Zobrazeno 1 - 9 of 9 pro vyhledávání: '"Evan Racah"'
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Deep Learning at 15PF: Supervised and Semi-Supervised Classification for Scientific Data
Autor: Mikhail E. Smorkalov, Md. Mostofa Ali Patwary, Evan Racah, Srinivas Sridharan, Prabhat, Wahid Bhimji, Thorsten Kurth, Nadathur Satish, Jian Zhang, Mikhail Shiryaev, Tareq M. Malas, Pradeep Dubey, Narayanan Sundaram, Jack Deslippe, Ioannis Mitliagkas
Publikováno v: SC
This paper presents the first, 15-PetaFLOP Deep Learning system for solving scientific pattern classification problems on contemporary HPC architectures. We develop supervised convolutional architectures for discriminating signals in high-energy phys
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3bf2d8cbbe6124c4a13adabe44da076a
http://arxiv.org/abs/1708.05256
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3
Exploring Raw HEP Data using Deep Neural Networks at NERSC
Autor: Wahid Bhimji, Evan Racah, Craig Tull, Peter Sadowski, Seyoon Ko, Sang-Yun Oh, Prabhat
Publikováno v: Proceedings of 38th International Conference on High Energy Physics — PoS(ICHEP2016).
High Energy Physics has made use of artificial neural networks for some time. Recently, however, there has been considerable development outside the HEP community, particularly in deep neural networks for the purposes of image recognition. We describ
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::74513e97b136eacfeb2149acdc177991
https://doi.org/10.22323/1.282.0889
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4
Revealing fundamental physics from the Daya Bay Neutrino Experiment using deep neural networks
Autor: Evan Racah, Seyoon Ko, Peter Sadowski, Wahid Bhimji, Craig Tull, Sang-Yun Oh, Pierre Baldi, null Prabhat
Publikováno v: Racah, E; Ko, S; Sadowski, P; Bhimji, W; Tull, C; Oh, SY; et al.(2017). Revealing fundamental physics from the Daya Bay Neutrino Experiment using deep neural networks. Proceedings-2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016, 892-897. doi: 10.1109/ICMLA.2016.151. Lawrence Berkeley National Laboratory: Retrieved from: http://www.escholarship.org/uc/item/9dk2b6fm
© 2016 IEEE. Experiments in particle physics produce enormous quantities of data that must be analyzed and interpreted by teams of physicists. This analysis is often exploratory, where scientists are unable to enumerate the possible types of signal
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9287914c60a93244b6218004cccb371f
https://escholarship.org/uc/item/9dk2b6fm
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5
Matrix factorizations at scale: A comparison of scientific data analytics in spark and C+MPI using three case studies
Autor: Evan Racah, Michael W. Mahoney, Jatin Chhugani, Jim Harrell, Jey Kottalam, Jialin Liu, Shane Canon, Pramod Sharma, Michael F. Ringenburg, James Demmel, Jiyan Yang, Prabhat, Aditya Devarakonda, Kristyn Maschhoff, Alex Gittens, L. Gerhardt, Venkat Krishnamurthy
Publikováno v: IEEE BigData
Gittens, Alex; Devarakonda, Aditya; Racah, Evan; Ringenburg, Michael; Gerhardt, Lisa; Kottalam, Jey; et al.(2016). Matrix Factorizations at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies:. Lawrence Berkeley National Laboratory: Lawrence Berkeley National Laboratory. Retrieved from: http://www.escholarship.org/uc/item/13q8t1hg
Gittens, Alex; Devarakonda, Aditya; Racah, Evan; Ringenburg, Michael; Gerhardt, Lisa; Kottaalam, Jey; et al.(2016). Matrix Factorizations at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies:. Lawrence Berkeley National Laboratory: Lawrence Berkeley National Laboratory. Retrieved from: http://www.escholarship.org/uc/item/2h15p99d
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: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7993a6588578f3c2f032af663634d3fa
https://doi.org/10.1109/bigdata.2016.7840606
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6
A Multi-Platform Evaluation of the Randomized CX Low-Rank Matrix Factorization in Spark
Autor: Venkat Krishnamurthy, Evan Racah, Michael W. Mahoney, Norman G. Lewis, Prabhat, Jiyan Yang, Jatin Chhugani, Curt R. Fischer, Jey Kottalam, Benjamin P. Bowen, Mohitdeep Singh, Yushu Yao, Michael F. Ringenburg, Oliver Ruebel, Alex Gittens
Publikováno v: IPDPS Workshops
Gittens, Alex; Kottalam, Jey; Yang, Jiyan; Ringenburg, Michael, F.; Chhugani, Jatin; Racah, Evan; et al.(2016). A multi-platform evaluation of the randomized CX low-rank matrix factorization in Spark:. Lawrence Berkeley National Laboratory: Lawrence Berkeley National Laboratory. Retrieved from: http://www.escholarship.org/uc/item/6jh4m35v
We investigate the performance and scalability of the randomized CX low-rank matrix factorization and demonstrate its applicability through the analysis of a 1TB mass spectrometry imaging (MSI) dataset, using Apache Spark on an Amazon EC2 cluster, a
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::27791ae8905040f20ad2fd4d9c2aa794
https://doi.org/10.1109/ipdpsw.2016.114
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7
Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC
Autor: Michela Paganini, Prabhat, Evan Racah, Thorsten Kurth, Steven Farrell, Wahid Bhimji
Publikováno v: Journal of Physics: Conference Series. 1085:042034
There has been considerable recent activity applying deep convolutional neural nets (CNNs) to data from particle physics experiments. Current approaches on ATLAS/CMS have largely focussed on a subset of the calorimeter, and for identifying objects or
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::656dbacd316e0f8c961f7f88d386d4be
https://doi.org/10.1088/1742-6596/1085/4/042034
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8
Deep learning with raw data from Daya Bay
Autor: Prabhat, D Dwyer, S Kohn, Craig Tull, Wahid Bhimji, Evan Racah
Publikováno v: Journal of Physics: Conference Series. 898:072050
The Daya Bay experiment uses reactor antineutrino disappearance to measure the θ 13 neutrino oscillation parameter. In this proceeding, the convolutional autoencoder machine learning technique is tested against a well-understood uncorrelated acciden
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::f1a7034be907c42b1055ec26473101f6
https://doi.org/10.1088/1742-6596/898/7/072050
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9
Akademický článek
Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC.
Autor: Wahid Bhimji, Steven Andrew Farrell, Thorsten Kurth, Michela Paganini, Prabhat, Evan Racah
Publikováno v: Journal of Physics: Conference Series; Oct2018, Vol. 1085 Issue 4, p1-1, 1p
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