Merge pull request #821 from Parallel-in-Time/bibtex-bibbot-820-cb3ddf6

pint.bib updates

_bibliography/pint.bib

@@ -7082,6 +7082,15 @@ @article{MiaoEtAl2024b
 	year = {2024},
 }
 
+@unpublished{MuralikrishnanEtAl2024,
+	abstract = {We propose ParaPIF, a parareal based time parallelization scheme for the particle-in-Fourier (PIF) discretization of the Vlasov-Poisson system used in kinetic plasma simulations. Our coarse propagators are based on the coarsening of the numerical discretization scheme combined with, if possible, temporal coarsening rather than coarsening of particles and/or Fourier modes, which are not possible or effective for PIF schemes. Specifically, we use PIF with a coarse tolerance for nonuniform FFTs or even the standard particle-in-cell schemes as coarse propagators for the ParaPIF algorithm. We state and prove the convergence of the algorithm and verify the results numerically with Landau damping, two-stream instability, and Penning trap test cases in 3D-3V. We also implement the space-time parallelization of the PIF schemes in the open-source, performance-portable library IPPL and conduct scaling studies up to 1536 A100 GPUs on the JUWELS booster supercomputer. The space-time parallelization utilizing the ParaPIF algorithm for the time parallelization provides up to $4-6$ times speedup compared to spatial parallelization alone and achieves a push rate of around 1 billion particles per second for the benchmark plasma mini-apps considered.},
+	author = {Sriramkrishnan Muralikrishnan and Robert Speck},
+	howpublished = {arXiv:2407.00485v1 [math.NA]},
+	title = {ParaPIF: A Parareal Approach for Parallel-in-Time Integration of Particle-in-Fourier schemes},
+	url = {http://arxiv.org/abs/2407.00485v1},
+	year = {2024},
+}
+
 @unpublished{PamelaEtAl2024,
 	abstract = {The fusion research facility ITER is currently being assembled to demonstrate that fusion can be used for industrial energy production, while several other programmes across the world are also moving forward, such as EU-DEMO, CFETR, SPARC and STEP. The high engineering complexity of a tokamak makes it an extremely challenging device to optimise, and test-based optimisation would be too slow and too costly. Instead, digital design and optimisation must be favored, which requires strongly-coupled suites of High-Performance Computing calculations. In this context, having surrogate models to provide quick estimates with uncertainty quantification is essential to explore and optimise new design options. Furthermore, these surrogates can in turn be used to accelerate simulations in the first place. This is the case of Parareal, a time-parallelisation method that can speed-up large HPC simulations, where the coarse-solver can be replaced by a surrogate. A novel framework, Neural-Parareal, is developed to integrate the training of neural operators dynamically as more data becomes available. For a given input-parameter domain, as more simulations are being run with Parareal, the large amount of data generated by the algorithm is used to train new surrogate models to be used as coarse-solvers for future Parareal simulations, leading to progressively more accurate coarse-solvers, and thus higher speed-up. It is found that such neural network surrogates can be much more effective than traditional coarse-solver in providing a speed-up with Parareal. This study is a demonstration of the convergence of HPC and AI which simply has to become common practice in the world of digital engineering design.},
 	author = {S. J. P. Pamela and N. Carey and J. Brandstetter and R. Akers and L. Zanisi and J. Buchanan and V. Gopakumar and M. Hoelzl and G. Huijsmans and K. Pentland and T. James and G. Antonucci and the JOREK Team},