A Minimum Cost Approach for Segmenting Networks of Lines
International Journal of Computer Vision
A software architecture for user transparent parallel image processing
Parallel Computing - Parallel computing in image and video processing
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
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
Parallel Computing Experiences with CUDA
IEEE Micro
A Grid framework to enable parallel and concurrent TMA image analyses
International Journal of Grid and Utility Computing
Programming Massively Parallel Processors: A Hands-on Approach
Programming Massively Parallel Processors: A Hands-on Approach
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)
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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 MMCA problems, the use of High Performance Computing (HPC) techniques is essential. As most MMCA researchers are not HPC experts, there is an urgent need for ‘familiar' programming models and tools that are both easy to use and efficient. Today, several user transparent library-based parallelization tools exist that aim to satisfy both these requirements. In general, such tools focus on data parallel execution on traditional compute clusters. As of yet, none of these tools also incorporate the use of many-core processors (e.g. GPUs), however. While traditional clusters are now being transformed into GPU-clusters, programming complexity vastly increases -- and the need for easy and efficient programming models is as urgent as ever. This paper presents our first steps in the direction of obtaining a user transparent programming model for data parallel and hierarchical multimedia computing on GPU-clusters. The model is obtained by extending an existing user transparent parallel programming system (applicable to traditional compute clusters) with a set of CUDA compute kernels. We show our model to be capable of obtaining orders-of-magnitude speed improvements, without requiring any additional effort from the application programmer.