Understanding the Impact of openPMD on BIT1, a Particle-in-Cell Monte Carlo Code, through Instrumentation, Monitoring, and In-Situ Analysis
Jeremy J. Williams, Stefan Costea, Allen D. Malony, David Tskhakaya, Leon Kos, Ales Podolnik, Jakub Hromadka, Kevin Huck, Erwin Laure, Stefano Markidis

TL;DR
This paper evaluates how openPMD integration enhances BIT1's data handling, performance monitoring, and in-situ analysis for large-scale plasma simulations, leading to better optimization and understanding of system behavior.
Contribution
It provides a detailed analysis of openPMD's impact on BIT1's instrumentation, performance profiling, and in-situ visualization, advancing simulation efficiency and insight.
Findings
Improved data throughput and storage efficiency in BIT1 with openPMD
Enhanced runtime behavior insights through fine-grained instrumentation
Effective in-situ visualization without disrupting simulations
Abstract
Particle-in-Cell Monte Carlo simulations on large-scale systems play a fundamental role in understanding the complexities of plasma dynamics in fusion devices. Efficient handling and analysis of vast datasets are essential for advancing these simulations. Previously, we addressed this challenge by integrating openPMD with BIT1, a Particle-in-Cell Monte Carlo code, streamlining data streaming and storage. This integration not only enhanced data management but also improved write throughput and storage efficiency. In this work, we delve deeper into the impact of BIT1 openPMD BP4 instrumentation, monitoring, and in-situ analysis. Utilizing cutting-edge profiling and monitoring tools such as gprof, CrayPat, Cray Apprentice2, IPM, and Darshan, we dissect BIT1's performance post-integration, shedding light on computation, communication, and I/O operations. Fine-grained instrumentation offers…
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Taxonomy
TopicsCatalytic Processes in Materials Science · Electron and X-Ray Spectroscopy Techniques
