In embedded and cyber-physical systems, the design of a desired functionality under constraints increasingly requires parallel execution of a set of tasks on a heterogeneous architecture. The nature of such parallel systems complicates the process of understanding and predicting performance in terms of response time. Indeed, response time depends on many factors related to both the functionality and the target architecture. State-of-the-art strategies derive response time by examining the operations required by each task for both processing and accessing shared resources. This procedure is often followed by the addition or elimination of potential interference due to task concurrency. However, such approaches require an advanced knowledge of the software and hardware details, rarely available in practice. This work presents an alternative “top-down” strategy, called PathTracer, aimed at understanding software response time and extending the cases in which it can be analyzed and estimated. PathTracer leverages on dataflow-based application representation and response time estimation of signal processing applications mapped on heterogeneous Multiprocessor Systems-on-a-Chip (MPSoCs). Experimental results demonstrate that PathTracer provides (i) information on the nature of the application (work-dominated, span-dominated, or balanced parallel), and (ii) response time modeling which can reach high accuracy when performed post-execution, leading to prediction errors with average and standard deviation under 5% and 3% respectively.
PathTracer: Understanding Response Time of Signal Processing Applications on Heterogeneous MPSoCs
Rubattu Claudio;Palumbo Francesca;
2021-01-01
Abstract
In embedded and cyber-physical systems, the design of a desired functionality under constraints increasingly requires parallel execution of a set of tasks on a heterogeneous architecture. The nature of such parallel systems complicates the process of understanding and predicting performance in terms of response time. Indeed, response time depends on many factors related to both the functionality and the target architecture. State-of-the-art strategies derive response time by examining the operations required by each task for both processing and accessing shared resources. This procedure is often followed by the addition or elimination of potential interference due to task concurrency. However, such approaches require an advanced knowledge of the software and hardware details, rarely available in practice. This work presents an alternative “top-down” strategy, called PathTracer, aimed at understanding software response time and extending the cases in which it can be analyzed and estimated. PathTracer leverages on dataflow-based application representation and response time estimation of signal processing applications mapped on heterogeneous Multiprocessor Systems-on-a-Chip (MPSoCs). Experimental results demonstrate that PathTracer provides (i) information on the nature of the application (work-dominated, span-dominated, or balanced parallel), and (ii) response time modeling which can reach high accuracy when performed post-execution, leading to prediction errors with average and standard deviation under 5% and 3% respectively.
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