The on-going work presented at ISGC25 Physics and Engineering track and in this article explores
different technical approaches for management and analysis of data obtained from large physics
simulations, with the goal of optimizing data-driven workflows across Cloud-Computing (IaaS)
and HPC systems. The work is connected to research activities on database usage in the context
of High Performance Computing conducted through the EXA4MIND Horizon Europe project.
The case presented here, is a large-scale parameter study of plasma physics simulations carried
out on supercomputing systems at LRZ (Garching b.M., DE). It uses the Particle-in-Cell [4]
code PSC [7], using the FDTD [16] method for electric fields, to simulate the movement of
ionized hydrogen plasma , i.e. protons and the corresponding electrons, in a small (nano- to
micrometer scale) amount of matter (”mass limited target”, MLT) that is driven to very high
energies by irradiation with ultra-intense femtosecond laser pulses. When evaluating the large
amount (approx. 50 TB) of simulation output consisting mostly of particle trajectories, much
work goes into postprocessing and re-assessing the data. This typically happens several times in a
repeating process of varying parameters for processing, like time step selection, cut-off thresholds
and plotting style adjustments. The goal is an integrated and comprehensive methodology to
facilitate such a re-use of the data with an eye on FAIR research data management is our final
objective.