOST GCC, CCC framework, cBench and cDatabase. It's a major update of cTuning tools including changes behind to support transparent optimizations of programs and libraries, better multi-objective optimization (including balancing of execution time, code size and compilation time), bug fixes in averaging multiple optimization cases, C++ support in MILEPOST GCC, support of all version of GCC 4.4, new static features in MILEPOST GCC, extended documentation, etc. Feedback and comments are welcome here.
- 2010.March.01 - CGO'10 program is available on-line.
- 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.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.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.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.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
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