Semantics and Concurrent Data Structures for Dynamic Streaming Languages

Prof. Albert Cohen (École polytechnique, Paris) Stream computing is often associated with regular, data-intensive applications, and more specifically with the family of cyclo-static data-flow models. The term 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 more general class of deterministic concurrent 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.