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pro vyhledávání: '"Kai Rothauge"'
Autor:
Michael F. Ringenburg, Kristyn Maschhoff, Alex Gittens, Kai Rothauge, Michael W. Mahoney, L. Gerhardt, Shusen Wang, Prabhat, Jey Kottalam
Publikováno v:
Concurrency and Computation: Practice and Experience.
The Apache Spark framework for distributed computation is popular in the data analytics community due to its ease of use, but its MapReduce-style programming model can incur significant overheads when performing computations that do not map directly
Autor:
Kai Rothauge, Michael W. Mahoney, Jey Kottalam, L. Gerhardt, Michael F. Ringenburg, Alex Gittens, Kristyn Maschhoff, Prabhat, Shusen Wang
Publikováno v:
KDD
Apache Spark is a popular system aimed at the analysis of large data sets, but recent studies have shown that certain computations---in particular, many linear algebra computations that are the basis for solving common machine learning problems---are
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3707e0b9de495c56b6008d091d266704
http://arxiv.org/abs/1805.11800
http://arxiv.org/abs/1805.11800
Publikováno v:
Applied Mathematical Modelling. 38:5515-5534
Flooding resulting from collapse of a dam is a highly destructive event. It is important to accurately predict the flow behaviour so that potential mitigation strategies can be investigated for disaster management planning. The meshless SPH method ha
Publikováno v:
International Journal of Image and Data Fusion. 1:337-357
Computational modelling of extreme rock and fluid flow events, such as landslides and dam collapses, can provide increased understanding of their post-initiation course. This can provide valuable insight into opportunities for designing mitigation st