EDBT 2026 Demo / reviewers in the wild / expert

Matthew L. Leininger

dblp:80/3835 · DBLP profile ↗
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7ranked-venue papers
0as first author
2since 2021 · last 2025
0009-0000-1286-7822ORCID · corroborated

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

Systems, architecture and hardware · 7 · 2 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
6 papers
High-performance computing · 36% Interconnection networks and networks-on-chip · 33% Performance modeling and evaluation · 31%
Software engineering, system software, and programming languages
2 papers
Operating systems · 100%

Topics — the 11 heaviest of 14, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
High-performance computing › supercomputing
exascale computing		 0.912025
Breaking the System Noise Barrier at Exascale · SC 2025
Interconnection networks and networks-on-chip › network topology › tree networks
fat-tree network		 0.522017
Predicting the performance impact of different fat-tree configurations · SC 2017
Characterizing parallel scientific applications on commodity clusters: an empirical study of a tapered fat-tree · SC 2016
Interconnection networks and networks-on-chip › cluster interconnect
infiniband		 0.412019
An evaluation of the CORAL interconnects · SC 2019
Performance modeling and evaluation › benchmarking
interconnect benchmarking		 0.412019
An evaluation of the CORAL interconnects · SC 2019
Interconnection networks and networks-on-chip › high-speed networks
supercomputer interconnect		 0.412019
An evaluation of the CORAL interconnects · SC 2019
High-performance computing › supercomputing
supercomputer deployment		 0.312018
The design, deployment, and evaluation of the CORAL pre-exascale systems · SC 2018
Interconnection networks and networks-on-chip › network reconfiguration
network configuration		 0.312017
Predicting the performance impact of different fat-tree configurations · SC 2017
Performance modeling and evaluation › simulation › communication system simulation
network simulation		 0.312017
Predicting the performance impact of different fat-tree configurations · SC 2017
Performance modeling and evaluation
workload characterization		 0.212016
Characterizing parallel scientific applications on commodity clusters: an empirical study of a tapered fat-tree · SC 2016
High-performance computing › supercomputing
supercomputing systems		 0.112019
An evaluation of the CORAL interconnects · SC 2019
Performance modeling and evaluation
benchmarking		 0.112018
The design, deployment, and evaluation of the CORAL pre-exascale systems · SC 2018

