Understanding the Impact of openPMD on BIT1, a Particle-in-Cell Monte Carlo Code, through Instrumentation, Monitoring, and In-Situ Analysis
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916794553335808 |
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| author | Williams, Jeremy J. Costea, Stefan Malony, Allen D. Tskhakaya, David Kos, Leon Podolnik, Ales Hromadka, Jakub Huck, Kevin Laure, Erwin Markidis, Stefano |
| author_facet | Williams, Jeremy J. Costea, Stefan Malony, Allen D. Tskhakaya, David Kos, Leon Podolnik, Ales Hromadka, Jakub Huck, Kevin Laure, Erwin Markidis, Stefano |
| contents | 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 insights into BIT1's runtime behavior, while immediate monitoring aids in understanding system dynamics and resource utilization patterns, facilitating proactive performance optimization. Advanced visualization techniques further enrich our understanding, enabling the optimization of BIT1 simulation workflows aimed at controlling plasma-material interfaces with improved data analysis and visualization at every checkpoint without causing any interruption to the simulation. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2406_19058 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Understanding the Impact of openPMD on BIT1, a Particle-in-Cell Monte Carlo Code, through Instrumentation, Monitoring, and In-Situ Analysis Williams, Jeremy J. Costea, Stefan Malony, Allen D. Tskhakaya, David Kos, Leon Podolnik, Ales Hromadka, Jakub Huck, Kevin Laure, Erwin Markidis, Stefano Computational Physics Distributed, Parallel, and Cluster Computing Performance Plasma Physics 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 insights into BIT1's runtime behavior, while immediate monitoring aids in understanding system dynamics and resource utilization patterns, facilitating proactive performance optimization. Advanced visualization techniques further enrich our understanding, enabling the optimization of BIT1 simulation workflows aimed at controlling plasma-material interfaces with improved data analysis and visualization at every checkpoint without causing any interruption to the simulation. |
| title | Understanding the Impact of openPMD on BIT1, a Particle-in-Cell Monte Carlo Code, through Instrumentation, Monitoring, and In-Situ Analysis |
| topic | Computational Physics Distributed, Parallel, and Cluster Computing Performance Plasma Physics |
| url | https://arxiv.org/abs/2406.19058 |