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Max P. Katz

dblp:284/4988 · also Maximilian Katz · DBLP profile ↗
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3ranked-venue papers
1as first author
1since 2021 · last 2021
0000-0003-0439-4556ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 3 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
3 papers
High-performance computing · 76% Parallel and multicore computing · 14% Hardware accelerators and domain-specific architectures · 6%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 10 heaviest of 11, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
drug discovery		 0.512021
High-throughput virtual screening of small molecule inhibitors for SARS-CoV-2 protein targets with deep fusion models · SC 2021
Bioinformatics and computational biology › molecular property prediction
protein-ligand binding affinity prediction		 0.512021
High-throughput virtual screening of small molecule inhibitors for SARS-CoV-2 protein targets with deep fusion models · SC 2021
Bioinformatics and computational biology › drug discovery
virtual screening		 0.512021
High-throughput virtual screening of small molecule inhibitors for SARS-CoV-2 protein targets with deep fusion models · SC 2021
High-performance computing › supercomputing
exascale computing		 0.412020
Preparing nuclear astrophysics for exascale · SC 2020
High-performance computing › performance engineering
performance portability		 0.412020
Preparing nuclear astrophysics for exascale · SC 2020
High-performance computing
scientific computing		 0.412020
Preparing nuclear astrophysics for exascale · SC 2020
High-performance computing
application porting		 0.412019
Preparation and optimization of a diverse workload for a large-scale heterogeneous system · SC 2019
Parallel and multicore computing
programming models		 0.412019
Preparation and optimization of a diverse workload for a large-scale heterogeneous system · SC 2019
Hardware accelerators and domain-specific architectures
machine learning accelerator		 0.112021
High-throughput virtual screening of small molecule inhibitors for SARS-CoV-2 protein targets with deep fusion models · SC 2021
Performance modeling and evaluation
benchmarking		 0.112019
Preparation and optimization of a diverse workload for a large-scale heterogeneous system · SC 2019

Methods — techniques the papers use, named apart from their topics

genetic hyperparameter optimization · 1.0distributed training · 1.0deep fusion models · 1.0
YearPublicationVenuePosition
2021 High-throughput virtual screening of small molecule inhibitors for SARS-CoV-2 protein targets with deep fusion models
abstract
Structure-based Deep Fusion models were recently shown to outperform several physics- and machine learning-based protein-ligand binding affinity prediction methods. As part of a multi-institutional COVID-19 pandemic response, over 500 million small molecules were computationally screened against four protein structures from the novel coronavirus (SARS-CoV-2), which causes COVID-19. Three enhancements to Deep Fusion were made in order to evaluate more than 5 billion docked poses on SARS-CoV-2 protein targets. First, the Deep Fusion concept was refined by formulating the architecture as one, coherently backpropagated model (Coherent Fusion) to improve binding-affinity prediction accuracy. Secondly, the model was trained using a distributed, genetic hyper-parameter optimization. Finally, a scalable, high-throughput screening capability was developed to maximize the number of ligands evaluated and expedite the path to experimental evaluation. In this work, we present both the methods developed for machine learning-based high-throughput screening and results from using our computational pipeline to find SARS-CoV-2 inhibitors.
Garrett Stevenson, Derek Jones, Hyojin Kim 0001, W. F. Drew Bennett, Brian J. Bennion, Monica Borucki, Feliza Bourguet, Aidan Epstein, Magdalena Franco, Brooke Harmon, Stewart He, Max P. Katz, Daniel A. Kirshner, Victoria Lao, Edmond Y. Lau, Jacky Lo, Kevin McLoughlin, Richard Mosesso, Deepa K. Murugesh, Oscar A. Negrete, Edwin A. Saada, Brent Segelke, Maxwell Stefan, Marisa W. Torres, Dina Weilhammer, Sergio Ernesto Wong, Yue Yang 0034, Adam T. Zemla, Fangqiang Zhu, Felice C. Lightstone, Jonathan E. Allen
 SC12
2020 Preparing nuclear astrophysics for exascale
abstract
Astrophysical explosions such as supernovae are fascinating events that require sophisticated algorithms and substantial computational power to model. Castro and MAESTROeX are nuclear astrophysics codes that simulate thermonuclear fusion in the context of supernovae and X-ray bursts. Examining these nuclear burning processes using high resolution simulations is critical for understanding how these astrophysical explosions occur. In this paper we describe the changes that have been made to these codes to transform them from standard MPI + OpenMP codes targeted at petascale CPU-based systems into a form compatible with the pre-exascale systems now online and the exascale systems coming soon. We then discuss what new science is possible to run on systems such as Summit and Perlmutter that could not have been achieved on the previous generation of supercomputers.
Max P. Katz, Ann S. Almgren, Maria Barrios Sazo, Kiran Eiden, Kevin Gott, Alice Harpole, Jean M. Sexton, Donald E. Willcox, Weiqun Zhang, Michael Zingale
 SC1
2019 Preparation and optimization of a diverse workload for a large-scale heterogeneous system
abstract
Productivity from day one on supercomputers that leverage new technologies requires significant preparation. An institution that procures a novel system architecture often lacks sufficient institutional knowledge and skills to prepare for it. Thus, the "Center of Excellence" (CoE) concept has emerged to prepare for systems such as Summit and Sierra, currently the top two systems in the Top 500. This paper documents CoE experiences that prepared a workload of diverse applications and math libraries for a heterogeneous system. We describe our approach to this preparation, including our management and execution strategies, and detail our experiences with and reasons for using different programming approaches. Our early science and performance results show that the project enabled significant early seismic science with up to a l4X throughput increase over Cori. In addition to our successes, we discuss our challenges and failures so others may benefit from our experience.
Ian Karlin, Yoonho Park, Bronis R. de Supinski, Bert Still, D. A. Beckingsale, Robert Blake, Tong Chen 0001, Guojing Cong, Carlos H. A. Costa, Johann Dahm, Giacomo Domeniconi, Thomas Epperly, Aaron Fisher, Sara Kokkila Schumacher, Steve H. Langer, Hai Le, Naoya Maruyama, Xinyu Que, David F. Richards, Björn Sjögreen, Jonathan Wong, Carol S. Woodward, Ulrike Meier Yang, Bob Anderson, David Appelhans, Levi Barnes, Peter D. Barnes Jr., Sorin Bastea, David Böhme, Jamie A. Bramwell, James M. Brase, José R. Brunheroto, Barry Chen, Charway R. Cooper, Tony Degroot, Robert D. Falgout, Todd Gamblin, David J. Gardner, James N. Glosli, John A. Gunnels, Max P. Katz, Tzanio V. Kolev, I-Feng W. Kuo, Matthew P. LeGendre, Pei-Hung Lin, Shelby Lockhart, Kathleen McCandless, Claudia Misale, Jaime H. Moreno, Rob Neely, Jarom Nelson, Rao Nimmakayala, Kathryn M. O'Brien, Kevin O'Brien, Ramesh Pankajakshan, Roger A. Pearce, Slaven Peles, Phil Regier, Steven C. Rennich, Martin Schulz 0001, Howard Scott, James C. Sexton, Kathleen Shoga, Shiv Sundram, Guillaume Thomas-Collignon, Brian Van Essen, Alexey Voronin, Bob Walkup, Chris Ward, Hui-Fang Wen, Daniel A. White, Christopher Young, Cyril Zeller, Edward Zywicz
 SC44