InvasIC Seminar: Vorträge im SFB/TRR 89 (HD 1280 - Video & Folien)

This talk highlights challenges and contributions in worst-case execution time analysis for real-time system considering architectural changes over time and discusses future trends and open research problems.

General purpose CPUs with dozens of computing cores are currently reaching the market. Some researchers even expect chips with thousands of computing cores to be available in the foreseeable future. In this talk we will discuss the architectures of some current multi- and many-core CPUs with an emphasis on understanding the hardware foundation of today's parallel computing systems. In particular we will concentrate on the memory hierarchy of these CPUs and the inherent cost of sharing in cache-coherent multi-core systems. Additionally, we will examine typical problems that system designers and application programmers have to solve when they try to utilize such hardware architectures effectively.

Many of today''s embedded systems are data-intensive. They are moreover more and more often operating in an open, dynamic environment with varying processing workloads and changing resource availability. Dataflow models of computation are well-suited for the model-driven design of data-intensive embedded systems. Traditional dataflow models like synchronous dataflow cannot efficiently cope with the dynamics of modern systems. Novel computational models such as scenario-aware dataflow have been developed to address this challenge. In this presentation, I will present an overview of model-driven embedded-system design using dataflow models, surveying the state of the art in system synthesis and highlighting some remaining challenges.

Many of today''s embedded systems are data-intensive. They are moreover more and more often operating in an open, dynamic environment with varying processing workloads and changing resource availability. Dataflow models of computation are well-suited for the model-driven design of data-intensive embedded systems. Traditional dataflow models like synchronous dataflow cannot efficiently cope with the dynamics of modern systems. Novel computational models such as scenario-aware dataflow have been developed to address this challenge. In this presentation, I will present an overview of model-driven embedded-system design using dataflow models, surveying the state of the art in system synthesis and highlighting some remaining challenges.

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.