Abstract: cTuning.org: Novel Extensible Methodology, Framework and Public Repository to Collaboratively Address Exascale Challenges

Innovation in science and technology is vital for our society and requires faster, more power efficient and reliable computer systems. However, designing and optimizing such systems has become intolerably complex, ad-hoc, costly and error prone due to ever increasing number of available design and optimization choices combined with complex interactions between all software and hardware components, multiple strict requirements placed on characteristics of new computer systems, and a large number of ever-changing and often incompatible analysis and optimization tools. 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.