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ORCIDStefan M. Wild
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- affiliation: Mathematics and Computer Science Division, Argonne National Laboratory
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Journal Articles
- 2024
[j43]Matt Menickelly
, Stefan M. Wild:
Stochastic average model methods. Comput. Optim. Appl. 88(2): 405-442 (2024)- [j42]Kwassi Joseph Dzahini, Stefan M. Wild:
Stochastic Trust-Region Algorithm in Random Subspaces with Convergence and Expected Complexity Analyses. SIAM J. Optim. 34(3): 2671-2699 (2024) - [j41]Stefan M. Wild:
Research Spotlights. SIAM Rev. 66(1): 89 (2024) - [j40]Stefan M. Wild:
Research Spotlights. SIAM Rev. 66(2): 285 (2024) - [j39]Stefan M. Wild:
Research Spotlights. SIAM Rev. 66(3): 479 (2024) - [j38]Moses Y.-H. Chan, Matthew Plumlee, Stefan M. Wild:
Constructing a Simulation Surrogate with Partially Observed Output. Technometrics 66(1): 1-13 (2024) - [j37]Özge Sürer, Matthew Plumlee, Stefan M. Wild:
Sequential Bayesian Experimental Design for Calibration of Expensive Simulation Models. Technometrics 66(2): 157-171 (2024) - 2023
- [j36]Tyler H. Chang, Stefan M. Wild:
ParMOO: A Python library for parallel multiobjective simulation optimization. J. Open Source Softw. 8(82): 4468 (2023) - [j35]Aleksandra Ciprijanovic, Ashia Lewis, Kevin Pedro, Sandeep Madireddy, Brian Nord, Gabriel N. Perdue, Stefan M. Wild:
DeepAstroUDA: semi-supervised universal domain adaptation for cross-survey galaxy morphology classification and anomaly detection. Mach. Learn. Sci. Technol. 4(2): 25013 (2023) - [j34]Raghu Bollapragada, Stefan M. Wild:
Adaptive sampling quasi-Newton methods for zeroth-order stochastic optimization. Math. Program. Comput. 15(2): 327-364 (2023) - [j33]Jeffrey Larson, Misha Padidar, Stefan M. Wild:
Modeling approaches for addressing unrelaxable bound constraints with unconstrained optimization methods. Optim. Lett. 17(3): 561-589 (2023) - [j32]Stefan M. Wild:
Research Spotlights. SIAM Rev. 65(1): 145 (2023) - [j31]Stefan M. Wild:
Research Spotlights. SIAM Rev. 65(2): 437 (2023) - [j30]Stefan M. Wild:
Research Spotlights. SIAM Rev. 65(3): 733 (2023) - [j29]Stefan M. Wild:
Research Spotlights. SIAM Rev. 65(4): 1029 (2023) - [j28]Abdulkadir Canatar, Evan Peters, Cengiz Pehlevan, Stefan M. Wild, Ruslan Shaydulin:
Bandwidth Enables Generalization in Quantum Kernel Models. Trans. Mach. Learn. Res. 2023 (2023) - 2022
- [j27]Aleksandra Ciprijanovic, Diana Kafkes, Gregory F. Snyder, F. Javier Sánchez, Gabriel Nathan Perdue, Kevin Pedro, Brian Nord, Sandeep Madireddy, Stefan M. Wild:
DeepAdversaries: examining the robustness of deep learning models for galaxy morphology classification. Mach. Learn. Sci. Technol. 3(3): 35007 (2022) - [j26]Stephen Hudson, Jeffrey Larson, John-Luke Navarro, Stefan M. Wild:
libEnsemble: A Library to Coordinate the Concurrent Evaluation of Dynamic Ensembles of Calculations. IEEE Trans. Parallel Distributed Syst. 33(4): 977-988 (2022) - 2021
- [j25]Nathan Wycoff, Mickaël Binois, Stefan M. Wild:
Sequential Learning of Active Subspaces. J. Comput. Graph. Stat. 30(4): 1224-1237 (2021) - [j24]Jeffrey Larson, Sven Leyffer, Prashant Palkar, Stefan M. Wild:
