Hans Pabst

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 20

Top Topics

High Energy Physics

Molecular Dynamics

Top Venues

Publications

Renato Golin, Lorenzo Chelini, Adam Siemieniuk, Kavitha Madhu, Niranjan Hasabnis, Hans Pabst, Evangelos Georganas, Alexander Heinecke
Towards a high-performance AI compiler with upstream MLIR. CoRR (2024)
Evangelos Georganas, Dhiraj D. Kalamkar, Kirill Voronin, Abhisek Kundu, Antonio Noack, Hans Pabst, Alexander Breuer, Alexander Heinecke
Harnessing Deep Learning and HPC Kernels via High-Level Loop and Tensor Abstractions on CPU Architectures. IPDPS (2024)
Evangelos Georganas, Dhiraj D. Kalamkar, Kirill Voronin, Antonio Noack, Hans Pabst, Alexander Breuer, Alexander Heinecke
Harnessing Deep Learning and HPC Kernels via High-Level Loop and Tensor Abstractions on CPU Architectures. CoRR (2023)
Robert Schade, Tobias Kenter, Hossam Elgabarty, Michael Lass, Ole Schütt, Alfio Lazzaro, Hans Pabst, Stephan Mohr, Jürg Hutter, Thomas D. Kühne, Christian Plessl
molecular dynamics simulations with hundreds of millions of atoms. Parallel Comput. 111 (2022)
Evangelos Georganas, Dhiraj D. 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
Tensor Processing Primitives: A Programming Abstraction for Efficiency and Portability in Deep Learning and HPC Workloads. Frontiers Appl. Math. Stat. 8 (2022)
Robert Schade, Tobias Kenter, Hossam Elgabarty, Michael Lass, Ole Schütt, Alfio Lazzaro, Hans Pabst, Stephan Mohr, Jürg Hutter, Thomas D. Kühne, Christian Plessl
Enabling Electronic Structure-Based Ab-Initio Molecular Dynamics Simulations with Hundreds of Millions of Atoms. CoRR (2021)
Florian Rehm, Sofia Vallecorsa, Vikram A. Saletore, Hans Pabst, Adel Chaibi, Valeriu Codreanu, Kerstin Borras, Dirk Krücker
Reduced Precision Strategies for Deep Learning: A High Energy Physics Generative Adversarial Network Use Case. CoRR (2021)
Evangelos Georganas, Dhiraj D. Kalamkar, Sasikanth Avancha, Menachem Adelman, Cristina Anderson, Alexander Breuer, Narendra Chaudhary, Abhisek Kundu, Vasimuddin Md, Sanchit Misra, Ramanarayan Mohanty, Hans Pabst, Barukh Ziv, Alexander Heinecke
Tensor Processing Primitives: A Programming Abstraction for Efficiency and Portability in Deep Learning Workloads. CoRR (2021)
Evangelos Georganas, Dhiraj D. Kalamkar, Sasikanth Avancha, Menachem Adelman, Cristina Anderson, Alexander Breuer, Jeremy Bruestle, Narendra Chaudhary, Abhisek Kundu, Denise Kutnick, Frank Laub, Vasimuddin Md, Sanchit Misra, Ramanarayan Mohanty, Hans Pabst, Barukh Ziv, Alexander Heinecke
Tensor processing primitives: a programming abstraction for efficiency and portability in deep learning workloads. SC (2021)
Florian Rehm, Sofia Vallecorsa, Vikram A. Saletore, Hans Pabst, Adel Chaibi, Valeriu Codreanu, Kerstin Borras, Dirk Krücker
Reduced Precision Strategies for Deep Learning: A High Energy Physics Generative Adversarial Network Use Case. ICPRAM (2021)
Evangelos Georganas, Kunal Banerjee, Dhiraj D. Kalamkar, Sasikanth Avancha, Anand Venkat, Michael J. Anderson, Greg Henry, Hans Pabst, Alexander Heinecke
Harnessing Deep Learning via a Single Building Block. IPDPS (2020)
