HIPERFIT Talk: Collective Knowledge Project: Towards Collaborative and Reproducible Computer Engineering – Københavns Universitet

HIPERFIT Talk: Collective Knowledge Project: Towards Collaborative and Reproducible Computer Engineering

Talk by Grigori Fursin, CTO, cTuning foundation. To attend the talk, please signup by sending an email to Martin Elsman (mael at di.ku.dk).

Abstract:

Designing novel computer systems and optimizing their software is becoming too tedious, ad-hoc, time consuming and error prone due to an enormous number of available design and optimization choices. Empirical auto-tuning combined with run-time adaptation and machine learning has been demonstrating some potential to address the above challenges for several decades but is still far from widespread production. Main reasons include 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.

In this talk, I will present our community-driven approach and the 4th version of our public infrastructure and repository to preserve, systematize and share knowledge and experience about program optimization. Our framework helps to describe and preserve the whole experimental setup with all related artifacts (benchmarks, kernels, data sets, libraries, tools) in a reproducible way to automate and crowdsource optimization space exploration and learning. Any unexpected behavior is recorded and analyzed using shared data mining and predictive modeling plugins or is exposed to the community for collaborative explanation. Collected knowledge can be used to validate past research techniques or can be extrapolated to predict better optimizations, run-time adaptation scenarios and hardware designs. During the past 5 years, this approach has been extensively validated with our industrial partners, and helped initiate a new publication model where experiments and artifacts are validated and improved by the community. A beta version of the new framework is available at http://github.com/ctuning/ck.

Biography:

Grigori Fursin is a founder and CTO of the cTuning foundation. In the past, he was a tenured research scientist at INRIA Saclay and was co-founder of the Intel Exascale Lab in France. Grigori has an interdisciplinary background in computer engineering, physics, electronics, machine learning and mathematics. He obtained a PhD in Computer Science from the University of Edinburgh in 2004.

Grigori pioneered machine-learning based program auto-tuning and hardware co-design combined with crowdsourcing and run-time adaptation. In 2008, he established a public repository of optimizations knowledge (cTuning.org) to initiate collaborative and reproducible R&D in computer engineering. Since then, his techniques and tools have been used and extended in multiple industrial projects together with ARM, IBM, Intel, Synopsys and ST. In 2012, Grigori received an INRIA award and 4-year fellowship for “making an outstanding contribution to research”. He is leading an Artifact Evaluation initiative for CGO and PPoPP while developing an open research SDK to preserve, structure and reuse knowledge about program and architecture optimization: http://github.com/ctuning/ck.