Analyzing memory access intensity in parallel programs on multicore
Proceedings of the 22nd annual international conference on Supercomputing
Overview of Multicore Requirements towards Real-Time Communication
SEUS '09 Proceedings of the 7th IFIP WG 10.2 International Workshop on Software Technologies for Embedded and Ubiquitous Systems
Multimedia Mining on Manycore Architectures: The Case for GPUs
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Scene text detection suitable for parallelizing on multi-core
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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Content-Based Video Information Retrieval (CBVIR) has becoming one of the best solutions for retrieving useful information from today's video information explosion. And with the rapid development of modern technologies, CBVIR is emerging as a mass market desktop application. There is evidence that visual feature extraction is the most time-consuming part in a CBVIR system. In this paper, we implement three video visual feature extractions in parallel by exploring different kinds of thread-level parallelism. We also conduct detailed scalability and memory performance analysis on two multi-core based systems, in order to gain more insights into video-analysis related applications on future multi-core systems. From our analysis we identify the likely causes of bottlenecks in these kinds of applications and suggest ways to improve scalability.