A method for convex black-box integer global optimization. J. Glob. Optim. 80(2): 439-477 (2021) - [j23]Jed Brown, Yunhui He, Scott P. MacLachlan, Matt Menickelly, Stefan M. Wild:
Tuning Multigrid Methods with Robust Optimization and Local Fourier Analysis. SIAM J. Sci. Comput. 43(1): A109-A138 (2021) - 2020
- [j22]Sven Leyffer, Matt Menickelly, Todd S. Munson, Charlie Vanaret, Stefan M. Wild:
A survey of nonlinear robust optimization. INFOR Inf. Syst. Oper. Res. 58(2): 342-373 (2020) - [j21]Matt Menickelly, Stefan M. Wild:
Derivative-free robust optimization by outer approximations. Math. Program. 179(1): 157-193 (2020) - 2019
- [j20]Jeffrey Larson, Matt Menickelly, Stefan M. Wild:
Derivative-free optimization methods. Acta Numer. 28: 287-404 (2019) - [j19]Anthony P. Austin, Zichao Wendy Di, Sven Leyffer, Stefan M. Wild:
Simultaneous Sensing Error Recovery and Tomographic Inversion Using an Optimization-Based Approach. SIAM J. Sci. Comput. 41(3): B497-B521 (2019) - 2018
- [j18]Jeffrey Larson, Stefan M. Wild:
Asynchronously parallel optimization solver for finding multiple minima. Math. Program. Comput. 10(3): 303-332 (2018) - [j17]Kamil A. Khan, Jeffrey Larson, Stefan M. Wild:
Manifold Sampling for Optimization of Nonconvex Functions That Are Piecewise Linear Compositions of Smooth Components. SIAM J. Optim. 28(4): 3001-3024 (2018) - 2017
- [j16]Rommel G. Regis, Stefan M. Wild:
CONORBIT: constrained optimization by radial basis function interpolation in trust regions. Optim. Methods Softw. 32(3): 552-580 (2017) - 2016
- [j15]Zichao (Wendy) Di, Sven Leyffer, Stefan M. Wild:
Optimization-Based Approach for Joint X-Ray Fluorescence and Transmission Tomographic Inversion. SIAM J. Imaging Sci. 9(1): 1-23 (2016) - [j14]Jeffrey Larson, Matt Menickelly, Stefan M. Wild:
Manifold Sampling for ℓ1 Nonconvex Optimization. SIAM J. Optim. 26(4): 2540-2563 (2016) - [j13]Robert B. Gramacy, Genetha A. Gray, Sébastien Le Digabel, Herbert K. H. Lee, Pritam Ranjan, Garth N. Wells, Stefan M. Wild:
Modeling an Augmented Lagrangian for Blackbox Constrained Optimization. Technometrics 58(1): 1-11 (2016) - [j12]Robert B. Gramacy, Genetha A. Gray, Sébastien Le Digabel, Herbert K. H. Lee, Pritam Ranjan, Garth N. Wells, Stefan M. Wild:
Rejoinder. Technometrics 58(1): 26-29 (2016) - 2014
- [j11]Jorge J. Moré, Stefan M. Wild:
Do you trust derivatives or differences? J. Comput. Phys. 273: 268-277 (2014) - 2013
- [j10]M. V. Stoitsov, Nicolas Schunck, Markus Kortelainen, N. Michel, H. Nam, E. Olsen, Jason Sarich, Stefan M. Wild:
Axially deformed solution of the Skyrme-Hartree-Fock-Bogoliubov equations using the transformed harmonic oscillator basis (II) hfbtho v2.00d: A new version of the program. Comput. Phys. Commun. 184(6): 1592-1604 (2013) - [j9]Scott Bogner, Aurel Bulgac, Joseph A. Carlson, Jonathan Engel, George L. Fann, Richard J. Furnstahl, Stefano Gandolfi, Gaute Hagen, Mihai Horoi, Calvin W. Johnson, Markus Kortelainen, Ewing L. Lusk, Pieter Maris, Hai Ah Nam, Petr Navratil, Witold Nazarewicz, Esmond G. Ng, Gustavo P. A. Nobre, W. Erich Ormand, Thomas Papenbrock, Junchen Pei, Steven C. Pieper, Sofia Quaglioni, Kenneth J. Roche, Jason Sarich, Nicolas Schunck, Masha Sosonkina, Jun Terasaki, Ian J. Thompson, James P. Vary, Stefan M. Wild:
Computational nuclear quantum many-body problem: The UNEDF project. Comput. Phys. Commun. 184(10): 2235-2250 (2013) - [j8]Jeffrey Larson, Stefan M. Wild:
Non-intrusive termination of noisy optimization. Optim. Methods Softw. 28(5): 993-1011 (2013) - [j7]Stefan M. Wild, Christine A. Shoemaker:
Global Convergence of Radial Basis Function Trust-Region Algorithms for Derivative-Free Optimization. SIAM Rev. 55(2): 349-371 (2013) - 2012
- [j6]Jorge J. Moré, Stefan M. Wild:
Estimating Derivatives of Noisy Simulations. ACM Trans. Math. Softw. 38(3): 19:1-19:21 (2012) - 2011
- [j5]Stefan M. Wild, Christine A. Shoemaker:
Global Convergence of Radial Basis Function Trust Region Derivative-Free Algorithms. SIAM J. Optim. 21(3): 761-781 (2011) - [j4]Jorge J. Moré, Stefan M. Wild:
Estimating Computational Noise. SIAM J. Sci. Comput. 33(3): 1292-1314 (2011) - 2009
- [j3]Jorge J. Moré, Stefan M. Wild:
Benchmarking Derivative-Free Optimization Algorithms. SIAM J. Optim. 20(1): 172-191 (2009) - 2008
- [j2]Stefan M. Wild, Rommel G. Regis, Christine A. Shoemaker:
ORBIT: Optimization by Radial Basis Function Interpolation in Trust-Regions. SIAM J. Sci. Comput. 30(6): 3197-3219 (2008) - 2004
- [j1]Stefan M. Wild, James Curry, Anne Dougherty:
Improving non-negative matrix factorizations through structured initialization. Pattern Recognit. 37(11): 2217-2232 (2004)
Conference and Workshop Papers
- 2022
- [c35]Ishan Abhinit, Emily K. Adams, Khairul Alam, Brian Chase, Ewa Deelman, Lev Gorenstein, Stephen Hudson, Tanzima Z. Islam, Jeffrey Larson, Geoffrey Lentner, Anirban Mandal, John-Luke Navarro, Bogdan Nicolae, Line Pouchard, Robert B. Ross, Banani Roy, Mats Rynge, Alexander Serebrenik, Karan Vahi, Stefan M. Wild, Yufeng Xin, Rafael Ferreira da Silva, Rosa Filgueira:
Novel Proposals for FAIR, Automated, Recommendable, and Robust Workflows. WORKS@SC 2022: 84-92 - 2021
- [c34]Arindam Fadikar, Stefan M. Wild, Jonas Chaves-Montero:
Scalable Statistical Inference of Photometric Redshift via Data Subsampling. ICCS (5) 2021: 245-258 - [c33]Zichao (Wendy) Di, Esteban Rangel, Shinjae Yoo, Stefan M. Wild:
Hierarchical Analysis of Halo Center in Cosmology. ICCS (1) 2021: 671-684 - [c32]Henri Casanova, Ewa Deelman, Sandra Gesing, Michael D. Hildreth, Stephen Hudson, William Koch, Jeffrey Larson, Mary Ann McDowell, Natalie Meyers, John-Luke Navarro, George Papadimitriou, Ryan Tanaka, Ian J. Taylor, Douglas Thain, Stefan M. Wild, Rosa Filgueira, Rafael Ferreira da Silva:
Emerging Frameworks for Advancing Scientific Workflows Research, Development, and Education. WORKS 2021: 74-80 - 2020
- [c31]Nathanaël Cheriere, Matthieu Dorier, Gabriel Antoniu, Stefan M. Wild, Sven Leyffer, Robert B. Ross:
Pufferscale: Rescaling HPC Data Services for High Energy Physics Applications. CCGRID 2020: 182-191 - 2019
- [c30]Xiang Huang, Stefan M. Wild, Zichao Wendy Di:
Calibrating Sensing Drift in Tomographic Inversion. ICIP 2019: 1267-1271 - [c29]Sandeep Madireddy, Prasanna Balaprakash, Philip H. Carns, Robert Latham, Glenn K. Lockwood, Robert B. Ross, Shane Snyder, Stefan M. Wild:
Adaptive Learning for Concept Drift in Application Performance Modeling. ICPP 2019: 79:1-79:11 - [c28]Prasanna Balaprakash, Romain Egele, Misha Salim, Stefan M. Wild, Venkatram Vishwanath, Fangfang Xia, Tom Brettin, Rick Stevens:
Scalable reinforcement-learning-based neural architecture search for cancer deep learning research. SC 2019: 37:1-37:33 - 2018
