User Transparent Task Parallel Multimedia Content Analysis

Kessel, Timo van; Drost, Niels; Seinstra, F.J.

doi:10.1007/978-3-642-15291-7_5

Cited by 3 publications

(6 citation statements)

References 12 publications

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“…Table I shows the results for the line detection application without post-processing. For this application it is known that a data parallel approach does not lead to good speedups [2]. This is because of a need for repetitive broadcasts of full image data structures among all nodes.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, in the task parallel programming model we use the same set of basic building block operations as in Pyxis-D to construct applications. In [2] we have shown that for certain specific applications this approach can outperform Parallel-Horus and Pyxis-D.…”

Section: A Task Parallel Alternativementioning

confidence: 96%

“…Results for Pyxis-T have indicated that a user transparent task parallel approach can be a viable alternative to data parallelism, but not in all cases [2].…”

Section: A Task Parallel Alternativementioning

confidence: 98%

“…Parallel-Horus defines a set of basic building block operations that can be combined to obtain efficient applications that run in strictly data parallel fashion. Alternatively, the same basic building block operations can be used to come to a strictly task parallel execution of the same applications [2].…”

Section: Introductionmentioning

confidence: 99%

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User Transparent Data and Task Parallel Multimedia Computing with Pyxis-DT

Kessel

Drost

Maassen

et al. 2012

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 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.

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Section: Discussionmentioning

confidence: 99%

Section: A Task Parallel Alternativementioning

confidence: 96%

“…Results for Pyxis-T have indicated that a user transparent task parallel approach can be a viable alternative to data parallelism, but not in all cases [2].…”

Section: A Task Parallel Alternativementioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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User Transparent Data and Task Parallel Multimedia Computing with Pyxis-DT

Kessel

Drost

Maassen

et al. 2012

2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (Ccgrid 2012)

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“…Moreover, and as shown in our evaluation below, extensions to Jorus and Parallel-Horus that allow for services-based distributed multimedia computing, have been applied successfully in large-scale distributed systems, involving hundreds of massively communicating compute resources covering the entire globe [42]. Finally, while the current Jorus implementation realizes data parallel execution on cluster systems in a fully user transparent manner, we are also working on a cluster-implementation that results in combined data and task parallel execution [50].…”

Section: High-performance Distributed Multimedia Analysis With Jorusmentioning

confidence: 96%

Jungle Computing: Distributed Supercomputing Beyond Clusters, Grids, and Clouds

Seinstra

Maassen

Nieuwpoort

et al. 2011

Computer Communications and Networks

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In recent years, the application of high-performance and distributed computing in scientific practice has become increasingly wide-spread. Among the most widely available platforms to scientists are clusters, grids, and cloud systems. Such infrastructures currently are undergoing revolutionary change due to the integration of many-core technologies, providing orders-of-magnitude speed improvements for selected compute kernels. With high-performance and distributed computing systems thus becoming more heterogeneous and hierarchical, programming complexity is vastly increased. Further complexities arise because urgent desire for scalability, and issues including data distribution, software heterogeneity, and ad-hoc hardware availability, commonly force scientists into simultaneous use of multiple platforms (e.g. clusters, grids, and clouds used concurrently). A true computing jungle. In this chapter we explore the possibilities of enabling efficient and transparent use of Jungle Computing Systems in every-day scientific practice. To this end, we discuss the fundamental methodologies required for defining programming models that are tailored to the specific needs of scientific researchers. Importantly, we claim that many of these fundamental methodologies already exist today, as integrated in our Ibis high-performance distributed programming system. We also make a case for the urgent need for easy and efficient Jungle Computing in scientific practice, by exploring a set of state-of-the-art application domains. For one of these domains we present results obtained with Ibis on a real-world Jungle Computing System. The chapter concludes by exploring fundamental research questions to be investigated in the years to come.

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