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http://hdl.handle.net/1942/23912
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DC Field | Value | Language |
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dc.contributor.author | NEMETH, Balazs | - |
dc.contributor.author | HABER, Tom | - |
dc.contributor.author | Ashby, Thomas J. | - |
dc.contributor.author | LAMOTTE, Wim | - |
dc.date.accessioned | 2017-06-21T12:11:26Z | - |
dc.date.available | 2017-06-21T12:11:26Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | The 17th International Conference on Computational Science (ICCS 2017), Zürich, Switzerland, 12-14/06/2017 | - |
dc.identifier.uri | http://hdl.handle.net/1942/23912 | - |
dc.description.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. | - |
dc.description.sponsorship | Part of the work presented in this paper was funded by Johnson & Johnson. This project has received funding from the European Union’s Horizon 2020 Research and Innovation programme under Grant Agreement no. 671555. | - |
dc.language.iso | en | - |
dc.title | Improving Operational Intensity in Data Bound Markov Chain Monte Carlo | - |
dc.type | Conference Material | - |
local.bibliographicCitation.conferencedate | 12-14/06/2017 | - |
local.bibliographicCitation.conferencename | International Conference on Computational Science (ICCS 2017) | - |
local.bibliographicCitation.conferenceplace | Zurich, Switzerland | - |
local.bibliographicCitation.jcat | C2 | - |
local.type.refereed | Non-Refereed | - |
local.type.specified | Poster | - |
local.type.programme | H2020 | - |
local.relation.h2020 | 671555 | - |
item.contributor | NEMETH, Balazs | - |
item.contributor | HABER, Tom | - |
item.contributor | Ashby, Thomas J. | - |
item.contributor | LAMOTTE, Wim | - |
item.fullcitation | NEMETH, Balazs; HABER, Tom; Ashby, Thomas J. & LAMOTTE, Wim (2017) Improving Operational Intensity in Data Bound Markov Chain Monte Carlo. In: The 17th International Conference on Computational Science (ICCS 2017), Zürich, Switzerland, 12-14/06/2017. | - |
item.accessRights | Open Access | - |
item.fulltext | With Fulltext | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
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main.pdf | Conference material | 255.82 kB | Adobe PDF | View/Open |
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