- [c27]Sandeep Madireddy, Prasanna Balaprakash, Philip H. Carns, Robert Latham, Robert B. Ross, Shane Snyder, Stefan M. Wild:
Modeling I/O Performance Variability Using Conditional Variational Autoencoders. CLUSTER 2018: 109-113 - [c26]Prasanna Balaprakash, Michael Salim, Thomas D. Uram, Venkat Vishwanath, Stefan M. Wild:
DeepHyper: Asynchronous Hyperparameter Search for Deep Neural Networks. HiPC 2018: 42-51 - [c25]Sandeep Madireddy, Prasanna Balaprakash, Philip H. Carns, Robert Latham, Robert B. Ross, Shane Snyder, Stefan M. Wild:
Machine Learning Based Parallel I/O Predictive Modeling: A Case Study on Lustre File Systems. ISC 2018: 184-204 - 2017
- [c24]Ian T. Foster, Mark Ainsworth, Bryce Allen, Julie Bessac, Franck Cappello, Jong Youl Choi, Emil M. Constantinescu, Philip E. Davis, Sheng Di, Zichao Wendy Di, Hanqi Guo, Scott Klasky, Kerstin Kleese van Dam, Tahsin M. Kurç, Qing Liu, Abid Malik, Kshitij Mehta, Klaus Mueller, Todd S. Munson, George Ostrouchov, Manish Parashar, Tom Peterka, Line Pouchard, Dingwen Tao, Ozan Tugluk, Stefan M. Wild, Matthew Wolf, Justin M. Wozniak, Wei Xu, Shinjae Yoo:
Computing Just What You Need: Online Data Analysis and Reduction at Extreme Scales. Euro-Par 2017: 3-19 - [c23]Franck Cappello, Rinku Gupta, Sheng Di, Emil M. Constantinescu, Thomas Peterka, Stefan M. Wild:
Understanding and Improving the Trust in Results of Numerical Simulations and Scientific Data Analytics. Euro-Par Workshops 2017: 545-556 - [c22]Sandeep Madireddy, Prasanna Balaprakash, Philip H. Carns, Robert Latham, Robert B. Ross, Shane Snyder, Stefan M. Wild:
Analysis and Correlation of Application I/O Performance and System-Wide I/O Activity. NAS 2017: 1-10 - 2016
- [c21]E. Wes Bethel, Martin Greenwald, Kerstin Kleese van Dam, Manish Parashar, Stefan M. Wild, H. Steven Wiley:
Management, analysis, and visualization of experimental and observational data - The convergence of data and computing. eScience 2016: 213-222 - [c20]Amit Roy, Prasanna Balaprakash, Paul D. Hovland, Stefan M. Wild:
Exploiting Performance Portability in Search Algorithms for Autotuning. IPDPS Workshops 2016: 1535-1544 - [c19]Victor Picheny, Robert B. Gramacy, Stefan M. Wild, Sébastien Le Digabel:
Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian. NIPS 2016: 1435-1443 - [c18]Mike Fagan, Jeremy Schlachter, Kazutomo Yoshii, Sven Leyffer, Krishna V. Palem, Marc Snir, Stefan M. Wild, Christian C. Enz:
Overcoming the power wall by exploiting inexactness and emerging COTS architectural features: Trading precision for improving application quality. SoCC 2016: 241-246 - [c17]Prasanna Balaprakash, Ananta Tiwari, Stefan M. Wild, Laura Carrington, Paul D. Hovland:
AutoMOMML: Automatic Multi-objective Modeling with Machine Learning. ISC 2016: 219-239 - 2015
- [c16]Florin Isaila, Prasanna Balaprakash, Stefan M. Wild, Dries Kimpe, Robert Latham, Robert B. Ross, Paul D. Hovland:
Collective I/O Tuning Using Analytical and Machine Learning Models. CLUSTER 2015: 128-137 - [c15]Babak Behzad, Surendra Byna, Stefan M. Wild, Prabhat, Marc Snir:
Dynamic Model-Driven Parallel I/O Performance Tuning. CLUSTER 2015: 184-193 - [c14]Azamat Mametjanov, Prasanna Balaprakash, Chekuri Choudary, Paul D. Hovland, Stefan M. Wild, Gerald Sabin:
Autotuning FPGA Design Parameters for Performance and Power. FCCM 2015: 84-91 - [c13]Ashish Tripathi, Sven Leyffer, Todd S. Munson, Stefan M. Wild:
Visualizing and Improving the Robustness of Phase Retrieval Algorithms. ICCS 2015: 815-824 - [c12]Shashi M. Aithal, Stefan M. Wild:
ACCOLADES: A Scalable Workflow Framework for Large-Scale Simulation and Analyses of Automotive Engines. ISC 2015: 87-95 - 2014
- [c11]Leonardo Arturo Bautista-Gomez, Prasanna Balaprakash, Mohamed-Slim Bouguerra, Stefan M. Wild, Franck Cappello, Paul D. Hovland:
Energy-performance tradeoffs in multilevel checkpoint strategies. CLUSTER 2014: 278-279 - [c10]Babak Behzad, Surendra Byna, Stefan M. Wild, Prabhat, Marc Snir:
Improving parallel I/O autotuning with performance modeling. HPDC 2014: 253-256 - [c9]Prasanna Balaprakash, Leonardo Arturo Bautista-Gomez, Mohamed-Slim Bouguerra, Stefan M. Wild, Franck Cappello, Paul D. Hovland:
Analysis of the Tradeoffs Between Energy and Run Time for Multilevel Checkpointing. PMBS@SC 2014: 249-263 - 2013
- [c8]Prasanna Balaprakash, Robert B. Gramacy, Stefan M. Wild:
Active-learning-based surrogate models for empirical performance tuning. CLUSTER 2013: 1-8 - [c7]Prasanna Balaprakash, Karl Rupp, Azamat Mametjanov, Robert B. Gramacy, Paul D. Hovland, Stefan M. Wild:
Empirical performance modeling of GPU kernels using active learning. PARCO 2013: 646-655 - [c6]Prasanna Balaprakash, Ananta Tiwari, Stefan M. Wild:
Multi Objective Optimization of HPC Kernels for Performance, Power, and Energy. PMBS@SC 2013: 239-260 - [c5]Siwei Wang, Jesse Ward, Sven Leyffer, Stefan M. Wild, Chris Jacobsen, Stefan Vogt:
Unsupervised cell identification on multidimensional X-ray fluorescence datasets. SIGGRAPH Posters 2013: 88 - 2012
- [c4]Prasanna Balaprakash, Stefan M. Wild, Paul D. Hovland:
An Experimental Study of Global and Local Search Algorithms in Empirical Performance Tuning. VECPAR 2012: 261-269 - [c3]Prasanna Balaprakash, Stefan M. Wild, Boyana Norris:
SPAPT: Search Problems in Automatic Performance Tuning. ICCS 2012: 1959-1968 - 2011
- [c2]Prasanna Balaprakash, Stefan M. Wild, Paul D. Hovland:
Can search algorithms save large-scale automatic performance tuning? ICCS 2011: 2136-2145 - 2007
- [c1]Tim Carnes, Chandrashekhar Nagarajan, Stefan M. Wild, Anke van Zuylen:
Maximizing influence in a competitive social network: a follower's perspective. ICEC 2007: 351-360
Informal and Other Publications
- 2024
- [i19]Stephen Hudson, Jeffrey Larson, John-Luke Navarro, Stefan M. Wild:
Portable, heterogeneous ensemble workflows at scale using libEnsemble. CoRR abs/2403.03709 (2024) - 2023
- [i18]Aleksandra Ciprijanovic, Ashia Lewis, Kevin Pedro, Sandeep Madireddy, Brian Nord, Gabriel N. Perdue, Stefan M. Wild:
DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection. CoRR abs/2302.02005 (2023) - [i17]Tyler H. Chang, Stefan M. Wild:
Designing a Framework for Solving Multiobjective Simulation Optimization Problems. CoRR abs/2304.06881 (2023) - [i16]Tyler H. Chang, Jakob R. Elias, Stefan M. Wild, Santanu Chaudhuri, Joseph A. Libera:
A framework for fully autonomous design of materials via multiobjective optimization and active learning: challenges and next steps. CoRR abs/2304.07445 (2023) - 2022
- [i15]Abdulkadir Canatar, Evan Peters, Cengiz Pehlevan, Stefan M. Wild, Ruslan Shaydulin:
