Commodity cluster-based parallel processing of hyperspectral imagery

Valencia, David; Plaza, Javier; Martı́nez, Pablo

doi:10.1016/j.jpdc.2005.10.001

Cited by 176 publications

(124 citation statements)

References 20 publications

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“…Update the MEI at each pixel using the SAM between the maximum and the minimum. As shown in previous work [2], computational complexity is ( )…”

Section: Morphological Algorithmmentioning

confidence: 68%

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Design and Implementation of a Parallel Heterogeneous Algorithm for Hyperspectral Image Analysis Using HeteroMPI

Valencia

Lastovetsky

2006

2006 Fifth International Symposium on Parallel and Distributed Computing

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“…Update the MEI at each pixel using the SAM between the maximum and the minimum. As shown in previous work [2], computational complexity is ( )…”

Section: Morphological Algorithmmentioning

confidence: 68%

“…1). This imager is able to continuously produce snapshot image cubes of tens or even hundreds of kilometers long, each of them with hundreds of MB in size, and this explosion in the amount of collected information has rapidly introduced new processing challenges [2].…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of a Parallel Heterogeneous Algorithm for Hyperspectral Image Analysis Using HeteroMPI

Valencia

Lastovetsky

2006

2006 Fifth International Symposium on Parallel and Distributed Computing

Self Cite

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“…Given the huge computational demands, parallel and distributed solutions are essential, at all levels of granularity. As a result, in the last decade the use of compute clusters for applications in remote sensing has become commonplace [38]. These approaches are proven beneficial for a diversity of problems, including target detection and classification [35], and automatic spectral unmixing and endmember extraction [37].…”

Section: Remote Sensingmentioning

confidence: 99%

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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“…Worse, with the integration of many-core technologies (e.g., GPUs), programming complexity is increased even further. As most MMCA researchers are not also HPC or many-core computing experts, there is a need for user transparent programming models and tools [2][3][4][5][6] that can assist in creating efficient parallel and hierarchical MMCA applications. Ideally, such tools require little or no extra effort compared to traditional (sequential) MMCA tools, and lead to efficient execution in most application scenarios.…”

Section: Introductionmentioning

confidence: 99%

“…Existing user transparent programming tools are based on a software library of pre-parallelized compute kernels that cover the bulk of all commonly applied MMCA functionality [2][3][4][5][6][7]. These tools, however, all aim at data parallel execution on traditional clusters, and do not incorporate the use of many-cores.…”

Section: Introductionmentioning

confidence: 99%

Towards User Transparent Parallel Multimedia Computing on GPU-Clusters

Werkhoven

Maassen

Seinstra

2011

Computer Architecture

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Abstract. 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.

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Commodity cluster-based parallel processing of hyperspectral imagery

Cited by 176 publications

References 20 publications

Design and Implementation of a Parallel Heterogeneous Algorithm for Hyperspectral Image Analysis Using HeteroMPI

Design and Implementation of a Parallel Heterogeneous Algorithm for Hyperspectral Image Analysis Using HeteroMPI

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

Towards User Transparent Parallel Multimedia Computing on GPU-Clusters

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