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J-GLOBAL ID:201902219512747738   Reference number:19A1929143

A Study of Synchronization Methods in Modern GPUs

最新のGPUの同期方法の研究
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Volume: 2019  Issue: HPC-170  Page: Vol.2019-HPC-170,No.21,1-6 (WEB ONLY)  Publication year: Jul. 17, 2019 
JST Material Number: U0451A  Document type: Proceedings
Article type: 原著論文  Country of issue: Japan (JPN)  Language: ENGLISH (EN)
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Control systems  ,  Special-purpose arithmetic and control units 
Reference (11):
  • C. Nvidia, “Programming guide,” 2019.
  • M. Harris et al., “Optimizing parallel reduction in cuda,” Nvidia developer technology, vol. 2, no. 4, p. 70, 2007.
  • S. Tokui, K. Oono, S. Hido, and J. Clayton, “Chainer: a next-generation open source framework for deep learning,” in Proceedings of workshop on machine learning systems (LearningSys) in the twenty-ninth annual conference on neural information processing systems (NIPS), vol. 5, pp. 1-6, 2015.
  • T. Sorensen, A. F. Donaldson, M. Batty, G. Gopalakrishnan, and Z. Rakamarić, “Portable inter-workgroup barrier synchronisation for gpus,” in ACM SIGPLAN Notices, vol. 51, pp. 39-58, ACM, 2016.
  • S. Xiao and W.-c. Feng, “Inter-block gpu communication via fast barrier synchronization,” in 2010 IEEE International Symposium on Parallel & Distributed Processing (IPDPS), pp. 1-12, IEEE, 2010.
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