A Framework for Exploiting Task and Data Parallelism on Distributed Memory Multicomputers
IEEE Transactions on Parallel and Distributed Systems
A Minimum Cost Approach for Segmenting Networks of Lines
International Journal of Computer Vision
Automatic Detection of Parallelism: A Grand Challenge for High-Performance Computing
IEEE Parallel & Distributed Technology: Systems & Technology
A PVM Implementation of a Portable Parallel Image Processing Library
EuroPVM '96 Proceedings of the Third European PVM Conference on Parallel Virtual Machine
IEEE Transactions on Parallel and Distributed Systems
Concurrency and Computation: Practice & Experience
Commodity cluster-based parallel processing of hyperspectral imagery
Journal of Parallel and Distributed Computing
The Semantic Pathfinder: Using an Authoring Metaphor for Generic Multimedia Indexing
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Grid framework to enable parallel and concurrent TMA image analyses
International Journal of Grid and Utility Computing
User Transparent Data and Task Parallel Multimedia Computing with Pyxis-DT
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
User transparent data and task parallel multimedia computing with Pyxis-DT
Future Generation Computer Systems
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The research area of Multimedia Content Analysis (MMCA) considers all aspects of the automated extraction of knowledge from multimedia archives and data streams. To satisfy the increasing computational demands of emerging MMCA problems, there is an urgent need to apply High Performance Computing (HPC) techniques. As most MMCA researchers are not also experts in the field of HPC, there is a demand for programming models and tools that can help MMCA researchers in applying these techniques. Ideally, such models and tools should be efficient and easy to use. At present there are several user transparent library-based tools available that aim to satisfy both these conditions. All such tools use a data parallel approach in which data structures (e.g. video frames) are scattered among the available compute nodes. However, for certain MMCA applications a data parallel approach induces intensive communication, which significantly decreases performance. In these situations, we can benefit from applying alternative parallelization approaches. This paper presents an innovative user transparent programming model for MMCA applications that employs task parallelism. We show our programmingmodel to be a viable alternative that is capable of outperforming existing user transparent data parallel approaches. As a result, the model is an important next step towards our goal of integrating data and task parallelism under a familiar sequential programming interface.