We now lead Artifact Evaluation initative or help improve submission/reviewing procedures and Artifact Appendices based for several leading conferences including CGO,PPoPP,PACT,RTSS and SC. We hope it will enable open, collaborative and reproducible computer engineering.
In 2015, we have released the 4th version of our cTuning infrastructure to enable collaborative and reproducible computer systems' research - a brand new, open-source, customizable Collective Knowledge Repository. It aggregates all our past developments, ideas and techniques, and allows users to share, cross-link and reference any object and knowledge (workloads, data sets, tools, optimization results, predictive models, etc.) as a reusable component with a unified JSON API via GitHub or BitBucket!
You can easily check out, reuse and improve the following workloads/artifacts and modules shared by the community! You can also find and reproduce various results from experiment crowdsourcing in the CK live repository and even participate in crowd-tuning yourself.
Since 2006 we are actively working on collaborative and
reproducible research, experimentation and publication
methodology in computer engineering where experimental
results and all related material is continuously shared,
validated and improved by the community as described
in our manifesto [ ACM
DL, arXiv ].
It originally happened as a side effect of the MHAOTEU
project (1999-2001) on performance tuning of real HPC
applications from supercomputer centers
[J15], and later
during our MILEPOST
project (2006-2009) to enable machine learning based
self-tuning compiler using public
of optimization knowledge, common machine learning-based
pluginized autotuning infrastructure, performance
statistics continuously collected from multiple users (big
data), and predictive analytics (statistical analysis, data
mining and machine learning and feature detection)
to adaptively explore and prune large optimization spaces,
and predict better optimizations and hardware designs.
We hope our initiative will help restore attractiveness
of computer engineering as a traditional, collaborative,
reproducible, fair and scientific discipline rather than
hacking, publication machine or monopolized business.
The main challenge we face is the variability of experimental results in a constantly changing software and hardware stack. Hence, it is not enough to simply pack and share relatively simple code and data in an archive or use some Virtual Machine to replay experiments particularly during performance/energy/resiliency analysis and optimization. Based on our practical knowledge and experience, cTuning foundation helps partners with: