Execution-time Prediction for Dynamic Streaming Applications with Task-level Parallelism
Programmable multiprocessor systems-on-chip are
becoming the preferred implementation platform for embedded
streaming applications. This enables using more software
components, which leads to large and frequent dynamic variations
of data-dependent execution times. In this context, accurate and
conservative prediction of execution times helps in maintaining
good audio/video quality and reducing energy consumption by
dynamic evaluation of the amount of on-chip resources needed by
applications. To be effective, multiprocessor systems have to
employ the available parallelism. The combination of task-level
parallelism and task delay variations makes predicting execution
times a very hard problem. So far, under these conditions, no
appropriate techniques exist for the conservative prediction of
execution times with the required accuracy. In this paper, we
present a novel technique for this problem, exploiting the concept
of scenario-based prediction, and taking into account the transient
and periodic behavior of scenarios and the effect of scenario
transitions. In our MPEG-4 shape-decoder case study, we observe
no more than 11% average overestimation.
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Execution-time Prediction for Dynamic Streaming Applications with Task-level Parallelism
P. Poplavko, T. Basten, J.L. van Meerbergen.
In H. Kubatova, editor, Digital System Design, 10th EUROMICRO Conference, DSD 2007, Proceedings, pages 228-235. Lübeck, Germany, 29-31 August 2007. IEEE Computer Society Press, Los Alamitos, CA, USA, 2007. (abstract, pdf).
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