A Minimum Cost Approach for Segmenting Networks of Lines
International Journal of Computer Vision
A data and task parallel image processing environment
Parallel Computing - Parallel computing in image and video processing
Automatic Detection of Parallelism: A Grand Challenge for High-Performance Computing
IEEE Parallel & Distributed Technology: Systems & Technology
Approaches for Integrating Task and Data Parallelism
IEEE Concurrency
A PVM Implementation of a Portable Parallel Image Processing Library
EuroPVM '96 Proceedings of the Third European PVM Conference on Parallel Virtual Machine
Commodity cluster-based parallel processing of hyperspectral imagery
Journal of Parallel and Distributed Computing
A Grid framework to enable parallel and concurrent TMA image analyses
International Journal of Grid and Utility Computing
User transparent task parallel multimedia content analysis
Euro-Par'10 Proceedings of the 16th international Euro-Par conference on Parallel processing: Part II
Towards user transparent parallel multimedia computing on GPU-Clusters
ISCA'10 Proceedings of the 2010 international conference on Computer Architecture
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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. However, as most MMCA researchers are not also HPC experts, in the field there is a demand~for~programming models and tools that are both efficient and easy~to~use. Today several user transparent library-based parallelization tools exist that aim to satisfy both these requirements. Such tools generally use a data parallel approach in which data structures (e.g. video frames) are scattered among the available nodes in a compute cluster. 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 approaches. This paper presents Pyxis-DT: a user transparent parallel programming model for MMCA applications that employs both data and task parallelism. Hybrid parallel execution is obtained by run-time construction and execution of a task graph consisting of strictly defined building block operations. Each of these building block operations can be executed in data parallel fashion. Results show that for realistic MMCA applications the concurrent use of data and task parallelism can significantly improve performance compared to using either approach in isolation.