Parametrized dataflow scenarios

The FSM-based scenario-aware dataflow (FSM-SADF) model of computation has been introduced to facilitate the analysis of dynamic streaming applications. FSM-SADF interprets application's execution as an execution of a sequence of static modes of operation called scenarios. Each scenario is modeled using a synchronous dataflow (SDF) graph (SDFG), while a finite-state machine (FSM) is used to encode scenario occurrence patterns. However, FSM-SADF can precisely capture only those dynamic applications whose behaviors can be abstracted into a reasonably sized set of scenarios (coarse-grained dynamism). Nevertheless, in many cases, the application may exhibit thousands or even millions of behaviours (fine-grained dynamism). In this work, we generalize the concept of FSM-SADF to one that is able to model dynamic applications exhibiting fine-grained dynamism. We achieve this by applying parametrization to the FSM-SADF's base model, i.e. SDF, and defining scenarios over parametrized SDFGs. We refer to the extension as parametrized FSM-SADF (PFSM-SADF). Thereafter, we present a novel and a fully parametric analysis technique that allows us to derive tight worst-case performance (throughput and latency) guarantees for PFSM-SADF specifications. We evaluate our approach on a realistic case-study from the multimedia domain

RDF: Un modèle de calcul flot de données reconfigurable

Dataflow Models of Computation (MoCs) are widely used in embedded systems, including multimedia processing, digital signal processing, telecommunications, and automatic control. In a dataflow MoC, an application is specified as a graph of actors connected by FIFO channels. One of the first and most popular dataflow MoCs, Synchronous Dataflow (SDF), provides static analyses to guarantee boundedness and liveness, which are key properties for embedded systems. However, SDF and most of its variants lacks the capability to express the dynamism needed by modern streaming applications. In particular, the applications mentioned above have a strong need for reconfigurability to accommodate changes in the input data, the control objectives, or the environment. We address this need by proposing a new MoC called Reconfigurable Dataflow (RDF). RDF extends SDF with transformation rules that specify how and when the topology and actors of the graph may be reconfigured. Starting from an initial RDF graph and a set of transformation rules, an arbitrary number of new RDF graphs can be generated at runtime. A key feature of RDF is that it can be statically analyzed to guarantee that all possible graphs generated at runtime will be consistent and live. We introduce the RDF MoC, describe its associated static analyses, and present its implementation and some experimental results.Les modèles de calcul (MoCs) flot de données synchrones sont très utilisés dans les systèmes embarqués et les applications multimédia, de traitement du signal, de télécommunication et de contrôle automatique. Dans ce style de modèle, une application est spécifiée par un graphe d’acteurs connectés par des liens FIFO de communication. Un des MoCs les plus connus, SDF (pour Synchronous Dataflow), permet des analyses statiques qui garantissent l’exécution en mémoire bornée et l’absence d’interblocage, propriétés clés pour les systèmes embarqués. Néanmoins, SDF (et la plupart de ses variantes) ne permet pas d’exprimer la dynamicité requise par les applications embarquées modernes. En particulier, ces applications ont souvent besoin de se reconfigurer pour s’adapter aux changements (par ex., de débit ou de qualité) du flot d’entrée, des objectifs de contrôle ou de l’environnement. Afin de répondre à ce besoin, nous proposons RDF (pour Reconfigurable DataFlow) un MoC qui étend SDF avec des règles de transformations spécifiant comment la topologie du graphe flot de données peut être reconfiguré dynamiquement. En considérant un graphe SDF initial et un ensemble de règles de transformation, un nombre arbitraire de nouveaux graphes peuvent être produits. La principale qualité de RDF est qu’il peut être analysé statiquement pour garantir que tous les graphes générés dynamiquement s’exécuteront en mémoire bornée et sans interblocage. Nous présentons le modèle RDF, les analyses statiques associées, sa mise en oeuvre et quelques expérimentations

RDF: A Reconfigurable Dataflow Model of Computation

International audienceDataflow Models of Computation (MoCs) are widely used in embedded systems, including multimedia processing, digital signal processing, telecommunications, and automatic control. In a dataflow MoC, an application is specified as a graph of actors connected by FIFO channels. One of the first and most popular dataflow MoCs, Synchronous Dataflow (SDF), provides static analyses to guarantee boundedness and liveness, which are key properties for embedded systems. However, SDF and most of its variants lack the capability to express the dynamism needed by modern streaming applications. In particular, the applications mentioned above have a strong need for reconfigurability to accommodate changes in the input data, the control objectives, or the environment. We address this need by proposing a new MoC called Reconfigurable Dataflow (RDF). RDF extends SDF with transformation rules that specify how and when the topology and actors of the graph may be reconfigured. Starting from an initial RDF graph and a set of transformation rules, an arbitrary number of new RDF graphs can be generated at runtime. A key feature of RDF is that it can be statically analyzed to guarantee that all possible graphs generated at runtime will be consistent and live. We introduce the RDF MoC, describe its associated static analyses, and present its implementation and some experimental results

Parametrized dataflow scenarios

Although well-suited for capturing concurrency in streaming applications, purely dataflow-based models of computation are lacking in expressing intricate control requirements that many modern streaming applications have. Consequently, a number of modeling approaches combining dataflow and finite-state machines has been proposed. However, these FSM/dataflow hybrids struggle with capturing the ne-grained data-dependent dynamics of modern streaming applications. In this article, we enrich the set of such FSM/dataflow hybrids with a novel formalism that uses parameterized dataflow as the concurrency model. We call the model FSM-based parameterized scenario-aware dataflow (PFSM-SADF). Through the use of parameterized dataflow, the formalism can capture the application ne-grained data-dependent dynamics while the enveloping FSM enables the capturing of the application control flow. We demonstrate the application of our modeling framework to synchronous dataflow (SDF), for which we propose a worst-case performance analysis framework based on the Max-plus algebraic semantics of SDF and the theory of Max-plus automata. We show that using the novel hybrid one can give tighter bounds on worst-case performance metrics such as throughput and latency for streaming applications exposing fine-grained dynamic behavior embedded inside a control-flow structure then by using the existing hybrids. We evaluate our approach on a realistic case-study from the multimedia domain

Parametrized dataflow scenarios

The FSM-based scenario-aware dataflow (FSM-SADF) model of computation has been introduced to facilitate the analysis of dynamic streaming applications. FSM-SADF interprets application's execution as an execution of a sequence of static modes of operation called scenarios. Each scenario is modeled using a synchronous dataflow (SDF) graph (SDFG), while a finite-state machine (FSM) is used to encode scenario occurrence patterns. However, FSM-SADF can precisely capture only those dynamic applications whose behaviors can be abstracted into a reasonably sized set of scenarios (coarse-grained dynamism). Nevertheless, in many cases, the application may exhibit thousands or even millions of behaviours (fine-grained dynamism). In this work, we generalize the concept of FSM-SADF to one that is able to model dynamic applications exhibiting fine-grained dynamism. We achieve this by applying parametrization to the FSM-SADF's base model, i.e. SDF, and defining scenarios over parametrized SDFGs. We refer to the extension as parametrized FSM-SADF (PFSM-SADF). Thereafter, we present a novel and a fully parametric analysis technique that allows us to derive tight worst-case performance (throughput and latency) guarantees for PFSM-SADF specifications. We evaluate our approach on a realistic case-study from the multimedia domain