Performance evaluation of UML2-modeled embedded streaming applications with system-level simulation

  • Authors:
  • Tero Arpinen;Erno Salminen;Timo D. Hämäläinen;Marko Hännikäinen

  • Affiliations:
  • Department of Computer Systems, Tampere University of Technology, Tampere, Finland;Department of Computer Systems, Tampere University of Technology, Tampere, Finland;Department of Computer Systems, Tampere University of Technology, Tampere, Finland;Department of Computer Systems, Tampere University of Technology, Tampere, Finland

  • Venue:
  • EURASIP Journal on Embedded Systems - Special issue on design and architectures for signal and image processing
  • Year:
  • 2009

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Abstract

This article presents an efficient method to capture abstract performance model of streaming data real-time embedded systems (RTESs). Unified Modeling Language version 2 (UML2) is used for the performance modeling and as a front-end for a tool framework that enables simulation-based performance evaluation and design-space exploration. The adopted application meta-model in UML resembles the Kahn Process Network (KPN) model and it is targeted at simulation-based performance evaluation. The application workload modeling is done using UML2 activity diagrams, and platform is described with structural UML2 diagrams and model elements. These concepts are defined using a subset of the profile for Modeling and Analysis of Realtime and Embedded (MARTE) systems from OMG and custom stereotype extensions. The goal of the performance modeling and simulation is to achieve early estimates on task response times, processing element, memory, and on-chip network utilizations, among other information that is used for design-space exploration. As a case study, a video codec application on multiple processors is modeled, evaluated, and explored. In comparison to related work, this is the first proposal that defines transformation between UML activity diagrams and streaming data application workload meta models and successfully adopts it for RTES performance evaluation.