Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Ringenburg, Michael"'
Autor:
Rothauge, Kai, Ayyalasomayajula, Haripriya, Maschhoff, Kristyn J., Ringenburg, Michael, Mahoney, Michael W.
Alchemist is a system that allows Apache Spark to achieve better performance by interfacing with HPC libraries for large-scale distributed computations. In this paper, we highlight some recent developments in Alchemist that are of interest to Cray us
Externí odkaz:
http://arxiv.org/abs/1910.01354
Autor:
Gittens, Alex, Rothauge, Kai, Wang, Shusen, Mahoney, Michael W., Kottalam, Jey, Gerhardt, Lisa, Prabhat, Ringenburg, Michael, Maschhoff, Kristyn
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
Externí odkaz:
http://arxiv.org/abs/1806.01270
Autor:
Gittens, Alex, Rothauge, Kai, Wang, Shusen, Mahoney, Michael W., Gerhardt, Lisa, Prabhat, Kottalam, Jey, Ringenburg, Michael, Maschhoff, Kristyn
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:
http://arxiv.org/abs/1805.11800
Autor:
Gittens, Alex, Devarakonda, Aditya, Racah, Evan, Ringenburg, Michael, Gerhardt, Lisa, Kottalam, Jey, Liu, Jialin, Maschhoff, Kristyn, Canon, Shane, Chhugani, Jatin, Sharma, Pramod, Yang, Jiyan, Demmel, James, Harrell, Jim, Krishnamurthy, Venkat, Mahoney, Michael W., Prabhat
We explore the trade-offs of performing linear algebra using Apache Spark, compared to traditional C and MPI implementations on HPC platforms. Spark is designed for data analytics on cluster computing platforms with access to local disks and is optim
Externí odkaz:
http://arxiv.org/abs/1607.01335
Akademický článek
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Autor:
Mathuriya, Amrita, Bard, Deborah, Mendygral, Peter, Meadows, Lawrence, Arnemann, James, Shao, Lei, He, Siyu, Karna, Tuomas, Moise, Diana, Pennycook, Simon J, Maschhoff, Kristyn, Sewall, Jason, Kumar, Nalini, Ho, Shirley, Ringenburg, Michael F, Prabhat, Lee, Victor, Press, IEEE
Deep learning is a promising tool to determine the physical model that describes our universe. To handle the considerable computational cost of this problem, we present CosmoFlow: a highly scalable deep learning application built on top of the Tensor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______325::9d16bde73418c67ef16ed0d5b42ecfa8
https://escholarship.org/uc/item/6g4145rq
https://escholarship.org/uc/item/6g4145rq
Akademický článek
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Publikováno v:
Concurrency & Computation: Practice & Experience; 10/25/2020, Vol. 32 Issue 20, p1-12, 12p
Autor:
Gittens, Alex, Kottalam, Jey, Yang, Jiyan, Ringenburg, Michael F., Chhugani, Jatin, Racah, Evan, Singh, Mohitdeep, Yao, Yushu, Fischer, Curt, Ruebel, Oliver, Bowen, Benjamin, Lewis, Norman G., Mahoney, Michael W., Krishnamurthy, Venkat, Prabhat
Publikováno v:
2016 IEEE International Parallel & Distributed Processing Symposium Workshops (IPDPSW); 2016, p1403-1412, 10p
Akademický článek
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