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 imageprocessing 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.
With the introduction of dynamic image processing, such as in image analysis, the computational complexity has become data dependent and memory usage irregular. Therefore, the possibility of runtime estimation of resource usage would be highly attractive and would enable Quality-of-Service (QoS) control for dynamic image-processing applications with shared resources. A possible solution to this problem is to characterize the application execution using model descriptions of the resource usage. In this paper, we attempt to predict resource usage for groups of dynamic imageprocessing tasks based on Markov-chain modeling. As a typical application, we explore a medical imaging application to enhance a wire mesh tube (stent) under X-ray fluoroscopy imaging during angioplasty. Simulations show that Markov modeling can be successfully applied to describe the resource usage function even if the flow graph dynamically switches between groups of tasks. For the evaluated sequences, an average prediction accuracy of 97% is reached with sporadic excursions of the prediction error up to 20-30%.
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