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Quantum Circuit Discovery for Fault-Tolerant Logical State Preparation with Reinforcement Learning
No full textTaming Thiemann’s Hamiltonian constraint in canonical loop quantum gravity: Reversibility, eigenstates, and graph-change analysis
No full textHigh-definition simulation of packed-bed chromatography in laterally unconfined compartments
No full textInterphase Modification and Investigation by Advanced Operando Raman Spectroscopy in High-Voltage Lithium-Ion Batteries
No full textKaon physics: a cornerstone for future discoveries
No full textThe kaon physics programme, long heralded as a cutting-edge frontier by the European Strategy for Particle Physics, continues to stand at the intersection of discovery and innovation in high-energy physics (HEP). With its unparalleled capacity to explore new physics at the multi-TeV scale, kaon research is poised to unveil phenomena that could reshape our understanding of the Universe. This document highlights the compelling physics case, with emphasis on exciting new opportunities for advancing kaon physics not only in Europe but also on a global stage. As an important player in the future of HEP, the kaon programme promises to drive transformative breakthroughs, inviting exploration at the forefront of scientific discovery
Efficient Massively Space-Time-Parallel Simulations with Adaptive Spectral Deferred Correction
No full textSpectral Deferred Correction (SDC) is a method for numerically integrating initialvalue problems. The method iteratively generates solutions to fully implicit Runge-Kuttamethods with forward substitution using low order solves. This allows great flexibility, forinstance in terms of splitting techniques or inexact solves. Furthermore, various parallel-in-time extensions exist that parallelize the solution of a single time-step or solve multiplesteps concurrently.We propose two adaptive step size selection algorithms that tailor the ideas behindembedded Runge-Kutta methods to SDC. Both are completely generic and work only onintermediate values within the time-integration process. We show, with a range of experi-ments, that computational efficiency can be boosted significantly by employing these algo-rithms compared to standard SDC. Furthermore, we show that parallel-in-time adaptiveSDC is competitive with state-of-the-art Runge-Kutta methods for stiff partial differentialequations.We also show that adaptivity increases the resilience against soft faults in SDC. Softfaults are unanticipated alterations of the data stored in memory, brought about, forinstance, by environmental radiation. Iterative or adaptive methods inherently providean elevated level of resilience, which is well known also in the context of the embeddedRunge-Kutta methods that the adaptive step size selection is based on.We then move on to port implementations of partial differential equations within theprototyping library pySDC to GPUs and make extensive space-time-parallel scaling tests.We find that the parallel-in-time extension diagonal SDC can help extend the scalingcapabilities and allowed to run a Gray-Scott example on 3584 GPUs at decent parallelefficiency. Finally, we demonstrate that findings from the previous experiments translateto practical use via space-time-parallel production runs of Gray-Scott and Rayleigh-Benardconvection using adaptive SDC
A Massively Parallel Performance Portable Free-Space Spectral Poisson Solver
No full textVico et al. suggest a fast algorithm for computing volume potentials, beneficial to fields with problems requiring the solution of the free-space Poisson’s equation, such as beam and plasma physics. Currently, the standard is the algorithm of Hockney and Eastwood, with second order in convergence at best. The algorithm proposed by Vico et al. converges spectrally for sufficiently smooth functions, i.e., faster than any fixed order in the number of grid points. We implement a performance portable version of the traditional Hockney-Eastwood and the novel Vico-Greengard Poisson solver as part of the Independent Parallel Particle Layer (IPPL) library. For sufficiently smooth source functions, the Vico-Greengard algorithm achieves higher accuracy than the Hockney-Eastwood method with the same grid size, reducing the computational demands of high-resolution simulations since one could use coarser grids to achieve them. Additionally, we propose an improvement to the Vico-Greengard method which further reduces its memory footprint. This is important for GPUs, which have limited memory, and should be taken into account when selecting numerical algorithms for performance portable codes. Finally, we showcase performance through GPU and CPU scaling studies on the Perlmutter (NERSC) supercomputer, with efficiencies staying above 50% in the strong scaling case. To showcase portability, we also run the scaling studies on the Alps supercomputer at CSCS, Switzerland and the GPU partition of the Lumi supercomputer at CSC, Finland
