Dear colleagues, Hope you had a nice and relaxing summer! There have been many news related to our [http://github.com/ctuning/ck Collective Knowledge framework] this summer, so I would like to share some of them with you. === News === We would like to thank [http://www.microsoft.com Microsoft] for providing a 1-year grant to host cknowledge.org repository at [http://cknowledge.org/repo Microsoft Azure cloud]! We will present a CK-based project with ARM at ARM TechCon'16 in Santa Clara in October (see [https://github.com/ctuning/ck/wiki/Demo-ARM-TechCon'16 schedule], [http://bit.ly/ck-date16 DATE'16 paper] and [http://arxiv.org/abs/1506.06256 CPC'15 paper]). We will demonstrate Workload Knowledge, an open framework for gathering and sharing knowledge about system design and optimization using real-world workloads. Powered by 3 open-source projects (ARM's Workload Automation, cTuning's Collective Knowledge and Jupyter Notebooks), Workload Knowledge will dramatically accelerate innovation in computer engineering and lead to designing highly efficient systems. We will announce various Collective Knowledge awards for the top contributors sharing workloads, data sets, tools, autotuning plugins, predictive models and optimization knowledge at ARM TechCon. Active student contributors will have a priority for internships at dividiti! Hurry up to try [http://github.com/ctuning/ck CK] and join our growing community! === Program crowd-tuning === We would like to thank [http://cTuning.org/crowdtuning-timeline all participants] for testing our [https://play.google.com/store/apps/details?id=openscience.crowdsource.experiments CK-powered] GCC/LLVM crowd-tuning approach using spare mobile phones, tablets, laptops and cloud servers. We now have more that [http://cTuning.org/crowdtuning-platforms 300 distinct Android, Windows, Linux and MacOS-based platforms] participated in experiment crowdsourcing (nearly [http://cTuning.org/crowdtuning-cpu 150 distinct CPU], and ~ [http://cTuning.org/crowdtuning-cpu 50 distinct GPUs]). You can find all meta shared at [http://github.com/ctuning/ck-crowdtuning-platforms GitHub]. Furthermore, the community collected and shared [http://cknowledge.org/repo numerous distinct GCC and LLVM optimizations] for more than 70 (CPU,compiler version) tuples across ~140 [http://github.com/ctuning/ctuning-programs shared workloads]. It opens up many interested opportunities for practical research in machine-learning based autotuning, run-time adaptation and SW/HW co-design! === CK improvements === Above collaborative optimization helped us stabilize CK framework including Android mobile app. Thanks to the community contributions and fixes, CK autotuning now supports MacOS. We also added support for continuous integration frameworks for Linux and Windows, for Jupyter notebooks, and for Docker! We have released new Collective Knowledge Framework V1.8.1 (available via pip): * [http://github.com/ctuning/ck] We have also released Android application V2.2 with sources to help you participate in various experiment crowdsourcing using Android-based mobile devices and IoT devices: * [https://play.google.com/store/apps/details?id=openscience.crowdsource.experiments]
* [https://github.com/ctuning/crowdsource-experiments-using-android-devices] Finally, we provided a new documentation with various Getting Started Guides: * [http://github.com/ctuning/ck/wiki]
* [http://github.com/ctuning/ck/wiki/Getting-started-guide]
* [http://github.com/ctuning/ck/wiki/Portable-workflows] === Open Science and Reproducible Research === * Congratulations to Dr. Abdul Memon (my last PhD student) for successfully defending his thesis [http://arxiv.org/abs/1506.06256 "Crowdtuning: Towards Practical and Reproducible Auto-tuning via Crowdsourcing and Predictive Analytics"] in the [https://en.wikipedia.org/wiki/University_of_Paris-Saclay University of Paris-Saclay]. Most of the software, data sets and experiments are not only reproducible but also shared as reusable and extensible components via [http://c-mind.org Collective Mind] and [http://cknowledge.org CK]! * We have moved our "Open Science" wiki with related resource to [http://github.com/ctuning/ck/wiki/Enabling-open-science CK GitHub]. * We have helped with Artifact Evaluation for [http://cTuning.org/ae/pact2016.html PACT'16]! * We continue discussions with [http://acm.org ACM colleagues] about how to enable collaborative and reproducible R&D across all SIGs. * Please, check [https://cambridgewirelessblog.wordpress.com/2016/05/23/10-mins-with-dividiti this interview] with Dr. Anton Lokhmotov (CEO of [http://dividiti.com dividiti]) above how CK can help enable efficient, reliable and cheap computing everywhere! === Plans === After nearly [http://arxiv.org/abs/1506.06256 20 years], we are finally moving back to AI research and have several projects related to unifying benchmarking, tuning and access to DNN networks via CK: * [http://bit.ly/ck-cnn]
* [http://github.com/dividiti/ck-caffe]
* [http://github.com/ctuning/ck-tensorflow] We also work on a unification of benchmarking and multi-objective autotuning across remote devices; adding more workloads in the CK format; adding more machine-learning based autotuning strategies; crowdsourcing compiler bug detection; crowdtuning OpenCL and CUDA BLAS libraries; improving documentation further; unifying predictive analytics including access to DNN frameworks via CK web service. See our assorted plans [http://github.com/ctuning/ck/wiki/Plans here]. You can also check open tickets at GitHub pages of [https://github.com/ctuning] and [https://github.com/dividiti]. === The community === Since there is a growing number of CK-powered collaborative and reproducible projects as well as CK users and participants in experiment crowdsourcing, we strongly suggest you to participate in public discussions via this public [https://groups.google.com/forum/#!forum/collective-knowledge mailing list] or [https://www.linkedin.com/groups?home=&gid=7433414 LinkedIn group]. This will help the community share knowledge and experience about CK while avoiding common pitfalls! Have a very productive Fall and looking forward discussing CK projects with you,
Grigori and the Collective Knowledge team