نتائج البحث - "Hans Pabst"

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دورية أكاديمية

Tensor Processing Primitives: A Programming Abstraction for Efficiency and Portability in Deep Learning and HPC Workloads

المؤلفون: Evangelos Georganas, Dhiraj Kalamkar, Sasikanth Avancha, Menachem Adelman, Deepti Aggarwal, Cristina Anderson, Alexander Breuer, Jeremy Bruestle, Narendra Chaudhary, Abhisek Kundu, Denise Kutnick, Frank Laub, Vasimuddin Md, Sanchit Misra, Ramanarayan Mohanty, Hans Pabst, Brian Retford, Barukh Ziv, Alexander Heinecke

المصدر: Frontiers in Applied Mathematics and Statistics, Vol 8 (2022)

مصطلحات موضوعية: deep learning, performance portability, programming abstraction, tensor processing, high productivity, high performance computing, Applied mathematics. Quantitative methods, T57-57.97, Probabilities. Mathematical statistics, QA273-280

وصف الملف: electronic resource

العلاقة: https://www.frontiersin.org/articles/10.3389/fams.2022.826269/fullTest; https://doaj.org/toc/2297-4687Test

الوصول الحر: https://doaj.org/article/0ca258dcbf0f493b97c2c2aef3829f43Test

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النص الكامل

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دورية أكاديمية

Machine Learning in High Energy Physics Community White Paper

المؤلفون: Kim Albertsson, Piero Altoe, Dustin Anderson, John Anderson, Michael Andrews, Juan Pedro Araque Espinosa, Adam Aurisano, Laurent Basara, Adrian Bevan, Wahid Bhimji, Daniele Bonacorsi, Bjorn Burkle, Paolo Calafiura, Mario Campanelli, Louis Capps, Federico Carminati, Stefano Carrazza, Yi-fan Chen, Taylor Childers, Yann Coadou, Elias Coniavitis, Kyle Cranmer, Claire David, Douglas Davis, Andrea De Simone, Javier Duarte, Martin Erdmann, Jonas Eschle, Amir Farbin, Matthew Feickert, Nuno Filipe Castro, Conor Fitzpatrick, Michele Floris, Alessandra Forti, Jordi Garra-Tico, Jochen Gemmler, Maria Girone, Paul Glaysher, Sergei Gleyzer, Vladimir Gligorov, Tobias Golling, Jonas Graw, Lindsey Gray, Dick Greenwood, Thomas Hacker, John Harvey, Benedikt Hegner, Lukas Heinrich, Ulrich Heintz, Ben Hooberman, Johannes Junggeburth, Michael Kagan, Meghan Kane, Konstantin Kanishchev, Przemysław Karpiński, Zahari Kassabov, Gaurav Kaul, Dorian Kcira, Thomas Keck, Alexei Klimentov, Jim Kowalkowski, Luke Kreczko, Alexander Kurepin, Rob Kutschke, Valentin Kuznetsov, Nicolas Köhler, Igor Lakomov, Kevin Lannon, Mario Lassnig, Antonio Limosani, Gilles Louppe, Aashrita Mangu, Pere Mato, Narain Meenakshi, Helge Meinhard, Dario Menasce, Lorenzo Moneta, Seth Moortgat, Mark Neubauer, Harvey Newman, Sydney Otten, Hans Pabst, Michela Paganini, Manfred Paulini, Gabriel Perdue, Uzziel Perez, Attilio Picazio, Jim Pivarski, Harrison Prosper, Fernanda Psihas, Alexander Radovic, Ryan Reece, Aurelius Rinkevicius, Eduardo Rodrigues, Jamal Rorie, David Rousseau, Aaron Sauers, Steven Schramm, Ariel Schwartzman, Horst Severini, Paul Seyfert, Filip Siroky, Konstantin Skazytkin, Mike Sokoloff, Graeme Stewart, Bob Stienen, Ian Stockdale, Giles Strong, Wei Sun, Savannah Thais, Karen Tomko, Eli Upfal, Emanuele Usai, Andrey Ustyuzhanin, Martin Vala, Justin Vasel, Sofia Vallecorsa, Mauro Verzetti, Xavier Vilasís-Cardona, Jean-Roch Vlimant, Ilija Vukotic, Sean-Jiun Wang, Gordon Watts, Michael Williams, Wenjing Wu, Stefan Wunsch, Kun Yang, Omar Zapata

