Applications of heterogeneous computing in computational and simulation science

Domanski, Luke; Bednarz, Tomasz; Gureyev, T. E.; Murray, Lawrence M.; Huang, Bevan Emma; Nesterets, Yakov; Thompson, Darren; Jones, Emlyn; Cavanagh, Colin; Wang, Dadong; Vallotton, Pascal; Sun, Changming; Khassapov, Alex; Stevenson, A. W.; Mayo, Sheridan C; Morell, Matthew K.; George, Andrew W.; Taylor, John A.

doi:10.1504/ijcse.2013.055356

Cited by 2 publications

(2 citation statements)

References 24 publications

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“…Domanski et al [19] developed a cluster Web services (CWS) framework capable of taking advantage of massively parallel technologies composed of a PC-cluster 4 and GPUs. 5 This framework facilitated communication between the client and server through the Internet in order to balance and distribute the computational load.…”

Section: Image Reconstructionmentioning

confidence: 99%

“…However, in view of the popularity of graphics processing units (GPUs) and the adoption of parallel programming methods, a number of research areas can advance significantly without the need for major investment in computational systems. Examples of these areas include: scientific simulation [19], life sciences [91], statistical modeling [91], emerging data-intensive applications [91], electronic design automation [91], ray tracing and rendering [96], computer vision [23], signal processing [23,91], and medical image processing and analysis [15,29,88].…”

Section: Introductionmentioning

confidence: 99%

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Techniques of medical image processing and analysis accelerated by high-performance computing: a systematic literature review

Gulo

Sementille

Tavares

2017

J Real-Time Image Proc

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Techniques of medical image processing and analysis play a crucial role in many clinical scenarios, including in diagnosis and treatment planning. However, immense quantities of data and high complexity of the algorithms often used are computationally demanding. As a result, there now exists a wide range of techniques of medical image processing and analysis that require the application of high-performance computing solutions in order to reduce the required runtime. The main purpose of this review is to provide a comprehensive reference source of techniques of medical image processing and analysis that have been accelerated by high-performance computing solutions. With this in mind, the articles available in the Scopus and Web of Science electronic repositories were searched. Subsequently, the most relevant articles found were individually analyzed in order to identify: (a) the metrics used to evaluate computing performance, (b) the high-performance computing solution used, (c) the parallel design adopted, and (d) the task of medical image processing and analysis involved. Hence, the techniques of medical image processing and analysis found were identified, reviewed, and discussed, particularly in terms of computational performance. Consequently, the techniques reviewed herein present the progress made so far in reducing the computational runtime involved, and the difficulties and challenges that remain to be overcome.

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Section: Image Reconstructionmentioning

confidence: 99%