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.
|Title of host publication||IEEE Internationatiol Symposium on Parallel and Distributed Processing 2009, IPDPS 2009, 23-29 May 2009, Rome, Italy|
|Place of Publication||Piscataway|
|Publisher||Institute of Electrical and Electronics Engineers|
|Publication status||Published - 2009|
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