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Grigori Fursin

2013-03-27
09:30:00 - 10:20:00

101 , Mathematics Research Center Building (ori. New Math. Bldg.)

Continuing innovation in science and technology is vital for our society and requires ever increasing computational resources. However, delivering such resources has become intolerably complex, ad-hoc, costly and error prone due to an enormous number of available design and optimization choices combined with the complex interactions between all software and hardware components. Auto-tuning, run-time adaptation and machine learning based approaches have been demonstrating good promise to address above challenges for more than a decade but are still far from the widespread production use due to unbearably long exploration and training times, lack of a common experimental methodology, and lack of public repositories for unified data collection, analysis and mining. In this talk, I will present a long-term holistic and cooperative methodology and infrastructure for systematic characterization and optimization of computer systems through unified and scalable repositories of knowledge and crowdsourcing. In this approach, multiobjective program and architecture tuning to balance performance, power consumption, compilation time, code size and any other important metric is transparently distributed among multiple users while utilizing any available mobile, cluster or cloud computer services. Collected information about program and architecture properties and behavior is continuously processed using statistical and predictive modeling techniques to build, keep and share only useful knowledge at multiple levels of granularity. Gradually increasing and systematized knowledge can be used to predict most profitable program optimizations, run-time adaptation scenarios and architecture configurations depending on user requirements. I will also present a new version of the public, open-source infrastructure and repository (cTuning3 aka Collective Mind) for crowdsourcing auto-tuning using thousands of shared kernels, benchmarks and datasets combined with online learning plugins. Finally, I will discuss encountered challenges and some future collaborative research directions on the way towards Exascale computing.