Evangelos Georganas, Kunal Banerjee, Dhiraj D. Kalamkar, Sasikanth Avancha, Anand Venkat, Michael J. Anderson, Greg Henry, Hans Pabst, Alexander Heinecke
High-Performance Deep Learning via a Single Building Block. CoRR (2019)
Evangelos Georganas, Sasikanth Avancha, Kunal Banerjee, Dhiraj D. Kalamkar, Greg Henry, Hans Pabst, Alexander Heinecke
Anatomy Of High-Performance Deep Learning Convolutions On SIMD Architectures. CoRR (2018)
Kim Albertsson, Piero Altoe, Dustin Anderson, Michael Andrews, Juan Pedro Araque Espinosa, Adam Aurisano, Laurent Basara, Adrian Bevan, Wahid Bhimji, Daniele Bonacorsi, Paolo Calafiura, Mario Campanelli, Louis Capps, Federico Carminati, Stefano Carrazza, Taylor Childers, Elias Coniavitis, Kyle Cranmer, Claire David, Douglas Davis, Javier M. 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 Alexander Heinrich, Ben Hooberman, Johannes Junggeburth, Michael Kagan, Meghan Kane, Konstantin Kanishchev, Przemyslaw Karpinski, 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 B. Newman, Hans Pabst, Michela Paganini, Manfred Paulini, Gabriel N. 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 Andrew Stewart, Bob Stienen, Ian Stockdale, Giles Strong, Savannah Thais, Karen Tomko, Eli Upfal, Emanuele Usai, Andrey Ustyuzhanin, Martin Vala, Sofia Vallecorsa, Mauro Verzetti, Xavier Vilasís-Cardona, Jean-Roch Vlimant, Ilija Vukotic, Sean-Jiun Wang, Gordon Watts, Michael Williams, Wenjing Wu, Stefan Wunsch, Omar Zapata
Machine Learning in High Energy Physics Community White Paper. CoRR (2018)
Sofia Vallecorsa, Federico Carminati, Gulrukh Khattak, Damian Podareanu, Valeriu Codreanu, Vikram A. Saletore, Hans Pabst
Distributed Training of Generative Adversarial Networks for Fast Detector Simulation. ISC Workshops (2018)
Evangelos Georganas, Sasikanth Avancha, Kunal Banerjee, Dhiraj D. Kalamkar, Greg Henry, Hans Pabst, Alexander Heinecke
Anatomy of high-performance deep learning convolutions on SIMD architectures. SC (2018)
Iain Bethune, Andreas Glöss, Jürg Hutter, Alfio Lazzaro, Hans Pabst, Fiona Reid
Porting of the DBCSR library for Sparse Matrix-Matrix Multiplications to Intel Xeon Phi systems. CoRR (2017)
Iain Bethune, Andreas Glöss, Jürg Hutter, Alfio Lazzaro, Hans Pabst, Fiona Reid
Porting of the DBCSR Library for Sparse Matrix-Matrix Multiplications to Intel Xeon Phi Systems. PARCO (2017)
Alexander Heinecke, Greg Henry, Maxwell Hutchinson, Hans Pabst
LIBXSMM: accelerating small matrix multiplications by runtime code generation. SC (2016)
Maxwell Hutchinson, Alexander Heinecke, Hans Pabst, Greg Henry, Matteo Parsani, David E. Keyes
Efficiency of High Order Spectral Element Methods on Petascale Architectures. ISC (2016)
Hans Pabst, Bev Bachmayer, Michael Klemm
Performance of a Structure-Detecting SpMV Using the CSR Matrix Representation. ISPDC (2012)
Alexander Heinecke, Michael Klemm, Hans Pabst, Dirk Pflüger
Towards High-Performance Implementations of a Custom HPC Kernel Using ® Array Building Blocks. Facing the Multicore-Challenge (2011)
Ariel Bernal, Ashok Thirumurthi, Hans Pabst, Tyler Nowicki, Michael McCool
Multigrid optical flow for deformable medical volume registration. SIGGRAPH Talks (2010)