Bandwidth Enables Generalization in Quantum Kernel Models. CoRR abs/2206.06686 (2022) - [i14]Aleksandra Ciprijanovic, Ashia Lewis, Kevin Pedro, Sandeep Madireddy, Brian Nord, Gabriel N. Perdue, Stefan M. Wild:
Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection. CoRR abs/2211.00677 (2022) - [i13]Lucas Slattery, Ruslan Shaydulin, Shouvanik Chakrabarti, Marco Pistoia, Sami Khairy, Stefan M. Wild:
Numerical evidence against advantage with quantum fidelity kernels on classical data. CoRR abs/2211.16551 (2022) - 2021
- [i12]Arindam Fadikar, Stefan M. Wild, Jonas Chaves-Montero:
Scalable Statistical Inference of Photometric Redshift via Data Subsampling. CoRR abs/2103.16041 (2021) - [i11]Stephen Hudson, Jeffrey Larson, John-Luke Navarro, Stefan M. Wild:
libEnsemble: A Library to Coordinate the Concurrent Evaluation of Dynamic Ensembles of Calculations. CoRR abs/2104.08322 (2021) - [i10]Aydin Buluç, Tamara G. Kolda, Stefan M. Wild, Mihai Anitescu, Anthony M. DeGennaro, John Jakeman, Chandrika Kamath, Ramakrishnan Kannan, Miles E. Lopes, Per-Gunnar Martinsson, Kary L. Myers, Jelani Nelson, Juan M. Restrepo, C. Seshadhri, Draguna L. Vrabie, Brendt Wohlberg, Stephen J. Wright, Chao Yang, Peter Zwart:
Randomized Algorithms for Scientific Computing (RASC). CoRR abs/2104.11079 (2021) - [i9]Raghu Bollapragada, Stefan M. Wild:
Adaptive Sampling Quasi-Newton Methods for Zeroth-Order Stochastic Optimization. CoRR abs/2109.12213 (2021) - [i8]Aleksandra Ciprijanovic, Diana Kafkes, Gabriel N. Perdue, Kevin Pedro, Gregory F. Snyder, F. Javier Sánchez, Sandeep Madireddy, Stefan M. Wild, Brian Nord:
Robustness of deep learning algorithms in astronomy - galaxy morphology studies. CoRR abs/2111.00961 (2021) - [i7]Ruslan Shaydulin, Stefan M. Wild:
Importance of Kernel Bandwidth in Quantum Machine Learning. CoRR abs/2111.05451 (2021) - [i6]Aleksandra Ciprijanovic, Diana Kafkes, Gregory F. Snyder, F. Javier Sánchez, Gabriel Nathan Perdue, Kevin Pedro, Brian Nord, Sandeep Madireddy, Stefan M. Wild:
DeepAdversaries: Examining the Robustness of Deep Learning Models for Galaxy Morphology Classification. CoRR abs/2112.14299 (2021) - 2020
- [i5]Jed Brown, Yunhui He, Scott P. MacLachlan, Matt Menickelly, Stefan M. Wild:
Tuning Multigrid Methods with Robust Optimization. CoRR abs/2001.00887 (2020) - 2019
- [i4]Nathan Wycoff, Mickaël Binois, Stefan M. Wild:
Sequential Learning of Active Subspaces. CoRR abs/1907.11572 (2019) - [i3]Prasanna Balaprakash, Romain Egele, Misha Salim, Stefan M. Wild, Venkatram Vishwanath, Fangfang Xia, Tom Brettin, Rick Stevens:
Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research. CoRR abs/1909.00311 (2019) - 2016
- [i2]Sven Leyffer, Stefan M. Wild, Mike Fagan, Marc Snir, Krishna V. Palem, Kazutomo Yoshii, Hal Finkel:
Doing Moore with Less - Leapfrogging Moore's Law with Inexactness for Supercomputing. CoRR abs/1610.02606 (2016) - 2011
- [i1]Esmond G. Ng, Jason Sarich, Stefan M. Wild, Todd S. Munson, Hasan Metin Aktulga, Chao Yang, Pieter Maris, James P. Vary, Nicolas Schunck, M. G. Bertolli, Markus Kortelainen, Witold Nazarewicz, Thomas Papenbrock, M. V. Stoitsov:
Advancing Nuclear Physics Through TOPS Solvers and Tools. CoRR abs/1110.1708 (2011)
Coauthor Index
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