Biomedical image analysis on a cooperative cluster of GPUs and multicores

Hartley, Timothy D. R.; Çatalyürek, Ümit V.; Ruiz, Alberto; Igual, Francisco D.; Mayo, Rafael; Ujaldón, Manuel

doi:10.1145/1375527.1375533

Cited by 67 publications

(36 citation statements)

References 16 publications

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“…The last of these applications is a real biomedical image analysis application [11]. The input to the overall application is a tissue slide image digitized at high resolution.…”

Section: Application Experimentsmentioning

confidence: 99%

A component-based framework for the Cell Broadband Engine

Hartley

Çatalyürek

2009

2009 IEEE International Symposium on Parallel &Amp; Distributed Processing

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With the increasing trend of microprocessor manufacturers to rely on parallelism to increase their products' performance, there is an associated increasing need for simple techniques to leverage this hardware parallelism for good application performance. Unfortunately, many application developers do not have the benefit of long experience in programming parallel and distributed systems. While the filter-stream programming paradigm helps bridge the gap between developers of scientific applications and the performance they need, current and future high-performance multicore processor designs do not have a filter-stream programming library available. This work aims to fill that gap in the software world. This initial DataCutter-Lite implementation defines a powerful, but simple abstraction for carrying out complex computations in a filter-stream model. Additionally, the initial implementation shows that complex architectures such as the Cell Broadband Engine Architecture can make use of the filter-stream model, and give good application performance when doing so.

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“…The last of these applications is a real biomedical image analysis application [11]. The input to the overall application is a tissue slide image digitized at high resolution.…”

Section: Application Experimentsmentioning

confidence: 99%

A component-based framework for the Cell Broadband Engine

Hartley

Çatalyürek

2009

2009 IEEE International Symposium on Parallel &Amp; Distributed Processing

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“…In DataCutter, the computations are carried by independent computing elements, called filters, that have different responsibilities and operate on data passing through them. DataCutter follows the component-based programming paradigm which has been used to describe and implement complex applications [11,12,13,29] by way of components -distinct tasks with well-defined interfaces. This is also known as the filter-stream programming model [3] (a specific implementation of the dataflow programming model).…”

Section: Datacuttermentioning

confidence: 99%

Incremental closeness centrality in distributed memory

et al. 2015

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Networks are commonly used to model traffic patterns, social interactions, or web pages. The vertices in a network do not possess the same characteristics: some vertices are naturally more connected and some vertices can be more important. Closeness centrality (CC) is a global metric that quantifies how important is a given vertex in the network. When the network is dynamic and keeps changing, the relative importance of the vertices also changes.The best known algorithm to compute the CC scores makes it impractical to recompute them from scratch after each modification. In this paper, we propose Streamer, a distributed memory framework for incrementally maintaining the closeness centrality scores of a network upon changes. It leverages pipelined, replicated parallelism, and SpMM-based BFSs, and it takes NUMA effects into account. It makes maintaining the Closeness Centrality values of real-life networks with millions of interactions significantly faster and obtains almost linear speedups on a 64 nodes 8 threads/node cluster.

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“…However, the CUDA kernels of such libraries can be reused in the presented approach. In [6] CUDA was integrated in a grid computing framework which is built on top of the DataCutter middleware. Since the DataCutter middleware focuses on processing a large number of completely independent tasks, it is not suitable for multimedia processing.…”

Section: Related Workmentioning

confidence: 99%

Stream Processing on GPUs Using Distributed Multimedia Middleware

Repplinger

Slusallek

2010

Parallel Processing and Applied Mathematics

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Abstract. Available GPUs provide increasingly more processing power especially for multimedia and digital signal processing. Despite the tremendous progress in hardware and thus processing power, there are and always will be applications that require using multiple GPUs either running inside the same machine or distributed in the network due to computational intensive processing algorithms. Existing solutions for developing applications for GPUs still require a lot of hand-optimization when using multiple GPUs inside the same machine and provide in general no support for using remote GPUs distributed in the network. In this paper we address this problem and show that an open distributed multimedia middleware, like the Network-Integrated Multimedia Middleware (NMM), is able (1) to seamlessly integrate processing components using GPUs while completely hiding GPU specific issues from the application developer, (2) to transparently combine processing components using GPUs or CPUs, and (3) to transparently use local and remote GPUs for distributed processing.

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Biomedical image analysis on a cooperative cluster of GPUs and multicores

Cited by 67 publications

References 16 publications

A component-based framework for the Cell Broadband Engine

A component-based framework for the Cell Broadband Engine

Incremental closeness centrality in distributed memory

Stream Processing on GPUs Using Distributed Multimedia Middleware

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