Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/23913
Title: | Improving Operational Intensity in Data Bound Markov Chain Monte Carlo | Authors: | NEMETH, Balazs HABER, Tom Ashby, Thomas J. LAMOTTE, Wim |
Issue Date: | 2017 | Publisher: | Elsevier Science BV | Source: | Procedia Computer Science, 108(C), p. 2348-2352 (Art N° 205) | Series/Report: | Procedia Computer Science | Series/Report no.: | 108 | Abstract: | Typically, parallel algorithms are developed to leverage the processing power of multiple processors simultaneously speeding up overall execution. At the same time, discrepancy between \{DRAM\} bandwidth and microprocessor speed hinders reaching peak performance. This paper explores how operational intensity improves by performing useful computation during otherwise stalled cycles. While the proposed methodology is applicable to a wide variety of parallel algorithms, and at different scales, the concepts are demonstrated in the machine learning context. Performance improvements are shown for Bayesian logistic regression with a Markov chain Monte Carlo sampler, either with multiple chains or with multiple proposals, on a dense data set two orders of magnitude larger than the last level cache on contemporary systems. | Keywords: | operational intensity; MCMC; Bayesian logistic regression; HPC; Big Data | Document URI: | http://hdl.handle.net/1942/23913 | Link to publication/dataset: | http://www.sciencedirect.com/science/article/pii/S1877050917305252 | DOI: | 10.1016/j.procs.2017.05.024 | ISI #: | 000404959000249 | Rights: | © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the International Conference on Computational Science | Category: | C1 | Type: | Proceedings Paper | Validations: | ecoom 2018 |
Appears in Collections: | Research publications |
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1-s2.0-S1877050917305252-main.pdf | Published version | 389.25 kB | Adobe PDF | View/Open |
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