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PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed Architectures.

M. Patwary, N. Satish, N. Sundaram, J. Liu, P. Sadowski, E. Racah, S. Byna, C. Tull, W. Bhimji, Prabhat, and P. Dubey. IPDPS, page 494-503. IEEE Computer Society, (2016)

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Matrix factorizations at scale: A comparison of scientific data analytics in spark and C+MPI using three case studies.A. Gittens, A. Devarakonda, E. Racah, M. Ringenburg, L. Gerhardt, J. Kottalam, J. Liu, K. Maschhoff, S. Canon, J. Chhugani and 7 other author(s). BigData, page 204-213. IEEE Computer Society, (2016)Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC.W. Bhimji, S. Farrell, T. Kurth, M. Paganini, Prabhat, and E. Racah. CoRR, (2017)Supervise Thyself: Examining Self-Supervised Representations in Interactive Environments.E. Racah, and C. Pal. CoRR, (2019)A Multi-Platform Evaluation of the Randomized CX Low-Rank Matrix Factorization in Spark.A. Gittens, J. Kottalam, J. Yang, M. Ringenburg, J. Chhugani, E. Racah, M. Singh, Y. Yao, C. Fischer, O. Rübel and 5 other author(s). IPDPS Workshops, page 1403-1412. IEEE Computer Society, (2016)Revealing Fundamental Physics from the Daya Bay Neutrino Experiment Using Deep Neural Networks.E. Racah, S. Ko, P. Sadowski, W. Bhimji, C. Tull, S. Oh, P. Baldi, and Prabhat. ICMLA, page 892-897. IEEE Computer Society, (2016)Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate Datasets.Y. Liu, E. Racah, Prabhat, J. Correa, A. Khosrowshahi, D. Lavers, K. Kunkel, M. Wehner, and W. Collins. CoRR, (2016)Deep learning at 15PF: supervised and semi-supervised classification for scientific data.T. Kurth, J. Zhang, N. Satish, E. Racah, I. Mitliagkas, M. Patwary, T. Malas, N. Sundaram, W. Bhimji, M. Smorkalov and 5 other author(s). SC, page 7:1-7:11. ACM, (2017)Semi-Supervised Detection of Extreme Weather Events in Large Climate Datasets.E. Racah, C. Beckham, T. Maharaj, Prabhat, and C. Pal. CoRR, (2016)ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events.E. Racah, C. Beckham, T. Maharaj, S. Kahou, Prabhat, and C. Pal. NIPS, page 3402-3413. (2017)PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed Architectures.M. Patwary, N. Satish, N. Sundaram, J. Liu, P. Sadowski, E. Racah, S. Byna, C. Tull, W. Bhimji, Prabhat and 1 other author(s). IPDPS, page 494-503. IEEE Computer Society, (2016)

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PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed Architectures.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed Architectures.

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PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed Architectures.