Albert Cohen
Abstract - Semantics and Concurrent Data Structures for Dynamic Streaming Constructs.
Stream computing is often associated with regular, data-intensive
applications, and more specifically with the family of cyclo-static
data-flow models. The term has also refers to bulk-synchronous data
parallelism overlapping computations and communications. Both
interpretations are valid but incomplete: streams underline the formal
definition of Kahn process networks for 4 decades, a foundation for a
much more general class of deterministic concurrency in languages and
systems with a solid heritage. Stream computing is a semantical
framework for parallel languages and as a model for pipelined,
task-parallel execution. Supporting research on parallel languages
with dynamic, nested task creation and first-class streams, we are
developing a generic stream-computing execution environment combining
expressiveness, efficiency and strong correctness guarantees. In
particular, we propose a new lock-free algorithm for stalling and
waking-up tasks in a user-space scheduler according to changes in the
state of the corresponding queues. The algorithm is portable and
proven correct against the C11 weak memory model.
Short bio
Albert Cohen is a senior research scientist at INRIA and a part-time
associate professor at École Polytechnique, Paris, France. He
graduated from École Normale Supérieure de Lyon, and received his PhD
from the University of Versailles in 1999 (awarded two national
prizes). He has been a visiting scholar at the University of Illinois
in 2000 and 2001, and an invited professor at Philips Research (then
NXP Semiconductors), Eindhoven in 2006 and 2007. Albert Cohen works on
optimizing compilers for high-performance and embedded systems,
automatic parallelization, data-flow and synchronous
programming. Several research projects initiated or led by Albert
Cohen resulted in the transfer of advanced compilation techniques to
production compilers.