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Revision as of 12:53, 24 September 2015
Note: Since September 2015, we have moved all developments and documentation to GitHub: src, docs
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<p style="text-align: center">Collective Mind We are a group of researchers working with the community on a new methodology, infrastructure and repository to enable collaborative and reproducible research and experimentation in computer engineering as a side effect of our projects on combining performance/energy/size auto-tuning with run-time adaptation, crowdsourcing, big data and predictive analytics (see our manifesto and history). Our approach, in turn, helped to enable new publication model where all research material (code and data artifacts) is shared along with articles to be continuously discussed, validated and improved by the community! To evangelize this community-driven approach and set up an example, we started releasing all our benchmarks, data sets, predictive models and tools with unified interfaces since 2007 at cTuning.org and later at c-mind.org/repo. We use ADAPT workshop on self-tuning computing systems to validate our new research and publication model. We hope that it can complement well recent academic initiatives on reproducible research at major conferences while focusing more on technological aspects of collaborative and reproducible research in computer engineering (rather than just sharing and validating artifacts).
<p style="text-align: center">This R&D is supported by the cTuning foundation.</p>
We successfully validated of our proof-of-concept Collective Mind framework in 2013-2014 and collected considerable user feedback. We now plan to get more funding to develop a new and more user-friendly cM version, update this website, improve documenation, and provide more demos by the end of 2014. Please, get in touch if you would like to sponor our R&D or join our mailing list to stay tuned.
With the rapid advances in information technology and all other fields of science comes dramatic growth in the amount of processing data ("big data"). Scientists, engineers and students are drowning in experimental data and often have to divert their research path towards data management, mining, and visualization. Such approaches often require additional interdisciplinary skills including statistical analysis, machine learning, programming and parallelization, database management, and Internet technologies, which still few researchers have or can afford to learn in parallel with their main research work. Multiple frameworks, languages and public data repositories started appearing recently to enable collaborative data analysis and processing but they are often either covering very narrow research topics and too simplistic (just data and code sharing) or very formal and still require special programming skills often including Object Oriented Programming.
Collective Mind technology (cM) attempts to fill in this gap by providing researchers and companies a simple, portable, technology-neutral and practically transparent way to gradually systematize and classify all their data, code and tools. Open source cM framework and repository fully relies on customizable public or private plugins (mostly written in python with support of any other language through OpenME interface) to gradually describe and classify similar data and code objects, or abstract interfaces of ever changing tools thus effectively protecting researchers' experimental setups. cM helps to easily preserve any complex research artifact (collection of files, benchmarks, codelets, datasets, tools, traces, models) with gradually and easily extensible JSON based meta description including classification, properties and either direct or semantic data connections. Furthermore, meta descriptions of all data can be transparently and easily indexed using third-party ElasticSearch enabling very fast and complex queries. At the same time, all research artifacts can be exposed to any public or workgroup user through unified web services to crowdsource experimentation, ranking, online learning and knowledge management.
<p style="text-align: center"> Here is our list of links to initiatives, publications, tools and techniques related to collaborative and reproducible reserarch, experimentation and development in computer engineering.
Do not waste your research material - use Collective Mind Framework and Repository to describe, run and share your experiments with the community!
Designing novel many-core computer systems becomes intolerably complex, ad-hoc, costly and error prone due to limitations of available technology, enormous number of available design and optimization choices, and complex interactions between all software and hardware components. Empirical auto-tuning combined with run-time adaptation and machine learning has been demonstrating good potential to address above challenges for more than a decade but still far from the widespread production use due to unbearably long exploration and training times, ever changing tools and their interfaces, lack of a common experimental methodology, and lack of unified mechanisms for knowledge building and exchange apart from publications where reproducibility of results is often not even considered. Since 1993, we have spent more time on preparing and analyzing huge amount of heterogeneous experiments for self-tuning machine-learning based computer systems or trying to validate and reproduce others research results rather than on exending our novel ideas.
In 2007, we decided to start collaborative systematization and unification of design and optimization of computer systems combined with a new publication model where experimental results are validated by the community. One of the possible promising solutions is to combine public repository of knowledge with online auto-tuning, machine learning and crowdsourcing techniques where HiPEAC and cTuning communities already have a good practical experience. Such collaborative approach should allow community to continuously validate, systematize and improve collective knowledge about computer systems, and extrapolate it to build faster, more power efficient and reliable computer systems. It can also help to restore the attractiveness of computer engineering making it a more systematic and rigorous discipline rather than "hacking".
We develop cTuning collaborative research and development infrastructure and repository (current version is cTuning3 aka Collective Mind) that enables:
Current cM version includes public benchmarks, datasets, tools, techniques and some stats from past Grigori Fursin's research:
Collective Mind is a community-based and continuously evolving project that uses agile development methodology. Hence, interfaces and modules may be changing from time to time to provide needed functionality. We are very thankful for your understanding, patience and any help to extend and improve this framework while making it clean, simple and easy to use.
towards collaborative, systematic and reproducible computer engineering</p>
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Contents
News
Our long term vision
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cM uses agile top-down methodology originating from physics to represent any experimental scenario and gradually decompose it into connected plugins with associated data or compose it from already shared plugins similar to "research LEGO". Universal structure immediately enables replay mode for any experiment, thus making this framework suitable for recent projects on reproducibility of experimental results and new publication model where experiments and techniques are validated, ranked and improved by the community. For example, we easily moved all our past R&D on program and architecture multi-objective auto-tuning, co-design and dynamic adaptation to cM plugins and gradually make them available together with all research artifacts at http://c-mind.org/repo. We hope that cM will be useful to a broad range of researchers and companies either as an open-source, community driven solution to systematize their research and experimentation, or possibly as an intermediate step before investing into more complex or commercial knowledge management systems.
Community
Related Collective Mind publications and presentations
Public repository of knowledge
Common infrastructure and support tools
Events
Upcoming
Past
Current customized usage scenarios
Related projects and tools
