Collaborative design and optimization using Collective Knowledge

Anton Lokhmotov, Grigori Fursin
dividiti, UK
Ninth International Workshop on Programmability and Architectures for Heterogeneous Multicores (MULTIPROG), 2016


   title={Collaborative design and optimization using Collective Knowledge},

   author={Lokhmotov, Anton and Fursin, Grigori},



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Designing faster, more energy efficient and reliable computer systems requires effective collaboration between hardware designers, system programmers and performance analysts, as well as feedback from system users. We present Collective Knowledge (CK), an open framework for reproducible and collaborative design and optimization. CK enables systematic and reproducible experimentation, combined with leading edge predictive analytics to gain valuable insights into system performance. The modular architecture of CK helps engineers create and share entire experimental workflows involving modules such as tools, programs, data sets, experimental results, predictive models and so on. We encourage a wide community, including system engineers and users, to share and reuse CK modules to fuel R&D on increasing the efficiency and decreasing the costs of computing everywhere.
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