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2020 – today
- 2024
[c31]Miguel Liu-Schiaffini, Julius Berner, Boris Bonev, Thorsten Kurth, Kamyar Azizzadenesheli, Anima Anandkumar:
Neural Operators with Localized Integral and Differential Kernels. ICML 2024- [c30]Hatem Ltaief, Rabab Alomairy, Qinglei Cao, Jie Ren, Lotfi Slim, Thorsten Kurth, Benedikt Dorschner, Salim Bougouffa, Rached Abdelkhalak, David E. Keyes:
Toward Capturing Genetic Epistasis From Multivariate Genome-Wide Association Studies Using Mixed-Precision Kernel Ridge Regression. SC 2024: 6 - [i23]Noah D. Brenowitz, Yair Cohen, Jaideep Pathak, Ankur Mahesh, Boris Bonev, Thorsten Kurth, Dale R. Durran, Peter Harrington, Michael S. Pritchard:
A Practical Probabilistic Benchmark for AI Weather Models. CoRR abs/2401.15305 (2024) - [i22]Miguel Liu-Schiaffini, Julius Berner, Boris Bonev, Thorsten Kurth, Kamyar Azizzadenesheli, Anima Anandkumar:
Neural Operators with Localized Integral and Differential Kernels. CoRR abs/2402.16845 (2024) - [i21]Chenggong Wang, Michael S. Pritchard, Noah D. Brenowitz, Yair Cohen, Boris Bonev, Thorsten Kurth, Dale R. Durran, Jaideep Pathak:
Coupled Ocean-Atmosphere Dynamics in a Machine Learning Earth System Model. CoRR abs/2406.08632 (2024) - [i20]Ankur Mahesh, William D. Collins, Boris Bonev, Noah D. Brenowitz, Yair Cohen, Peter Harrington, Karthik Kashinath, Thorsten Kurth, Joshua North, Travis A. O'Brien, Michael S. Pritchard, David Pruitt, Mark Risser, Shashank Subramanian, Jared Willard:
Huge Ensembles Part II: Properties of a Huge Ensemble of Hindcasts Generated with Spherical Fourier Neural Operators. CoRR abs/2408.01581 (2024) - [i19]Ankur Mahesh, William D. Collins, Boris Bonev, Noah D. Brenowitz, Yair Cohen, Joshua Elms, Peter Harrington, Karthik Kashinath, Thorsten Kurth, Joshua North, Travis A. O'Brien, Michael S. Pritchard, David Pruitt, Mark Risser, Shashank Subramanian, Jared Willard:
Huge Ensembles Part I: Design of Ensemble Weather Forecasts using Spherical Fourier Neural Operators. CoRR abs/2408.03100 (2024) - [i18]Hatem Ltaief, Rabab Alomairy, Qinglei Cao, Jie Ren, Lotfi Slim, Thorsten Kurth, Benedikt Dorschner, Salim Bougouffa, Rached Abdelkhalak, David E. Keyes:
Toward Capturing Genetic Epistasis From Multivariate Genome-Wide Association Studies Using Mixed-Precision Kernel Ridge Regression. CoRR abs/2409.01712 (2024) - [i17]Jussi Leinonen, Boris Bonev, Thorsten Kurth, Yair Cohen:
Modulated Adaptive Fourier Neural Operators for Temporal Interpolation of Weather Forecasts. CoRR abs/2410.18904 (2024) - 2023
- [c29]Felix Liu, Vanitha Sankaranarayanan, Javier E. Villanueva-Meyer, Shawn Hervey-Jumper, James Hawkins, Pablo F. Damasceno, Mauro Bisson, Joshua Romero, Thorsten Kurth, Massimiliano Fatica, Eleftherios Garyfallidis, Ariel Rokem
, Jason C. Crane, Sharmila Majumdar:
Clinical validation of rapid GPU-enabled DTI tractography of the brain. High Performance Computing for Imaging 2023: 1-4 - [c28]Boris Bonev, Thorsten Kurth, Christian Hundt, Jaideep Pathak, Maximilian Baust, Karthik Kashinath, Anima Anandkumar:
Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere. ICML 2023: 2806-2823 - [c27]Sungduk Yu, Walter M. Hannah, Liran Peng, Jerry Lin, Mohamed Aziz Bhouri, Ritwik Gupta, Björn Lütjens, Justus C. Will, Gunnar Behrens, Julius Busecke, Nora Loose, Charles Stern, Tom Beucler, Bryce E. Harrop, Benjamin R. Hillman, Andrea M. Jenney, Savannah L. Ferretti, Nana Liu, Animashree Anandkumar, Noah D. Brenowitz, Veronika Eyring, Nicholas Geneva, Pierre Gentine, Stephan Mandt, Jaideep Pathak, Akshay Subramaniam, Carl Vondrick, Rose Yu, Laure Zanna, Tian Zheng, Ryan Abernathey, Fiaz Ahmed, David C. Bader, Pierre Baldi, Elizabeth A. Barnes, Christopher S. Bretherton, Peter M. Caldwell, Wayne Chuang, Yilun Han, Yu Huang, Fernando Iglesias-Suarez, Sanket R. Jantre, Karthik Kashinath, Marat Khairoutdinov, Thorsten Kurth, Nicholas J. Lutsko, Po-Lun Ma, Griffin Mooers, J. David Neelin, David A. Randall, Sara Shamekh, Mark Taylor, Nathan M. Urban, Janni Yuval, Guang Zhang, Mike Pritchard:
ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation. NeurIPS 2023 - [c26]Aristeidis Tsaris, Joshua Romero, Thorsten Kurth, Jacob D. Hinkle, Hong-Jun Yoon, Feiyi Wang, Sajal Dash, Georgia D. Tourassi:
Scaling Resolution of Gigapixel Whole Slide Images Using Spatial Decomposition on Convolutional Neural Networks. PASC 2023: 2:1-2:11 - [c25]Thorsten Kurth, Shashank Subramanian, Peter Harrington, Jaideep Pathak, Morteza Mardani, David Hall, Andrea Miele, Karthik Kashinath, Anima Anandkumar:
FourCastNet: Accelerating Global High-Resolution Weather Forecasting Using Adaptive Fourier Neural Operators. PASC 2023: 13:1-13:11 - [i16]Boris Bonev, Thorsten Kurth, Christian Hundt, Jaideep Pathak, Maximilian Baust, Karthik Kashinath, Anima Anandkumar:
Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere. CoRR abs/2306.03838 (2023) - [i15]Sungduk Yu, Walter M. Hannah, Liran Peng, Mohamed Aziz Bhouri, Ritwik Gupta, Jerry Lin, Björn Lütjens, Justus C. Will, Tom Beucler, Bryce E. Harrop, Benjamin R. Hillman, Andrea M. Jenney, Savannah L. Ferretti, Nana Liu, Anima Anandkumar, Noah D. Brenowitz, Veronika Eyring, Pierre Gentine, Stephan Mandt, Jaideep Pathak, Carl Vondrick, Rose Yu, Laure Zanna, Ryan P. Abernathey, Fiaz Ahmed, David C. Bader, Pierre Baldi, Elizabeth A. Barnes, Gunnar Behrens, Christopher S. Bretherton, Julius J. M. Busecke, Peter M. Caldwell, Wayne Chuang, Yilun Han, Yu Huang, Fernando Iglesias-Suarez, Sanket R. Jantre, Karthik Kashinath, Marat Khairoutdinov, Thorsten Kurth, Nicholas J. Lutsko, Po-Lun Ma, Griffin Mooers, J. David Neelin, David A. Randall, Sara Shamekh, Akshay Subramaniam, Mark A. Taylor, et al.:
ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators. CoRR abs/2306.08754 (2023) - [i14]Matthias Karlbauer, Nathaniel Cresswell-Clay, Raul A. Moreno, Dale R. Durran, Thorsten Kurth, Martin V. Butz:
Advancing Parsimonious Deep Learning Weather Prediction using the HEALPix Mesh. CoRR abs/2311.06253 (2023) - 2022
- [c24]Robin Shao, Thorsten Kurth, Zhengji Zhao:
NERSC Job Script Generator. HUST@SC 2022: 41-43 - [i13]Jaideep Pathak, Shashank Subramanian, Peter Harrington, Sanjeev Raja, Ashesh Chattopadhyay, Morteza Mardani, Thorsten Kurth, David Hall, Zongyi Li, Kamyar Azizzadenesheli, Pedram Hassanzadeh, Karthik Kashinath, Animashree Anandkumar:
FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators. CoRR abs/2202.11214 (2022) - [i12]Thorsten Kurth, Shashank Subramanian, Peter Harrington, Jaideep Pathak, Morteza Mardani, David Hall, Andrea Miele, Karthik Kashinath, Animashree Anandkumar:
FourCastNet: Accelerating Global High-Resolution Weather Forecasting using Adaptive Fourier Neural Operators. CoRR abs/2208.05419 (2022) - 2021
- [j7]Lorenzo Casalino, Abigail C. Dommer, Zied Gaieb, Emília P. Barros, Terra Sztain, Surl-Hee Ahn, Anda Trifan, Alexander Brace, Anthony T. Bogetti, Austin Clyde, Heng Ma, Hyungro Lee, Matteo Turilli, Syma Khalid, Lillian T. Chong, Carlos Simmerling, David J. Hardy, Julio D. C. Maia, James C. Phillips, Thorsten Kurth, Abraham C. Stern, Lei Huang, John D. McCalpin, Mahidhar Tatineni, Tom Gibbs, John E. Stone, Shantenu Jha, Arvind Ramanathan, Rommie E. Amaro:
AI-driven multiscale simulations illuminate mechanisms of SARS-CoV-2 spike dynamics. Int. J. High Perform. Comput. Appl. 35(5) (2021) - [c23]Aymen Al Saadi, Dario Alfè, Yadu N. Babuji, Agastya Bhati, Ben Blaiszik, Alexander Brace, Thomas S. Brettin, Kyle Chard, Ryan Chard, Austin Clyde, Peter V. Coveney, Ian T. Foster, Tom Gibbs, Shantenu Jha, Kristopher Keipert, Dieter Kranzlmüller, Thorsten Kurth, Hyungro Lee, Zhuozhao Li, Heng Ma, Gerald Mathias, André Merzky, Alexander Partin, Arvind Ramanathan, Ashka Shah, Abraham C. Stern, Rick Stevens, Li Tan, Mikhail Titov, Anda Trifan, Aristeidis Tsaris, Matteo Turilli, Huub J. J. Van Dam, Shunzhou Wan, David Wifling, Junqi Yin:
IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads. ICPP 2021: 40:1-40:12 - [c22]Steven Farrell, Murali Emani, Jacob Balma, Lukas Drescher, Aleksandr Drozd, Andreas Fink, Geoffrey C. Fox, David Kanter, Thorsten Kurth, Peter Mattson, Dawei Mu, Amit Ruhela, Kento Sato, Koichi Shirahata, Tsuguchika Tabaru, Aristeidis Tsaris, Jan Balewski, Ben Cumming, Takumi Danjo, Jens Domke, Takaaki Fukai, Naoto Fukumoto, Tatsuya Fukushi, Balazs Gerofi, Takumi Honda, Toshiyuki Imamura, Akihiko Kasagi, Kentaro Kawakami, Shuhei Kudo, Akiyoshi Kuroda, Maxime Martinasso, Satoshi Matsuoka, Henrique Mendonça, Kazuki Minami, Prabhat Ram, Takashi Sawada, Mallikarjun Shankar, Tom St. John, Akihiro Tabuchi, Venkatram Vishwanath, Mohamed Wahib, Masafumi Yamazaki, Junqi Yin:
MLPerf™ HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems. MLHPC@SC 2021: 33-45 - [c21]Charlene Yang, Yunsong Wang, Thorsten Kurth, Steven Farrell, Samuel Williams:
Hierarchical Roofline Performance Analysis for Deep Learning Applications. SAI (2) 2021: 473-491 - [i11]Steven Farrell, Murali Emani, Jacob Balma, Lukas Drescher, Aleksandr Drozd, Andreas Fink, Geoffrey C. Fox, David Kanter, Thorsten Kurth, Peter Mattson, Dawei Mu, Amit Ruhela, Kento Sato, Koichi Shirahata, Tsuguchika Tabaru, Aristeidis Tsaris, Jan Balewski, Ben Cumming, Takumi Danjo, Jens Domke, Takaaki Fukai, Naoto Fukumoto, Tatsuya Fukushi, Balazs Gerofi, Takumi Honda, Toshiyuki Imamura, Akihiko Kasagi, Kentaro Kawakami, Shuhei Kudo, Akiyoshi Kuroda, Maxime Martinasso, Satoshi Matsuoka, Henrique Mendonça, Kazuki Minami, Prabhat Ram, Takashi Sawada, Mallikarjun Shankar, Tom St. John, Akihiro Tabuchi, Venkatram Vishwanath, Mohamed Wahib, Masafumi Yamazaki, Junqi Yin:
