Our success stories

We believe that our main contribution to the community is our radically new, holistic, interdisciplinary and scientific research methodology for computer engineering where we effectively combined our open-source Collective Knowledge framework and repository (involving the community to share realistic programs, workloads, experiment workflows and predictive models as reusable components with unified JSON API), crowd-benchmarking, multi-objective autotuning, big data predictive analytics including statistical analysis, machine learning and feature selection, run-time adaptation with multi-versioning, experiment crowdsourcing and collective intelligence [ DATE'16, CPC'15, Scientific Programming'14, TRUST@PLDI'14 ].

Our collaborative and reproducible computer engineering methodology combined with predictive analytics helped enable world's first machine learning based self-tuning compiler (MILEPOST GCC), start crowdsourcing optimization and machine learning, and eventually initiate artifact evaluation at PPoPP, CGO and ADAPT (backed up by ACM) to validate techniques published in computer systems' conferences, workshops and journals.

Our techniques were published in PLDI, MICRO, CGO, ACM TACO, CASES, DATE and other major conferences and journals, included to mainline GCC (one of the most used compilers in the world), received various international awards, and cited by ARM (p.17), Fujitsu and IBM.


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