Prof. Allen D. Malony
Professor in the University of Oregon Dept. of Computer & Information Science, and Director of the NeuroInformatics Center (NIC) at the University of Oregon. Founder and CEO of ParaTools, Inc.
First-Author Publications
[2021]
Artemis: Automatic Runtime Tuning of Parallel Execution Parameters Using Machine Learning
Chad Wood, Giorgis Georgakoudis, David Beckingsale, David Poliakoff, Alfredo Gimenez, Kevin Huck, Allen Malony, and Todd Gamblin. International Conference on High-Performance Computing (ISC21).
[2017]
Projecting Performance Data Over Simulation Geometry Using SOSflow and ALPINE
Chad Wood, Matthew Larsen, Alfredo Gimenez, Cyrus Harrison, Todd Gamblin, and Allen Malony.
2017. In Proceedings of the 4th International Workshop on Visual Performance Analysis (VPA17).
[2016]
A Scalable Observation System for Introspection and In Situ Analytics
Chad Wood, Sudhanshu Sane, Daniel Ellsworth, Alfredo Gimenez, Kevin Huck, Todd Gamblin, and Allen Malony.
2016. In Proceedings of the 5th Workshop on Extreme-Scale Programming Tools (ESPT16).
IEEE Press, Piscataway, NJ, USA, 42-49. DOI: 10.1109/ESPT.2016.7
Additional Publications
[2019]
Scalable Performance Awareness For In Situ Scientific Applications
Matthew Wolf, Jong Choi, Greg Eisenhauer, Stéphane Ethier, Kevin Huck,
Scott Klasky, Jeremy Logan, Allen Malony, Chad Wood, Julien Dominski,
and Gabriele Merlo. 2019. 15th International Conference on eScience (eScience).
San Diego, CA, USA. 266-276. DOI: 10.1109/eScience.2019.00037
[2019]
Towards Runtime Analytics in a Parallel Performance System
Allen Malony, Srinivasan Ramesh, Kevin Huck, Chad Wood, and Sameer Shende.
2019. International Conference on High Performance Computing & Simulation (HPCS).
Dublin, Ireland. DOI: 10.1109/HPCS48598.2019.9188097
[2019]
When Parallel Performance Measurement and Analysis Meets In Situ Analytics and Visualization
Allen Malony, Matt Larsen, Kevin Huck, Chad Wood, Sudhanshu Sane, and Hank Childs. 2019.
PARCO, 521-530. DOI: 10.3233/APC200080
[2018]
Coupling Exascale Multiphysics Applications: Methods and Lessons Learned
Jong Youl Choi, Choong-Seock Chang, Julien Dominski, Scott Klasky, Gabriele Merlo,
Eric Suchyta, Mark Ainsworth, Bryce Allen, Franck Cappello, Michael Churchill, Philip Davis,
Sheng Di, Greg Eisenhauer, Stephane Ethier, Ian Foster, Berk Geveci, Hanqi Guo,
Kevin Huck, Frank Jenko, Mark Kim, James Kress, Seung-Hoe Ku, Qing Liu, Jeremy Logan,
Allen Malony, Kshitij Mehta, Kenneth Moreland, Todd Munson, Manish Parashar,
Tom Peterka, Norbert Podhorszki, Dave Pugmire, Ozan Tugluk, Ruonan Wang, Ben Whitney,
Matthew Wolf, Chad Wood. 2018. IEEE 14th International Conference on e-Science
(e-Science). Amsterdam, Netherlands, Netherlands. DOI: 10.1109/eScience.2018.00133
[2017]
ScrubJay: Deriving Knowledge from the Disarray of HPC Performance Data
Alfredo Gimenez, Todd Gamblin, Abhinav Bhatele, Chad Wood, Kathleen Shoga, Aniruddha Marathe,
Peer-Timo Bremer, Bernd Hamann, Martin Schulz. 2017. In Proceedings of the International Conference
for High Performance Computing, Networking, Storage and Analysis (SC17).
ACM New York, NY, USA. DOI: 10.1145/3126908.3126935
[2017]
Extending Skel to Support the Development and Optimization of Next Generation I/O Systems
Jeremy Logan, Jong Youl Choi, Matthew Wolf, George Ostrouchov, Lipeng Wan, Norbert Podhorszki,
William Godoy, Scott Klasky, Erich Lohrmann, Greg Eisenhauer, Chad Wood, Kevin Huck.
2017. IEEE International Conference on Cluster Computing (CLUSTER).
Honolulu, HI, USA. DOI: 10.1109/CLUSTER.2017.30
Research Interests
High Performance Computing (HPC)
Working in the Performance Research Lab
at the University of Oregon, I am the primary developer of the
Scalable Observation System for Scientific Workflows (SOSflow) targeting current and future-scale HPC architectures.
The goal of this effort is to provide robust software infrastructure for in situ analysis of work flows, intelligent real-time work flow adaptation, and data coallation for comprehensive profiling of performance. SOS is being codesigned as both a general-purpose system and a collection of specific modules to facilitate well-established HPC codes.
SOS is designed to enable:
- Unified global views with minimal latency and overhead
- Observation and control from both a systems perspective as well as logical work flow perspectives
- Detailed attribution of the root causes underlying observed performance characteristics
- Information collection, archival, and efficient real-time sharing through a flexible publish / subscribe model
- Event detection and event-triggered behaviors
- Intelligent management of data sampling acoss every source: hardware, OS, libraries, applications, etc.
- Many-[layer/component/channel/epoch] instrumentation and perspective-integration
- Meta-analysis of performance across many work flow sessions and software versions
- ...and much more, all designed especially for exascale (10^9 core) systems.
Research Supervisors
Get In Touch
You are welcome to contact me with any questions or comments!