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CAPS entreprise (CAPS)


Founded in 2002 by members of an INRIA research team, CAPS develops and commercializes innovative software for high performance application tuning in the domains of HPC and embedded systems. CAPS offers a whole range of development tools and services enabling its customers‘ applications to optimize the performance of multicore processors used by the last generation hardware. In 2010 CAPS opened two new subsidiary offices in Shanghai, China, and in the heart of Silicon Valley, in USA. CAPS is a member of the OpenMP consortium and a member of the Multicore Association.


CAPS mission is in keeping with the innovative and fast moving multicore market and helps industries with high level HPC issues such as oil and gas, defense, finance and life sciences to allow their software developers to take the most of multicore processors while preserving their legacy source. Built on over five years of advanced research and development, CAPS provides high quality and cost effective programming tools that leverage the computing power of evolving manycore hybrid platforms.


In this project, CAPS will bring its expertise in parallel high level code generation and optimization for heterogeneous multi-cores.

Key personnel

Francois Bodin cofounded CAPS in 2002 while he was a Professor at University of Rennes I and since January 2008 he joined the company as CTO. His contribution includes new approaches for exploiting high performance processors in scientific computing and in embedded applications. Prior to joining CAPS, Francois Bodin held various research positions at University of Rennes I and at the INRIA research lab. He has published over 60 papers in international journals and conferences and he has supervised over 15 PhD thesis. Professor Francois Bodin holds a Master‘s and a PhD in CS, both from University of Rennes I. He is a member of HIPEAC. Currently, he is also the Chairman of Irisa, a major computer science laboratory.

Result dissemination and exploitation

CAPS entreprise expect AutoTune to have a great impact on the multicore programming market (which is potentially huge). Indeed, with more cores available and more applications being ported the need for more performance tuning tools to help writing efficient parallel is rapidly increasing. Currently, multicore programming, especially for heterogeneous systems, is limited to educated advanced programmers. New technology is required, on one hand, for helping less trained people, and on second hand, to  provide more portability in performance. AutoTune proposed approach carries the potential to achieve both goals.

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