NERSCPowering Scientific Discovery Since 1974

Jan Balewski

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Jan Balewski Ph.D.
PDSF Consultant
Phone: (812) 606.5607
Fax: (510) 486-6459
1 Cyclotron Road
Mailstop: 59R4010A
Berkeley, CA 94720 US

 

Biographical Sketch

Jan Balewski is a PDSF Consultant in the Data Science Engagement Group. He obtained his PhD in Physics from the Jagiellonian University in Krakow, Poland, and worked as a research scientist at the Indiana University Cyclotron Facility and the Massachusetts Institute of Technology. He worked on the design and analysis of multiple accelerator experiments at COSY in Jülich, Germany, RHIC at the Brookhaven National Lab, and LERF at Jefferson Lab.

 

Conference Papers

Glenn K. Lockwood, Wucherl Yoo, Suren Byna, Nicholas J. Wright, Shane Snyder, Kevin Harms, Zachary Nault, Philip Carns, "UMAMI: a recipe for generating meaningful metrics through holistic I/O performance analysis", Proceedings of the 2nd Joint International Workshop on Parallel Data Storage & Data Intensive Scalable Computing Systems (PDSW-DISCS'17), Denver, CO, ACM, November 2017, 55-60, doi: 10.1145/3149393.3149395

I/O efficiency is essential to productivity in scientific computing, especially as many scientific domains become more data-intensive. Many characterization tools have been used to elucidate specific aspects of parallel I/O performance, but analyzing components of complex I/O subsystems in isolation fails to provide insight into critical questions: how do the I/O components interact, what are reasonable expectations for application performance, and what are the underlying causes of I/O performance problems? To address these questions while capitalizing on existing component-level characterization tools, we propose an approach that combines on-demand, modular synthesis of I/O characterization data into a unified monitoring and metrics interface (UMAMI) to provide a normalized, holistic view of I/O behavior.

We evaluate the feasibility of this approach by applying it to a month-long benchmarking study on two distinct large-scale computing platforms. We present three case studies that highlight the importance of analyzing application I/O performance in context with both contemporaneous and historical component metrics, and we provide new insights into the factors affecting I/O performance. By demonstrating the generality of our approach, we lay the groundwork for a production-grade framework for holistic I/O analysis.

Presentation/Talks

Tutorial w/ handouts. use of Shifter w/ image of chos=sl64 from PDSF Download the slides at https://docs.google.com/presentation/d/1Hh8vFE3ixxxiYTz9TgfljbUJcjmWUCNwzs-NugmLjSs/edit?usp=sharing

 

Reports

Glenn K. Lockwood, Damian Hazen, Quincey Koziol, Shane Canon, Katie Antypas, Jan Balewski, Nicholas Balthaser, Wahid Bhimji, James Botts, Jeff Broughton, Tina L. Butler, Gregory F. Butler, Ravi Cheema, Christopher Daley, Tina Declerck, Lisa Gerhardt, Wayne E. Hurlbert, Kristy A. Kallback-
Rose, Stephen Leak, Jason Lee, Rei Lee, Jialin Liu, Kirill Lozinskiy, David Paul, Prabhat, Cory Snavely, Jay Srinivasan, Tavia Stone Gibbins, Nicholas J. Wright,
"Storage 2020: A Vision for the Future of HPC Storage", October 20, 2017, LBNL LBNL-2001072,

As the DOE Office of Science's mission computing facility, NERSC will follow this roadmap and deploy these new storage technologies to continue delivering storage resources that meet the needs of its broad user community. NERSC's diversity of workflows encompass significant portions of open science workloads as well, and the findings presented in this report are also intended to be a blueprint for how the evolving storage landscape can be best utilized by the greater HPC community. Executing the strategy presented here will ensure that emerging I/O technologies will be both applicable to and effective in enabling scientific discovery through extreme-scale simulation and data analysis in the coming decade.