Parametric Throughput Analysis of Synchronous Data Flow Graphs

Synchronous Data Flow Graphs (SDFGs) have proved to be a very successful tool for modeling, analysis and synthesis of multimedia applications targeted at both single- and multiprocessor platforms. One of the most prominent performance constraints of concurrent real-time applications is throughput. For given actor execution times, throughput can be verified by analyzing the SDFG models of such applications, for instance using maximum cycle mean analysis or state space analysis. In various contexts, such as design space exploration or run-time reconfiguration, many fast throughput computations are required for varying actor execution times. We present methods to compute throughput of an SDFG where actor execution times can be parameters. The throughput of these graphs is obtained in the form of a function of these parameters. Recalculation of throughput is then merely an evaluation of this function for specific parameter values, which is much faster than the standard throughput analysis. We propose three different algorithms for parametric throughput analysis and evaluate these algorithms experimentally, showing the feasibility of the approach and showing that a divide and conquer algorithm performs best.

QoS management for wireless sensor networks with a mobile sink

10.1007/978-3-642-00224-3_4Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)5432 LNCS53-6

A Scenario-Aware Data Flow Model for Combined Long-Run Average and Worst-Case Performance Analysis

Data flow models are used for specifying and analysing signal processing and streaming applications. However, traditional data flow models are either not capable of expressing the dynamic aspects of modern streaming applications or they do not support relevant analysis techniques. The dynamism in modern streaming applications often originates from different modes of operation (scenarios) in which data production and consumption rates and/or execution times may differ. This paper introduces a scenario-aware generalisation of the Synchronous Data Flow model, which uses a stochastic approach to model the order in which scenarios occur. The formally defined operational semantics of a Scenario-Aware Data Flow model implies a Markov chain, which can be analysed for both long-run average and worst-case performance metrics using existing exhaustive or simulation-based techniques. The potential of using Scenario-Aware Data Flow models for performance analysis of modern streaming applications is illustrated with an MPEG-4 decoder example. 1

Throughput analysis of synchronous data flow graphs

Synchronous Data Flow Graphs (SDFGs) are a useful tool for modeling and analyzing embedded data flow applications, both in a single processor and a multiprocessing context or for application mapping on platforms. Throughput analysis of these SDFGs is an important step for verifying throughput requirements of concurrent real-time applications, for instance within design-space exploration activities. Analysis of SDFGs can be hard, since the worst-case complexity of analysis algorithms is often high. This is also true for throughput analysis. In particular, many algorithms involve a conversion to another kind of data flow graph, the size of which can be exponentially larger than the size of the original graph. In this paper, we present a method for throughput analysis of SDFGs, based on explicit statespace exploration and we show that the method, despite its worst-case complexity, works well in practice, while existing methods often fail. We demonstrate this by comparing the method with state-of-the-art cycle mean computation algorithms. Moreover, since the state-space exploration method is essentially the same as simulation of the graph, the results of this paper can be easily obtained as a byproduct in existing simulation tools.