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STR2RTS: Refactored StreamIT Benchmarks into Statically Analyzable Parallel Benchmarks for WCET Estimation & Real-Time Scheduling

Authors Benjamin Rouxel, Isabelle Puaut

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Benjamin Rouxel
Isabelle Puaut

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Benjamin Rouxel and Isabelle Puaut. STR2RTS: Refactored StreamIT Benchmarks into Statically Analyzable Parallel Benchmarks for WCET Estimation & Real-Time Scheduling. In 17th International Workshop on Worst-Case Execution Time Analysis (WCET 2017). Open Access Series in Informatics (OASIcs), Volume 57, pp. 1:1-1:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)
https://doi.org/10.4230/OASIcs.WCET.2017.1

Abstract

We all had quite a time to find non-proprietary architecture-independent exploitable parallel benchmarks for Worst-Case Execution Time (WCET) estimation and real-time scheduling. However, there is no consensus on a parallel benchmark suite, when compared to the single-core era and the Mälardalen benchmark suite. This document bridges part of this gap, by presenting a collection of benchmarks with the following good properties: (i) easily analyzable by static WCET estimation tools (written in structured C language, in particular neither goto nor dynamic memory allocation, containing flow information such as loop bounds); (ii) independent from any particular run-time system (MPI, OpenMP) or real-time operating system. Each benchmark is composed of the C source code of its tasks, and an XML description describing the structure of the application (tasks and amount of data exchanged between them when applicable). Each benchmark can be integrated in a full end-to-end empirical method validation protocol on multi-core architecture. This proposed collection of benchmarks is derived from the well known StreamIT [Thies et al., Comp. Constr., 2002] benchmark suite and will be integrated in the TACleBench suite [Falk et al., WCET, 2016] in a near future. All these benchmarks are available at https://gitlab.inria.fr/brouxel/STR2RTS.
Keywords
  • Parallel benchmarks
  • Tasks scheduling
  • Worst-Case Execution Time estimation

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