NERSCPowering Scientific Discovery Since 1974

Jan Balewski

Screen Shot 2016 05 11 at 1.58.54 PM
Jan Balewski Ph.D.
Fax: (510) 486-6459
1 Cyclotron Road
Mailstop: 59R4010A
Berkeley, CA 94720 US

Biographical Sketch

Jan Balewski works at NERSC on quantum computing experiments at AQT/LBNL  as well as on machine learning HPC projects applied to cosmology and neuroscience data. Jan holds a Ph.D. in physics from Jagiellonian Uni, Cracow, Poland.  He worked as a researcher at MIT, LNS, on the dark matter detection experiment  DarkLight at JLAB and on polarized protons experiment STAR at RHIC/BNL. 

Publications & Conferences

The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs with Hybrid Parallelism
Dec 31, 2020 , IEEE Transactions on Parallel & Distributed Systems, vol. , no. 01, pp. 1-1, 5555

QubiC - Qubits Control Systems at LBNL
Nov 11, 2020 , Supercomputing Conference, SC20

STAR Data Production Workflow on HPC: Lessons Learned & Best Practices
Apr 4, 2020 Journal of Physics: Conference Series, DOI: 10.1088/1742-6596/1525/1/012068

Inferring neuronal ionic conductances from membrane potentials using CNNs
Aug 6, 2019 , bioRxiv 727974; doi:

Design and Operation of a Windowless Gas Target Internal to a Solenoidal Magnet for Use with a Megawatt Electron Beam
May 30, 2019 ,Elsevier BV, arXiv:1903.02648

Accurate prediction of bacterial two-component signaling with a deep recurrent neural network ORAKLE
Jan 28, 2019 , bioRxiv doi:

Precision Measurement of the Weak Charge of the Proton
May 9, 2018 , Nature volume 557, pages 207–211(2018)

STAR Data Reconstruction at NERSC/Cori, an adaptable Docker container approach for HPC
Oct 1, 2017 Journal of Physics: Conference Series, 898 082023

Measurement of Longitudinal Spin Asymmetries for Weak Boson Production in Polarized Proton-Proton Collisions at RHIC
Aug 13, 2014 , PhysRevLett.113.072301


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.


Tutorial w/ handouts. use of Shifter w/ image of chos=sl64 from PDSF Download the slides at



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.