Efficient implementation of a portable parallel programming model for image processing

Morrow, Philip; Crookes, Danny; Brown, Tony N.; McAleese, G.; Roantree, D. K.; Spence, Ivor

doi:10.1002/(sici)1096-9128(199909)11:11<671::aid-cpe450>3.0.co;2-6

Cited by 13 publications

(9 citation statements)

References 20 publications

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“…Apart from the obvious differences between the Java and C++ languages, the Jorus and Parallel-Horus APIs are identical to that of a popular sequential programming system, called Horus [26]. Similar to other frameworks [34], Horus recognizes that a small set of algorithmic patterns can be identified that covers the bulk of all commonly applied multimedia computing functionality.…”

Section: High-performance Distributed Multimedia Analysis With Jorusmentioning

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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Section: High-performance Distributed Multimedia Analysis With Jorusmentioning

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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“…The library's API is made identical to that of an existing sequential library: Horus [9]. Similar to other frameworks [3], Horus recognizes that a small set of algorithmic patterns can be identified that covers the bulk of all commonly applied functionality.…”

Section: Parallel-horusmentioning

confidence: 99%

“…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%

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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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“…The library's API is made identical to that of an existing sequential library: Horus [7]. Similar to other frameworks [8], Horus recognizes that a small set of algorithmic patterns can be identified that covers the bulk of all multimedia-oriented functionality.…”

Section: Parallel-horusmentioning

confidence: 99%

Adaptive Parallel Householder Bidiagonalization

Liu

Seinstra²

2009

Lecture Notes in Computer Science

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Abstract.With the increasing use of large image and video archives and high-resolution multimedia data streams in many of today's research and application areas, there is a growing need for multimedia-oriented high-performance computing. As a consequence, a need for algorithms, methodologies, and tools that can serve as support in the (automatic) parallelization of multimedia applications is rapidly emerging.This paper discusses the parallelization of Householder bidiagonalization, a matrix factorization method which is an integral part of full Singular Value Decomposition (SVD) -an important algorithm for many multimedia problems. Householder bidiagonalization is hard to parallelize efficiently because the total number of matrix elements taking part in the calculations reduces during runtime. To overcome the growing negative performance impact of load imbalances and overprovisioning of compute resources, we apply adaptive runtime techniques of periodic matrix remapping and process reduction for improved performance. Results show that our adaptive parallel execution approach provides a significant improvement in efficiency, even when applying a set of compute resources which is (initially) very large.

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Efficient implementation of a portable parallel programming model for image processing

Cited by 13 publications

References 20 publications

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

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

Towards User Transparent Parallel Multimedia Computing on GPU-Clusters

Adaptive Parallel Householder Bidiagonalization

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