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http://hdl.handle.net/1942/23912
Title: | Improving Operational Intensity in Data Bound Markov Chain Monte Carlo | Authors: | NEMETH, Balazs HABER, Tom Ashby, Thomas J. LAMOTTE, Wim |
Issue Date: | 2017 | Source: | The 17th International Conference on Computational Science (ICCS 2017), Zürich, Switzerland, 12-14/06/2017 | 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. | Document URI: | http://hdl.handle.net/1942/23912 | Category: | C2 | Type: | Conference Material |
Appears in Collections: | Research publications |
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File | Description | Size | Format | |
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main.pdf | Conference material | 255.82 kB | Adobe PDF | View/Open |
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