Jan Balewski

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: https://doi.org/10.1101/727974
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: https://doi.org/10.1101/532721
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.
Presentation/Talks
Jan Balewski, Interactive Cori Tutorial for users of LZ-experiment, February 24, 2017,
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,
- Download File: Storage-2020-A-Vision-for-the-Future-of-HPC-Storage.pdf (pdf: 3.6 MB)