Designing and optimizing computing solutions has become extremely challenging due to an exploding number of available choices and their interactions. Unfortunately, limited understanding of trade-offs, combined with the cost and time-to-market pressures, leads to few design and optimization choices being explored. This often results in over-provisioned (expensive) and under-performing (uncompetitive) products.
Over the past 10 years we have developed Collective Knowledge, a unique technology and scientific methodology using universal autotuning, optimization knowledge sharing and predictive analytics, to dramatically accelerate software and hardware co-design.
Our approach has been successfully validated in multiple academic and industrial projects, and received several international awards. We have helped our partners (including some Fortune 100 companies) achieve 2-20x performance increases, 30% energy reductions, 20% code size reductions, and automatic detection of software and hardware bugs for their business-critical use cases.
We use our exceptional scientific and engineering background, strong commitment and motivation, and unique Collective Knowledge approach to deliver results of highest value, on time.
We can help you:
Anton has been working in the area of programming languages and tools for 15 years, both as a researcher and engineer, primarily focusing on productivity, efficiency and portability of programming techniques for heterogeneous systems. Anton founded dividiti to pursue his vision of efficient and reliable computing everywhere.
In 2010-2015, Anton led development of GPU Compute programming technologies for the ARM Mali GPUs, including production (OpenCL, RenderScript) and research (EU-funded CARP project) compilers. He was actively involved in championing technology transfer, educating professional and academic developers, engaging with partners and customers, contributing to open-source projects and standardization efforts. In 2008-2009 he was a post-doctoral research associate at Imperial College London.
Grigori has an interdisciplinary background in computer engineering, physics and predictive analytics with more than 20 years of R&D experience. He has pioneered systematic performance analysis, optimization and adaptation of computing systems based on statistical analysis, machine learning, automatic and crowdsourced tuning. Grigori co-founded dividiti to focus on transferring to industry his unique open-source Collective Knowledge technology consisting of a customizable repository of knowledge, plugin-based autotuning framework and web services for predictive analytics (statistical analysis, data mining, machine learning and feature selection).
Since 2007 when he joined INRIA as a tenured research scientist, Grigori lead several highly successful R&D projects including the EU-funded MILEPOST project that produced the world's first machine learning based production compiler (MILEPOST GCC). In 2010-2011, Grigori was on leave from INRIA invited to establish the Intel Exascale Lab in France while serving as the head of its program optimization and characterization group.
As founder of the nonprofit cTuning Foundation, Grigori is also leading a new artifact evaluation initiative for PPoPP and CGO, the premier ACM conferences on parallel programming and code generation, which aims to encourage sharing of code and data to enable reproducible systems research and engineering.