Optimal and fast throughput evaluation of CSDF

International audienceThe Synchronous Dataow Graph (SDFG) and Cyclo-Static Dataow Graph (CSDFG) are two well-known models, used practically by industry for many years, and for which there is a large number of analysis techniques. Yet, basic problems such as the throughput computation or the liveness evaluation are not well solved, and their complexity is still unknown. In this paper, we propose K-Iter, an iterative algorithm based on K-periodic scheduling to compute the throughput of a CSDFG. By using this technique, we are able to compute in less than a minute the throughput of industry applications for which no result was available before

System-level design of energy-efficient sensor-based human activity recognition systems: a model-based approach

This thesis contributes an evaluation of state-of-the-art dataflow models of computation regarding their suitability for a model-based design and analysis of human activity recognition systems, in terms of expressiveness and analyzability, as well as model accuracy. Different aspects of state-of-the-art human activity recognition systems have been modeled and analyzed. Based on existing methods, novel analysis approaches have been developed to acquire extra-functional properties like processor utilization, data communication rates, and finally energy consumption of the system

Generalized strictly periodic scheduling analysis, resource optimization, and implementation of adaptive streaming applications

This thesis focuses on addressing four research problems in designing embedded streaming systems. Embedded streaming systems are those systems thatprocess a stream of input data coming from the environment and generate a stream of output data going into the environment. For many embeddedstreaming systems, the timing is a critical design requirement, in which the correct behavior depends on both the correctness of output data and on the time at which the data is produced. An embedded streaming system subjected to such a timing requirement is called a real-time system. Some examples of real-time embedded streaming systems can be found in various autonomous mobile systems, such as planes, self-driving cars, and drones. To handle the tight timing requirements of such real-time embedded streaming systems, modern embedded systems have been equipped with hardware platforms, the so-called Multi-Processor Systems-on-Chip (MPSoC), that contain multiple processors, memories, interconnections, and other hardware peripherals on a single chip, to benefit from parallel execution. To efficiently exploit the computational capacity of an MPSoC platform, a streaming application which is going to be executed on the MPSoC platform must be expressed primarily in a parallel fashion, i.e., the application is represented as a set of parallel executing and communicating tasks. Then, the main challenge is how to schedule the tasks spatially, i.e., task mapping, and temporally, i.e., task scheduling, on the MPSoC platform such that all timing requirements are satisfied while making efficient utilization of available resources (e.g, processors, memory, energy, etc.) on the platform. Another challenge is how to implement and run the mapped and scheduled application tasks on the MPSoC platform. This thesis proposes several techniques to address the aforementioned two challenges.NWOComputer Systems, Imagery and Medi

On hard real-time scheduling of cyclo-static dataflow and its application in system-level design

This dissertation addresses the problem of designing hard real-time streaming systems running a set of parallel streaming programs in an automated way such that the programs provably meet their timing requirements. A scheduling framework is proposed with which it is analytically proven that any streaming program, modeled as an acyclic Cyclo-Static Dataflow (CSDF) graph, can be executed as a set of real-time periodic tasks. The proposed framework computes the parameters of the periodic tasks corresponding to the graph actors and the minimum buffer sizes of the communication channels such that a valid periodic schedule is guaranteed to exist. In order to demonstrate the effectiveness of the proposed scheduling framework, a system-level design flow that incorporates the scheduling framework is proposed. This proposed design flow accepts, as input, algorithmic sequential specifications of streaming programs, and then applies a set of systematic and automated steps that produce, as output, the final system implementation, which provably meets the timing requirements of the programs. The final system implementation consists of the parallelized versions of the input streaming programs together with the hardware needed to run them. The proposed scheduling framework and design flow are evaluated through a set of experiments. These experiments illustrate the effectiveness of the proposed scheduling framework and design flow.Computer Systems, Imagery and Medi

On the Hard-Real-Time Scheduling of Embedded Streaming Applications

Computer Systems, Imagery and Medi

Ordonnancement hybride des applications flots de données sur des systèmes embarqués multi-coeurs