MLPerf HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems. CoRR abs/2110.11466 (2021) - 2020
- [j6]Charlene Yang, Thorsten Kurth, Samuel Williams:
Hierarchical Roofline analysis for GPUs: Accelerating performance optimization for the NERSC-9 Perlmutter system. Concurr. Comput. Pract. Exp. 32(20) (2020) - [c20]Yunsong Wang, Charlene Yang, Steven Farrell, Yan Zhang, Thorsten Kurth, Samuel Williams:
Time-Based Roofline for Deep Learning Performance Analysis. DLS@SC 2020: 10-19 - [i10]Yunsong Wang, Charlene Yang, Steven Farrell, Yan Zhang, Thorsten Kurth, Samuel Williams:
Time-Based Roofline for Deep Learning Performance Analysis. CoRR abs/2009.04598 (2020) - [i9]Yunsong Wang, Charlene Yang, Steven Farrell, Thorsten Kurth, Samuel Williams:
Hierarchical Roofline Performance Analysis for Deep Learning Applications. CoRR abs/2009.05257 (2020) - [i8]Jaideep Pathak, Mustafa Mustafa, Karthik Kashinath, Emmanuel Motheau, Thorsten Kurth, Marcus Day:
Using Machine Learning to Augment Coarse-Grid Computational Fluid Dynamics Simulations. CoRR abs/2010.00072 (2020) - [i7]Aymen Al Saadi, Dario Alfè, Yadu N. Babuji, Agastya Bhati, Ben Blaiszik, Thomas S. Brettin, Kyle Chard, Ryan Chard, Peter V. Coveney, Anda Trifan, Alex Brace, Austin Clyde, Ian T. Foster, Tom Gibbs, Shantenu Jha, Kristopher Keipert, Thorsten Kurth, Dieter Kranzlmüller, Hyungro Lee, Zhuozhao Li, Heng Ma, André Merzky, Gerald Mathias, Alexander Partin, Junqi Yin, Arvind Ramanathan, Ashka Shah, Abraham C. Stern, Rick Stevens, Li Tan, Mikhail Titov, Aristeidis Tsaris, Matteo Turilli, Huub J. J. Van Dam, Shunzhou Wan, David Wifling:
IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads. CoRR abs/2010.06574 (2020)
2010 – 2019
- 2019
- [j5]Brandon Cook, Thorsten Kurth, Jack Deslippe, Pierre Carrier, Nick Hill, Nathan Wichmann:
Eigensolver performance comparison on Cray XC systems. Concurr. Comput. Pract. Exp. 31(16) (2019) - [j4]Thorsten Kurth, Mikhail Smorkalov, Peter Mendygral, Srinivas Sridharan, Amrita Mathuriya:
TensorFlow at Scale: Performance and productivity analysis of distributed training with Horovod, MLSL, and Cray PE ML. Concurr. Comput. Pract. Exp. 31(16) (2019) - [c19]David Ojika, Ann Gordon-Ross, Herman Lam, Shinjae Yoo, Younggang Cui, Zhihua Dong, Kirstin Kleese van Dam, Seyong Lee, Thorsten Kurth:
PCS: A Productive Computational Science Platform. HPCS 2019: 636-641 - [c18]Liu Yang, Prabhat, George E. Karniadakis, Sean Treichler, Thorsten Kurth, Keno Fischer, David A. Barajas-Solano, Joshua Romero, Valentin Churavy, Alexandre M. Tartakovsky, Michael Houston:
Highly-Ccalable, Physics-Informed GANs for Learning Solutions of Stochastic PDEs. DLS@SC 2019: 1-11 - [c17]Bálint Joó, Thorsten Kurth, Michael A. Clark, Jeongnim Kim, Christian Robert Trott, Dan Ibanez, Daniel Sunderland, Jack Deslippe:
Performance Portability of a Wilson Dslash Stencil Operator Mini-App Using Kokkos and SYCL. P3HPC@SC 2019: 14-25 - [c16]Chao Jiang, Dave Ojika, Thorsten Kurth, Prabhat, Sofia Vallecorsa, Bhavesh Patel, Herman Lam:
