arXiv subjects

Grigori Fursin

Publications and source records attributed to Grigori Fursin.

21 records · Page 2Linked to original sources

arXiv2014

Collective Tuning Initiative

Computing systems rarely deliver best possible performance due to ever increasing hardware and software complexity and limitations of the current optimization technology. Additional code and architecture optimizations are often required to improve execution time, size, power consumption, reliability and other important characteristics of computing systems. However, it is often a tedious, repetitive, isolated and time consuming process. In order to automate, simplify and systematize program optimization and architecture design, we are developing open-source modular plugin-based Collective Tuning Infrastructure (CTI, http://cTuning.org) that can distribute optimization process and leverage optimization experience of multiple users. CTI provides a novel fully integrated, collaborative, "one button" approach to improve existing underperfoming computing systems ranging from embedded architectures to high-performance servers based on systematic iterative compilation, statistical collective optimization and machine learning. Our experimental results show that it is possible to reduce execution time (and code size) of some programs from SPEC2006 and EEMBC among others by more than a factor of 2 automatically. It can also reduce development and testing time considerably. Together with the first production quality machine learning enabled interactive research compiler (MILEPOST GCC) this infrastructure opens up many research opportunities to study and develop future realistic self-tuning and self-organizing adaptive intelligent computing systems based on systematic statistical performance evaluation and benchmarking. Finally, using common optimization repository is intended to improve the quality and reproducibility of the research on architecture and code optimization.

cs.DC↗

arXiv2014

Community-driven reviewing and validation of publications

In this report, we share our practical experience on crowdsourcing evaluation of research artifacts and reviewing of publications since 2008. We also briefly discuss encountered problems including reproducibility of experimental results and possible solutions.

cs.DL↗

arXiv2013

Collective Mind: cleaning up the research and experimentation mess in computer engineering using crowdsourcing, big data and machine learning

Software and hardware co-design and optimization of HPC systems has become intolerably complex, ad-hoc, time consuming and error prone due to enormous number of available design and optimization choices, complex interactions between all software and hardware components, and multiple strict requirements placed on performance, power consumption, size, reliability and cost. We present our novel long-term holistic and practical solution to this problem based on customizable, plugin-based, schema-free, heterogeneous, open-source Collective Mind repository and infrastructure with unified web interfaces and on-line advise system. This collaborative framework distributes analysis and multi-objective off-line and on-line auto-tuning of computer systems among many participants while utilizing any available smart phone, tablet, laptop, cluster or data center, and continuously observing, classifying and modeling their realistic behavior. Any unexpected behavior is analyzed using shared data mining and predictive modeling plugins or exposed to the community at cTuning.org for collaborative explanation, top-down complexity reduction, incremental problem decomposition and detection of correlating program, architecture or run-time properties (features). Gradually increasing optimization knowledge helps to continuously improve optimization heuristics of any compiler, predict optimizations for new programs or suggest efficient run-time (online) tuning and adaptation strategies depending on end-user requirements. We decided to share all our past research artifacts including hundreds of codelets, numerical applications, data sets, models, universal experimental analysis and auto-tuning pipelines, self-tuning machine learning based meta compiler, and unified statistical analysis and machine learning plugins in a public repository to initiate systematic, reproducible and collaborative research, development and experimentation with a new publication model where experiments and techniques are validated, ranked and improved by the community.

cs.SE↗