المساهمون: K. Albertsson, P. Altoe, D. Anderson, J. Anderson, M. Andrew, J. Pedro Araque Espinosa, A. Aurisano, L. Basara, A. Bevan, W. Bhimji, D. Bonacorsi, B. Burkle, P. Calafiura, M. Campanelli, L. Capp, F. Carminati, S. Carrazza, Y. Chen, T. Childer, Y. Coadou, E. Coniaviti, K. Cranmer, C. David, D. Davi, A. De Simone, J. Duarte, M. Erdmann, J. Eschle, A. Farbin, M. Feickert, N. Filipe Castro, C. Fitzpatrick, M. Flori, A. Forti, J. Garra-Tico, J. Gemmler, M. Girone, P. Glaysher, S. Gleyzer, V. Gligorov, T. Golling, J. Graw, L. Gray, D. Greenwood, T. Hacker, J. Harvey, B. Hegner, L. Heinrich, U. Heintz, B. Hooberman, J. Junggeburth, M. Kagan, M. Kane, K. Kanishchev, P. Karpiński, Z. Kassabov, G. Kaul, D. Kcira, T. Keck, A. Klimentov, J. Kowalkowski, L. Kreczko, A. Kurepin, R. Kutschke, V. Kuznetsov, N. Köhler, I. Lakomov, K. Lannon, M. Lassnig, A. Limosani, G. Louppe, A. Mangu, P. Mato, N. Meenakshi, H. Meinhard, D. Menasce, L. Moneta, S. Moortgat, M. Neubauer, H. Newman, S. Otten, H. Pabst, M. Paganini, M. Paulini, G. Perdue, U. Perez, A. Picazio, J. Pivarski, H. Prosper, F. Psiha, A. Radovic, R. Reece, A. Rinkeviciu, E. Rodrigue, J. Rorie, D. Rousseau, A. Sauer, S. Schramm, A. Schwartzman, H. Severini

مصطلحات موضوعية: Settore FIS/02 - Fisica Teorica, Modelli e Metodi Matematici

العلاقة: numberofpages:28; http://hdl.handle.net/2434/735874Test; https://arxiv.org/abs/1807.02876Test

الإتاحة: http://hdl.handle.net/2434/735874Test
https://arxiv.org/abs/1807.02876Test

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Data_Sheet_1_Tensor Processing Primitives: A Programming Abstraction for Efficiency and Portability in Deep Learning and HPC Workloads.PDF

المؤلفون: Evangelos Georganas, Dhiraj Kalamkar, Sasikanth Avancha, Menachem Adelman, Deepti Aggarwal, Cristina Anderson, Alexander Breuer, Jeremy Bruestle, Narendra Chaudhary, Abhisek Kundu, Denise Kutnick, Frank Laub, Vasimuddin Md, Sanchit Misra, Ramanarayan Mohanty, Hans Pabst, Brian Retford, Barukh Ziv, Alexander Heinecke

مصطلحات موضوعية: Statistics, Computation Theory and Mathematics, Ordinary Differential Equations, Difference Equations and Dynamical Systems, Financial Mathematics, Applied Mathematics not elsewhere classified, Optimisation, Numerical and Computational Mathematics not elsewhere classified, Applied Statistics, Mathematical Physics not elsewhere classified, Numerical Computation, Computation Theory and Mathematics not elsewhere classified, deep learning, performance portability, programming abstraction, tensor processing, high productivity, high performance computing

العلاقة: https://figshare.com/articles/dataset/Data_Sheet_1_Tensor_Processing_Primitives_A_Programming_Abstraction_for_Efficiency_and_Portability_in_Deep_Learning_and_HPC_Workloads_PDF/19614360Test

الإتاحة: https://doi.org/10.3389/fams.2022.826269.s001Test
https://figshare.com/articles/dataset/Data_Sheet_1_Tensor_Processing_Primitives_A_Programming_Abstraction_for_Efficiency_and_Portability_in_Deep_Learning_and_HPC_Workloads_PDF/19614360Test

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Reduced Precision Strategies for Deep Learning: A High Energy Physics Generative Adversarial Network Use Case

المؤلفون: Dirk Krücker, Sofia Vallecorsa, Valeriu Codreanu, Hans Pabst, Vikram A. Saletore, Kerstin Borras, Adel Chaibi, Florian Rehm

المصدر: Setúbal, Portugal : SCITEPRESS-Science and Technology Publications 251-258 (2021). doi:10.5220/0010245002510258
ICPRAM 2021 : proceedings of the 10th International Conference on Pattern Recognition Applications and Methods : online streaming, February 4-6, 2021 / sponsored by: INSTICC-Institute for Systems and Technologies of Information, Control and Communication ; edited by Maria De Marsico, Gabriella Sanniti di Baja, Ana Fred
ICPRAM 2021 : proceedings of the 10th International Conference on Pattern Recognition Applications and Methods : online streaming, February 4-6, 2021 / sponsored by: INSTICC-Institute for Systems and Technologies of Information, Control and Communication ; edited by Maria De Marsico, Gabriella Sanniti di Baja, Ana Fred10. International Conference on Pattern Recognition Applications and Methods, ICPRAM 2021, online, 2021-02-04-2021-02-06

مصطلحات موضوعية: FOS: Computer and information sciences, Computer Science - Artificial Intelligence, neural network, Monte Carlo method, Other Fields of Physics, FOS: Physical sciences, High Energy Physics - Experiment, High Energy Physics - Experiment (hep-ex), benchmark, numerical methods, calorimeter, numerical calculations, Artificial neural network, business.industry, Deep learning, Memory bandwidth, artificial intelligence, interaction [particle], Computing and Computers, particle: interaction, Artificial Intelligence (cs.AI), Computer engineering, Physics - Data Analysis, Statistics and Probability, network, Memory footprint, Benchmark (computing), Subatomic particle, Artificial intelligence, quantization, business, Data Analysis, Statistics and Probability (physics.data-an), Particle Physics - Experiment, Energy (signal processing), performance