Les systèmes embarqués sont de plus en plus présents dans l'industrie comme dans la vie quotidienne. Une grande partie de ces systèmes comprend des applications effectuant du traitement intensif des données: elles utilisent de nombreux filtres numériques, où les opérations sur les données sont répétitives et ont un contrôle limité. Les graphes "flots de données", grâce à leur déterminisme fonctionnel inhérent, sont très répandus pour modéliser les systèmes embarqués connus sous le nom de "data-driven". L'ordonnancement statique et périodique des graphes flot de données a été largement étudié, surtout pour deux modèles particuliers: SDF et CSDF. Dans cette thèse, on s'intéresse plus particulièrement à l'ordonnancement périodique des graphes CSDF. Le problème consiste à identifier des séquences périodiques infinies d'actionnement des acteurs qui aboutissent à des exécutions complètes à buffers bornés. L'objectif est de pouvoir aborder ce problème sous des angles différents : maximisation de débit, minimisation de la latence et minimisation de la capacité des buffers. La plupart des travaux existants proposent des solutions pour l'optimisation du débit et négligent le problème d'optimisation de la latence et propose même dans certains cas des ordonnancements qui ont un impact négatif sur elle afin de conserver les propriétés de périodicité. On propose dans cette thèse un ordonnancement hybride, nommé Self-Timed Périodique (STP), qui peut conserver les propriétés d'un ordonnancement périodique et à la fois améliorer considérablement sa performance en terme de latence.One of the most important aspects of parallel computing is its close relation to the underlying hardware and programming models. In this PhD thesis, we take dataflow as the basic model of computation, as it fits the streaming application domain. Cyclo-Static Dataflow (CSDF) is particularly interesting because this variant is one of the most expressive dataflow models while still being analyzable at design time. Describing the system at higher levels of abstraction is not sufficient, e.g. dataflow have no direct means to optimize communication channels generally based on shared buffers. Therefore, we need to link the dataflow MoCs used for performance analysis of the programs, the real time task models used for timing analysis and the low-level model used to derive communication times. This thesis proposes a design flow that meets these challenges, while enabling features such as temporal isolation and taking into account other challenges such as predictability and ease of validation. To this end, we propose a new scheduling policy noted Self-Timed Periodic (STP), which is an execution model combining Self-Timed Scheduling (STS) with periodic scheduling. In STP scheduling, actors are no longer strictly periodic but self-timed assigned to periodic levels: the period of each actor under periodic scheduling is replaced by its worst-case execution time. Then, STP retains some of the performance and flexibility of self-timed schedule, in which execution times of actors need only be estimates, and at the same time makes use of the fact that with a periodic schedule we can derive a tight estimation of the required performance metrics

Adaptive streaming applications : analysis and implementation models

This thesis presents a highly automated design framework, called DaedalusRT, and several novel techniques. As the foundation of the DaedalusRT design framework, two types of dataflow Models-of-Computation (MoC) are used, one as timing analysis model and another one as the implementation model. The timing analysis model is used to formally reason about timing behavior of an application. In the context of DaedalusRT, the Mode-Aware Data Flow (MADF) MoC has been developed as the timing analysis model for adaptive streaming applications using different static modes. A novel mode transition protocol is devised to allow efficient reasoning of timing behavior during mode transitions. Based on the transition protocol, a hard real-time scheduling approach is proposed. On the other hand, the implementation model is used for efficient code generation of parallel computation, communication, and synchronization. In this thesis, the Parameterized Polyhedral Process Network (P3N) MoC has been developed to model adaptive streaming applications with parameter reconfiguration. An approach to verify the functional property of the P3N MoC has been devised. Finally, implementation of the P3N MoC on a MPSoC platform has shown that run-time performance penalty due to parameter reconfiguration is negligible.Technology Foundation STWComputer Systems, Imagery and Medi