Acceleration of Scientific Deep Learning Models on Heterogeneous Computing Platform with Intel® FPGAs. ISC Workshops 2019: 587-600 - [i6]Liu Yang, Sean Treichler, Thorsten Kurth, Keno Fischer, David A. Barajas-Solano, Joshua Romero, Valentin Churavy, Alexandre M. Tartakovsky, Michael Houston, Prabhat, George E. Karniadakis:
Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs. CoRR abs/1910.13444 (2019) - 2018
- [j3]Yun (Helen) He, Brandon Cook, Jack Deslippe, Brian Friesen, Richard A. Gerber, Rebecca Hartman-Baker, Alice Koniges, Thorsten Kurth, Stephen Leak, Woo-Sun Yang, Zhengji Zhao, Eddie Baron, Peter Hauschildt:
Preparing NERSC users for Cori, a Cray XC40 system with Intel many integrated cores. Concurr. Comput. Pract. Exp. 30(1) (2018) - [j2]Chin-Chen Chang, Amy N. Nicholson, Enrico Rinaldi, Evan Berkowitz, Nicolas Garron, D. A. Brantley, H. Monge-Camacho, C. J. Monahan, Chris Bouchard, Michael A. Clark, Bálint Joó, Thorsten Kurth, Konstantinos Orginos, Pavlos Vranas, André Walker-Loud:
A per-cent-level determination of the nucleon axial coupling from quantum chromodynamics. Nat. 558(7708): 91-94 (2018) - [c15]Thorsten Kurth, Sean Treichler, Joshua Romero, Mayur Mudigonda, Nathan Luehr, Everett H. Phillips, Ankur Mahesh, Michael A. Matheson, Jack Deslippe, Massimiliano Fatica, Prabhat, Michael Houston:
Exascale deep learning for climate analytics. SC 2018: 51:1-51:12 - [c14]Evan Berkowitz, Michael A. Clark, Arjun Singh Gambhir, Kenneth McElvain, Amy N. Nicholson, Enrico Rinaldi, Pavlos Vranas, André Walker-Loud, Chia-Cheng Chang, Bálint Joó, Thorsten Kurth, Kostas Orginos:
Simulating the weak death of the Neutron in a femtoscale universe with near-exascale computing. SC 2018: 55:1-55:9 - [c13]Brian Austin, Christopher S. Daley, Douglas Doerfler, Jack Deslippe, Brandon Cook, Brian Friesen, Thorsten Kurth, Charlene Yang, Nicholas J. Wright:
A Metric for Evaluating Supercomputer Performance in the Era of Extreme Heterogeneity. PMBS@SC 2018: 63-71 - [c12]Rahulkumar Gayatri, Charlene Yang, Thorsten Kurth, Jack Deslippe:
A Case Study for Performance Portability Using OpenMP 4.5. WACCPD@SC 2018: 75-95 - [c11]Brandon Cook, Charlene Yang, Thorsten Kurth, Jack Deslippe:
Sparse CSB_Coo Matrix-Vector and Matrix-Matrix Performance on Intel Xeon Architectures. ISC Workshops 2018: 463-471 - [c10]Bálint Joó, Thorsten Kurth:
Lessons Learned from Optimizing Kernels for Adaptive Aggregation Multi-grid Solvers in Lattice QCD. ISC Workshops 2018: 472-486 - [i5]Evan Berkowitz, Michael A. Clark, Arjun Singh Gambhir, Kenneth McElvain, Amy N. Nicholson, Enrico Rinaldi, Pavlos Vranas, André Walker-Loud, Chia-Cheng Chang, Bálint Joó, Thorsten Kurth, Kostas Orginos:
Simulating the weak death of the neutron in a femtoscale universe with near-Exascale computing. CoRR abs/1810.01609 (2018) - [i4]Thorsten Kurth, Sean Treichler, Joshua Romero, Mayur Mudigonda, Nathan Luehr, Everett H. Phillips, Ankur Mahesh, Michael A. Matheson, Jack Deslippe, Massimiliano Fatica, Prabhat, Michael Houston:
Exascale Deep Learning for Climate Analytics. CoRR abs/1810.01993 (2018) - 2017
- [j1]Taylor Barnes, Thorsten Kurth, Pierre Carrier, Nathan Wichmann, David Prendergast, Paul R. C. Kent, Jack Deslippe:
