Relevant bibliographies by topics / GPU Systems

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  1. Journal articles

Academic literature on the topic 'GPU Systems'

Author: Grafiati
Published: 6 September 2023
Last updated: 7 September 2023

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Journal articles on the topic "GPU Systems"

1

Jararweh, Yaser, Moath Jarrah, and Abdelkader Bousselham. "GPU Scaling." International Journal of Information Technology and Web Engineering 9, no. 4 (2014): 13–23. http://dx.doi.org/10.4018/ijitwe.2014100102.

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Current state-of-the-art GPU-based systems offer unprecedented performance advantages through accelerating the most compute-intensive portions of applications by an order of magnitude. GPU computing presents a viable solution for the ever-increasing complexities in applications and the growing demands for immense computational resources. In this paper the authors investigate different platforms of GPU-based systems, starting from the Personal Supercomputing (PSC) to cloud-based GPU systems. The authors explore and evaluate the GPU-based platforms and the authors present a comparison discussion against the conventional high performance cluster-based computing systems. The authors' evaluation shows potential advantages of using GPU-based systems for high performance computing applications while meeting different scaling granularities.
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2

Dematte, L., and D. Prandi. "GPU computing for systems biology." Briefings in Bioinformatics 11, no. 3 (2010): 323–33. http://dx.doi.org/10.1093/bib/bbq006.

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3

Ban, Zhihua, Jianguo Liu, and Jeremy Fouriaux. "GMMSP on GPU." Journal of Real-Time Image Processing 17, no. 2 (2018): 245–57. http://dx.doi.org/10.1007/s11554-018-0762-3.

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4

Georgii, Joachim, and Rüdiger Westermann. "Mass-spring systems on the GPU." Simulation Modelling Practice and Theory 13, no. 8 (2005): 693–702. http://dx.doi.org/10.1016/j.simpat.2005.08.004.

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5

Huynh, Huynh Phung, Andrei Hagiescu, Ong Zhong Liang, Weng-Fai Wong, and Rick Siow Mong Goh. "Mapping Streaming Applications onto GPU Systems." IEEE Transactions on Parallel and Distributed Systems 25, no. 9 (2014): 2374–85. http://dx.doi.org/10.1109/tpds.2013.195.

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6

Deniz, Etem, and Alper Sen. "MINIME-GPU." ACM Transactions on Architecture and Code Optimization 12, no. 4 (2016): 1–25. http://dx.doi.org/10.1145/2818693.

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7

Braak, Gert-Jan Van Den, and Henk Corporaal. "R-GPU." ACM Transactions on Architecture and Code Optimization 13, no. 1 (2016): 1–24. http://dx.doi.org/10.1145/2890506.

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8

INO, Fumihiko, Shinta NAKAGAWA, and Kenichi HAGIHARA. "GPU-Chariot: A Programming Framework for Stream Applications Running on Multi-GPU Systems." IEICE Transactions on Information and Systems E96.D, no. 12 (2013): 2604–16. http://dx.doi.org/10.1587/transinf.e96.d.2604.

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9

Rosenfeld, Viktor, Sebastian Breß, and Volker Markl. "Query Processing on Heterogeneous CPU/GPU Systems." ACM Computing Surveys 55, no. 1 (2023): 1–38. http://dx.doi.org/10.1145/3485126.

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Due to their high computational power and internal memory bandwidth, graphic processing units (GPUs) have been extensively studied by the database systems research community. A heterogeneous query processing system that employs CPUs and GPUs at the same time has to solve many challenges, including how to distribute the workload on processors with different capabilities; how to overcome the data transfer bottleneck; and how to support implementations for multiple processors efficiently. In this survey we devise a classification scheme to categorize techniques developed to address these challenges. Based on this scheme, we categorize query processing systems on heterogeneous CPU/GPU systems and identify open research problems.
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10

Besozzi, Daniela, Giulio Caravagna, Paolo Cazzaniga, Marco Nobile, Dario Pescini, and Alessandro Re. "GPU-powered Simulation Methodologies for Biological Systems." Electronic Proceedings in Theoretical Computer Science 130 (September 30, 2013): 87–91. http://dx.doi.org/10.4204/eptcs.130.14.

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