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

performance analysis · 1.7performance evaluation · 0.9communication benchmarking · 0.4TraceR-CODES simulation · 0.3trace analysis · 0.2empirical study · 0.2
YearPublicationVenuePosition
2025 Breaking the System Noise Barrier at Exascale
abstract
To meet the increasing demands of parallel scientific applications, supercomputers continue to grow in both scale and complexity. The fastest supercomputer in the world, El Capitan, features over a million CPU cores and tens of thousands of GPUs. Applications running on such large-scale systems are particularly susceptible to system noise or interference caused by the operating system (OS) and other services running on the same compute nodes as the application.
Edgar A. León, Joseph Glenski, Mark J. Stock, Kim H. McMahon, William Loewe, Clark Snyder, Larry Kaplan, Srinath Vadlamani, Timothy I. Mattox, Trent D'Hooge, Brian Behlendorf, Nathan Hanford, Ramesh Pankajakshan, Matthew L. Leininger
 SC14
2024 Quantum-centric supercomputing for materials science: A perspective on challenges and future directions
Yuri Alexeev, Maximilian Amsler, Marco Antonio Barroca, Sanzio Bassini, Torey Battelle, Daan Camps, David Casanova, Young Jay Choi, Fred Chong, Charles Chung, Christopher Codella, Antonio D. Córcoles, James Cruise, Alberto Di Meglio, Ivan Duran, Thomas Eckl, Sophia E. Economou, Stephan J. Eidenbenz, Bruce Elmegreen, Clyde Fare, Ismael Faro, Cristina Sanz Fernández, Rodrigo Neumann Barros Ferreira, Keisuke Fuji, Bryce Fuller, Laura Gagliardi, Giulia Galli, Jennifer R. Glick, Isacco Gobbi, Pranav Gokhale, Salvador de la Puente Gonzalez, Johannes Greiner, William Gropp, Michele Grossi, Emanuel Gull, Burns Healy, Matthew R. Hermes, Benchen Huang, Travis S. Humble, Nobuyasu Ito, Artur F. Izmaylov, Ali Javadi-Abhari, Douglas M. Jennewein, Shantenu Jha, Bert de Jong, Petar Jurcevic, William M. Kirby, Stefan Kister, Masahiro Kitagawa, Joel Klassen, Katherine Klymko, Kwangwon Koh, Masaaki Kondo, Doga Murat Kürkçüoglu, Krzysztof Kurowski, Teodoro Laino, Ryan Landfield, Matthew L. Leininger, Vicente Leyton-Ortega, Ang Li 0006, Meifeng Lin, Junyu Liu, Nicolás Lorente, André Luckow, Simon Martiel, Francisco Martín-Fernández, Margaret Martonosi, Claire Marvinney, Arcesio Castañeda Medina, Dirk Merten, Antonio Mezzacapo, Kristel Michielsen, Abhishek Mitra, Tushar Mittal, Kyungsun Moon, Joel Moore, Sarah Mostame, Mario Motta, Young-Hye Na, Yunseong Nam, Prineha Narang, Yu-ya Ohnishi, Daniele Ottaviani, Matthew Otten, Scott Pakin, Vincent R. Pascuzzi, Edwin Pednault, Tomasz Piontek, Jed W. Pitera, Patrick Rall, Gokul Subramanian Ravi, Niall Robertson, Matteo A. C. Rossi, Piotr Rydlichowski, Hoon Ryu, Georgy Samsonidze, Mitsuhisa Sato, Nishant Saurabh, Kunal Sharma, Soyoung Shin, George Slessman, Mathias Steiner, Iskandar Sitdikov, In-Saeng Suh, Eric D. Switzer, Joel Thompson, Synge Todo, Minh C. Tran, Dimitar Trenev, Christian Trott, Huan-Hsin Tseng, Norm M. Tubman, Esin Tureci, David García Valiñas, Sofia Vallecorsa, Christopher Wever, Konrad W. Wojciechowski, Xiaodi Wu 0001, Shinjae Yoo, Nobuyuki Yoshioka, Victor Wen-zhe Yu, Seiji Yunoki, Sergiy Zhuk, Dmitry Zubarev
 Future Gener. Comput. Syst.60
2020 TOSS-2020: a commodity software stack for HPC
abstract
The simulation environment of any HPC platform is key to the performance, portability, and productivity of scientific applications. This environment has traditionally been provided by platform vendors, presenting challenges for HPC centers and users including platform-specific software that tend to stagnate over the lifetime of the system. In this paper, we present the Tri-Laboratory Operating System Stack (TOSS), a production simulation environment based on Linux and open source software, with proprietary software components integrated as needed. TOSS, focused on mid-to-large scale commodity HPC systems, provides a common simulation environment across system architectures, reduces the learning curve on new systems, and benefits from a lineage of past experience and bug fixes. To further the scope and applicability of TOSS, we demonstrate its feasibility and effectiveness on a leadership-class supercomputer architecture. Our evaluation, relative to the vendor stack, includes an analysis of resource manager complexity, system noise, networking, and application performance.
Edgar A. León, Trent D'Hooge, Nathan Hanford, Ian Karlin, Ramesh Pankajakshan, Jim Foraker, Christopher M. Chambreau, Matthew L. Leininger
 SC8
2019 An evaluation of the CORAL interconnects