Improved treatment of exact exchange in Quantum ESPRESSO. Comput. Phys. Commun. 214: 52-58 (2017) - [c9]Thorsten Kurth, Jian Zhang, Nadathur Satish, Evan Racah, Ioannis Mitliagkas, Md. Mostofa Ali Patwary, Tareq M. Malas, Narayanan Sundaram, Wahid Bhimji, Mikhail Smorkalov, Jack Deslippe, Mikhail Shiryaev, Srinivas Sridharan, Prabhat, Pradeep Dubey:
Deep learning at 15PF: supervised and semi-supervised classification for scientific data. SC 2017: 7 - [c8]Thorsten Kurth, William Arndt, Taylor Barnes, Brandon Cook, Jack Deslippe, Douglas Doerfler, Brian Friesen, Yun (Helen) He, Tuomas Koskela, Mathieu Lobet, Tareq M. Malas, Leonid Oliker, Andrey Ovsyannikov, Samuel Williams, Woo-Sun Yang, Zhengji Zhao:
Analyzing Performance of Selected NESAP Applications on the Cori HPC System. ISC Workshops 2017: 334-347 - [c7]Brandon Cook, Thorsten Kurth, Brian Austin, Samuel Williams, Jack Deslippe:
Performance Variability on Xeon Phi. ISC Workshops 2017: 419-429 - [i3]Thorsten Kurth, Jian Zhang, Nadathur Satish, Ioannis Mitliagkas, Evan Racah, Md. Mostofa Ali Patwary, Tareq M. Malas, Narayanan Sundaram, Wahid Bhimji, Mikhail Smorkalov, Jack Deslippe, Mikhail Shiryaev, Srinivas Sridharan, Prabhat, Pradeep Dubey:
Deep Learning at 15PF: Supervised and Semi-Supervised Classification for Scientific Data. CoRR abs/1708.05256 (2017) - [i2]Wahid Bhimji, Steven Andrew Farrell, Thorsten Kurth, Michela Paganini, Prabhat, Evan Racah:
Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC. CoRR abs/1711.03573 (2017) - [i1]Amrita Mathuriya, Thorsten Kurth, Vivek Rane, Mustafa Mustafa, Lei Shao, Debbie Bard, Prabhat, Victor W. Lee:
Scaling GRPC Tensorflow on 512 nodes of Cori Supercomputer. CoRR abs/1712.09388 (2017) - 2016
- [c6]Zhaoyi Meng, Alice Koniges, Yun (Helen) He, Samuel Williams, Thorsten Kurth, Brandon Cook, Jack Deslippe, Andrea L. Bertozzi:
OpenMP Parallelization and Optimization of Graph-Based Machine Learning Algorithms. IWOMP 2016: 17-31 - [c5]Alice Koniges, Brandon Cook, Jack Deslippe, Thorsten Kurth, Hongzhang Shan:
MPI usage at NERSC: Present and Future. EuroMPI 2016: 217 - [c4]Taylor Barnes, Brandon Cook, Jack Deslippe, Douglas Doerfler, Brian Friesen, Yun (Helen) He, Thorsten Kurth, Tuomas Koskela, Mathieu Lobet, Tareq M. Malas, Leonid Oliker, Andrey Ovsyannikov, Abhinav Sarje, Jean-Luc Vay, Henri Vincenti, Samuel Williams, Pierre Carrier, Nathan Wichmann, Marcus Wagner, Paul R. C. Kent, Christopher Kerr, John M. Dennis:
Evaluating and Optimizing the NERSC Workload on Knights Landing. PMBS@SC 2016: 43-53 - [c3]Douglas Doerfler, Jack Deslippe, Samuel Williams, Leonid Oliker, Brandon Cook, Thorsten Kurth, Mathieu Lobet, Tareq M. Malas, Jean-Luc Vay, Henri Vincenti:
Applying the Roofline Performance Model to the Intel Xeon Phi Knights Landing Processor. ISC Workshops 2016: 339-353 - [c2]Tareq M. Malas, Thorsten Kurth, Jack Deslippe:
Optimization of the Sparse Matrix-Vector Products of an IDR Krylov Iterative Solver in EMGeo for the Intel KNL Manycore Processor. ISC Workshops 2016: 378-389 - [c1]Bálint Joó, Dhiraj D. Kalamkar, Thorsten Kurth, Karthikeyan Vaidyanathan, Aaron Walden:
Optimizing Wilson-Dirac Operator and Linear Solvers for Intel® KNL. ISC Workshops 2016: 415-427
Coauthor Index
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