Triple-C: resource-usage prediction for semi-automatic parallelization of groups of dynamic image-processing tasks

A.H.R. Albers, P.H.N. With, de, E. Suijs

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Abstract

With the emergence of dynamic video processing, such as in image analysis, runtime estimation of resource usage would be highly attractive for automatic parallelization and QoS control with shared resources. A possible solution is to characterize the application execution using model descriptions of the resource usage. In this paper, we introduce Triple-C, a prediction model for computation, cache-memory and communication-bandwidth usage with scenario-based Markov chains. As a typical application, we explore a medical imaging function to enhance objects of interest in X-ray angiography sequences. Experimental results show that our method can be successfully applied to describe the resource usage for dynamic image-processing tasks, even if the flow graph dynamically switches between groups of tasks. An average prediction accuracy of 97% is reached with sporadic excursions of the prediction error up to 20-30%. As a case study, we exploit the prediction results for semi-automatic parallelization. Results show that with Triple-C prediction, dynamic processing tasks can be executed in real-time with a constant low latency.
Original languageEnglish
Title of host publicationIEEE Internationatiol Symposium on Parallel and Distributed Processing 2009, IPDPS 2009, 23-29 May 2009, Rome, Italy
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages1-8
ISBN (Print)978-1-4244-3751-1
DOIs
Publication statusPublished - 2009

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    Albers, A. H. R., With, de, P. H. N., & Suijs, E. (2009). Triple-C: resource-usage prediction for semi-automatic parallelization of groups of dynamic image-processing tasks. In IEEE Internationatiol Symposium on Parallel and Distributed Processing 2009, IPDPS 2009, 23-29 May 2009, Rome, Italy (pp. 1-8). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IPDPS.2009.5160942