High-Performance Implementation of the Optimized Event Generator for Strong-Field QED Plasma Simulations
September 25, 2024 ยท Declared Dead ยท ๐ Parallel Processing and Applied Mathematics
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Elena Panova, Valentin Volokitin, Aleksei Bashinov, Alexander Muraviev, Evgeny Efimenko, Iosif Meyerov
arXiv ID
2409.17299
Category
physics.comp-ph
Cross-listed
cs.DC
Citations
0
Venue
Parallel Processing and Applied Mathematics
Last Checked
1 month ago
Abstract
Numerical simulation of strong-field quantum electrodynamics (SFQED) processes is an essential step towards current and future high-intensity laser experiments. The complexity of SFQED phenomena and their stochastic nature make them extremely computationally challenging, requiring the use of supercomputers for realistic simulations. Recently, we have presented a novel approach to numerical simulation of SFQED processes based on an accurate approximation of precomputed rates, which minimizes the number of rate calculations per QED event. The current paper is focused on the high-performance implementation of this method, including vectorization of resource-intensive kernels and improvement of parallel computing efficiency. Using two codes, PICADOR and hi-$ฯ$ (the latter being free and publicly available), we demonstrate significant reduction in computation time due to these improvements. We hope that the proposed approach can be applied in other codes for the numerical simulation of SFQED processes.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ physics.comp-ph
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics
R.I.P.
๐ป
Ghosted
Heterogeneous Parallelization and Acceleration of Molecular Dynamics Simulations in GROMACS
R.I.P.
๐ป
Ghosted
By-passing the Kohn-Sham equations with machine learning
R.I.P.
๐ป
Ghosted
Machine Learning of coarse-grained Molecular Dynamics Force Fields
R.I.P.
๐ป
Ghosted
Towards Physics-informed Deep Learning for Turbulent Flow Prediction
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Language Models are Few-Shot Learners
R.I.P.
๐ป
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
๐ป
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
๐ป
Ghosted