Arm is one of the first and main users of the Collective Knowledge Technology to automate the design of the more efficient computer systems for emerging workloads such as deep learning across the whole SW/HW stack from IoT to HPC. See the HiPEAC info (page 17) and the Arm TechCon'16 demo for more details about Arm and the cTuning foundation using CK to accelerate computer engineering.
We collaborate with the Association for Computing Machinery (ACM) to use the Collective Knowledge framework to unify sharing of code, data, models and workflows as portable, reusable and customizable components along with reproduced papers as a part of our Reproducibility initiative.
We collaborate with colleagues from TomTom on a model-driven approach for a new generation of adaptive libraries, while automating and crowdsourcing experiments and ML-based modeling using the Collective Knowledge framework.
We collaborate with Raspberry Pi foundation for a new educational initiative to teach students and researchers how to automatically optimize and test (autotuning, crowd-tuning and crowd-fuzzing) realistic workloads in terms of speed, size, energy usage, accuracy and costs across diverse software and hardware stack using CK workflow framework and open optimization repository.
We collaborate with the colleagues from the University of Edinburgh and Glasgow use CK to automate and crowdsource optimization of mathematical libraries and compilers.
We collaborate with the colleagues from ENS Paris to automate and crowdsource polyhedral optimization using CK.
We collaborate with the colleagues from Hartree SuperComputing Center to use CK for customizable and sustainable experimental workflows and collaboratively optimize realistic workloads across various HPC systems.
University of Cambridge colleagues use Collective Knowledge framework to develop sustainable software, accelerate research, automate experimentation and reuse artifacts. For example, portable and reproducible experimental workflow from the "Software Prefetching for Indirect Memory Accesses" article by Sam Ainsworth and Timothy M. Jones received a distinguished artifact award at CGO'17.