abstract
The US Department of Energy deployed the Summit and Sierra supercomputers with the latest state-of-the-art network interconnect technology in 2018 and both systems entered production in 2019. In this paper, we provide an in-depth assessment of the systems' network interconnects that are based on Enhanced Data Rate (EDR) 100 Gb/s Mellanox InfiniBand. Both systems use second-generation EDR Host Channel Adapters (HCAs) and switches with several new features such as Adaptive Routing (AR), switch-based collectives, and HCA-based tag matching. Although based on the same components, Summit's network is "non-blocking" (i.e., a fully provisioned Clos network) and Sierra's network has a 2:1 taper between the racks and aggregation switches. We evaluate the two systems' interconnects using traditional communication benchmarks as well as production applications. We find that the new Adaptive Routing dramatically improves performance but the other new features still need improvement.
Christopher Zimmer 0001, Scott Atchley, Ramesh Pankajakshan, Brian E. Smith, Ian Karlin, Matthew L. Leininger, Adam Bertsch, Brian S. Ryujin, Jason Burmark, André Walker-Loud, Michael A. Clark, Olga Pearce
 SC6
2018 The design, deployment, and evaluation of the CORAL pre-exascale systems
Sudharshan S. Vazhkudai, Bronis R. de Supinski, Arthur S. Bland, Al Geist, James C. Sexton, James A. Kahle, Christopher Zimmer 0001, Scott Atchley, Sarp Oral, Don E. Maxwell, Verónica G. Vergara Larrea, Adam Bertsch, Robin Goldstone, Wayne Joubert, Christopher M. Chambreau, David Appelhans, Robert Blackmore, Ben Casses, George Chochia, Gene Davison, Matthew Ezell, Thomas Gooding, Elsa Gonsiorowski, Leopold Grinberg, Bill Hanson, Bill Hartner, Ian Karlin, Matthew L. Leininger, Dustin Leverman, Chris Marroquin, Adam Moody, Martin Ohmacht, Ramesh Pankajakshan, Fernando Pizzano, James H. Rogers, Bryan S. Rosenburg, Drew Schmidt, Mallikarjun Shankar, Feiyi Wang, Py Watson, Bob Walkup, Lance D. Weems, Junqi Yin
 SC28
2017 Predicting the performance impact of different fat-tree configurations
abstract
The fat-tree topology is one of the most commonly used network topologies in HPC systems. Vendors support several options that can be configured when deploying fat-tree networks on production systems, such as link bandwidth, number of rails, number of planes, and tapering. This paper showcases the use of simulations to compare the impact of these design options on representative production HPC applications, libraries, and multi-job workloads. We present advances in the TraceR-CODES simulation framework that enable this analysis and evaluate its prediction accuracy against experiments on a production fat-tree network. In order to understand the impact of different network configurations on various anticipated scenarios, we study workloads with different communication patterns, computation-to-communication ratios, and scaling characteristics. Using multi-job workloads, we also study the impact of inter-job interference on performance and compare the cost-performance tradeoffs.
Abhinav Bhatele, Louis H. Howell, David Böhme, Ian Karlin, Edgar A. León, Misbah Mubarak, Noah Wolfe, Todd Gamblin, Matthew L. Leininger
 SC10
2016 Characterizing parallel scientific applications on commodity clusters: an empirical study of a tapered fat-tree
abstract
Understanding the characteristics and requirements of applications that run on commodity clusters is key to properly configuring current machines and, more importantly, procuring future systems effectively. There are only a few studies, however, that are current and characterize realistic workloads. For HPC practitioners and researchers, this limits our ability to design solutions that will have an impact on real systems. We present a systematic study that characterizes applications with an emphasis on communication requirements. It includes cluster utilization data, identifying a representative set of applications from a U.S. Department of Energy laboratory, and characterizing their communication requirements. The driver for this work is understanding application sensitivity to a tapered fat-tree network. These results provided key insights into the procurement of our next generation commodity systems. We believe this investigation can provide valuable input to the HPC community in terms of workload characterization and requirements from a large supercomputing center.
Edgar A. León, Ian Karlin, Abhinav Bhatele, Steve H. Langer, Christopher M. Chambreau, Louis H. Howell, Trent D'Hooge, Matthew L. Leininger
 SC8