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.

Timeline

• Our CGO'07 paper received "test of time award" at CGO'17! (2017)!
• We partnered with General Motors and developed a unique, portable and customizable open-source workflow based on the CK to help the community optimize deep learning at all levels across diverse and ever changing HW/SW stack from IoT to supercomputers!
• We partnered with ARM to design more efficient computer systems powered by Collective Knowledge and ARM's workload automation. (2016)!
• ARM and dividiti made a press-release about our Collective Knowledge Technology [ PDF (page 17) ] (2016)!
• We initiated crowd-tuning campaign, i.e. crowdsourcing GCC/LLVM tuning and combining it with active learning across diverse hardware including mobile devices and cloud services provided by volunteers using CK framework. You can see latest crowd-results in our live repository! (2016)!
• Collective Knowledge will be used in the TETRACOM-funded project to crowd-source compiler bug detection (Imperial College London and dividiti) (2016)!
• Ed Plowman (director of performance analysis strategy in ARM) says "contribute to CK and workload automation" [ Slides ] (2016)!
• We have presented our CK approach at DATE'16 [ PDF ] (2016)!
• Our Collective Knowledge technology have received HiPEAC technology transfer award (2015).
• We have received funding from the European Union (TETRACOM) and ARM to aggregate all our developments on machine-learning based auto-tuning and optimization repositories within a new, small and portable Collective Knowledge Framework. We successfully completed the project and released framework in September, 2015 (2015-cur.).
• We initiated completely open and community-driven reviewing of publications and artifacts at our ADAPT workshop (2015-cur.).
• After nearly a decade of persuading the community, we finally initiated artifact evaluation at CGO and PPoPP (our major computer systems' research conferences) (2014-cur.)!
• cTuning technology was referenced by Fujitsu as closely related to their long-term initiative on "big data" driven optimization of Exascale computer systems (2014).
• cTuning technology has been accepted as a new theme on reproducible research and experimentation in computer engineering for the EU HiPEAC network of excellence [More] (2012-2016).
• Grigori Fursin received INRIA award for "making an outstanding contribution to research" and 4-year fellowship for his cTuning technology (2012).
• cTuning technology demonstrated the possibility to fully automate construction of compiler optimization heuristics and speed up benchmarking, optimization and co-design of multi-core reconfigurable systems by several orders of magnitude thus dramatically reducing time to market for the new systems and increasing ROI. This technology is considered by IBM to be the first in the world (2009) [M4, P19, P28, press about our project]. (2006-2009).
• We proposed and implemented crowdsourcing of program optimization and compiler tuning using Collective Mind Technology and commodity mobile phones [J10, P28, P10, P50] (2006-2012)
• Grigori Fursin extended cTuning-based technology to develop customized "in house" repository of knowledge when helping to establish Intel Exascale Lab in France [I2] (2010-2011).
• We added our Interactive Compilation Interface to mainline GCC 4.6+ to support our machine learning based pluginized autotuning, sponsored by Google [S17, M7, P19, P28, F6] (2005-2009).
• We helped to add our novel run-time adaptation technique for statically compiled programs based on code multi-versioning and fast decision trees to mainline GCC 4.8+ (to automatically maximize program performance and power consumption without the need for complex recompilation and JIT infrastructures across a variety of systems such as mobile devices or data centers with VM) sponsored by Google [S14, F6, P50, P32, P28, P10, P6] (2004-2009).
• Our novel crowd-tuning approach (crowdsourcing SW/HW optimization) together with cTuning.org framework with unified interfaces, public repository of knowledge, and machine learning web services enabled world's first machine-learning based self-tuning compiler (MILEPOST GCC) [project website, new live optimization repository, framework, IBM's press-release] (2006-2010).
• After more than a decade of evangelizing collaborative and reproducible research in computer engineering with code and data sharing, our community started eventually accepting this idea. We even managed to initiate artifact evaluation at CGO, PPoPP and ADAPT (1997-cur.).