All these problems forced motivated me to develop an open-source framework and repository (cTuning.org) to share, validate and reuse workloads, data sets, tools, experimental results and predictive models, while involving the community in this effort . This experience, in turn, helped us initiate so-called Artifact Evaluation (AE) at the premier ACM conferences on parallel programming, architecture and code generation (CGO, PPoPP, PACT and SC). AE aims to independently validate experimental results reported in the publications, and to encourage code and data sharing.
On the one hand, we have received incredible support from the research community, ACM, universities and companies. We have even received a record number of artifact submissions at the CGO/PPoPP'17 AE (27 vs 17 two years ago) sponsored by NVIDIA, dividiti and cTuning foundation. We have also introduced Artifact Appendices and co-authored the new ACM Result and Artifact Review and Badging policy now used at Supercomputing.
On the other hand, the use of proprietary benchmarks, rare hardware platforms, and totally ad-hoc scripts to set up, run and process experiments all place a huge burden on evaluators. It is simply too difficult and time-consuming to customize and rebuild experimental setups, reuse artifacts and eventually build upon others’ efforts - the main pillars of open science!
I will then present Collective Knowledge (CK), our humble attempt to introduce a customizable workflow framework with a unified JSON API and a cross-platform package manager, which can automate experimentation and enable interactive articles, while automatically adapting to the ever evolving software and hardware . I will also demonstrate a practical CK workflow for collaboratively optimizing deep learning engines (such as Caffe and TensorFlow) and models across different compilers, libraries, data sets and diverse platforms from constrained mobile devices to data centers (CK-Caffe on GitHub / Android app to crowdsource DNN optimization) .
Finally, I will describe our open research initiative to publicly evaluate artifacts and papers which we have successfully validated at CGO-PPoPP’17, and plan to keep building upon in the future .
I am looking forward to your participation and feedback! Please feel free to contact me at Grigori.Fursin@cTuning.org or email@example.com if you have any questions or comments!
 “Optimizing Convolutional Neural Networks on Embedded Platforms with OpenCL”, IWOCL'16, Vienna, Austria, 2016