GPU Day 2026

Session

Session IV

28 May 2026, 15:50

HUN-REN Centre

191. Performance Analysis and Optimization of a Parallel GPU-accelerated Low-Temperature 2D Particle-in-Cell Plasma Simulation Code

Zoltan Juhasz (Pannon Egyetem)

28/05/2026, 15:50

Lecture

In this talk, we plan to report the results of our performance optimisation effort aimed at speeding up a GPU-accelerated 2D Particle-in-Cell plasma simulation code, following an international plasma simulation benchmarking effort of 19 leading plasma research groups. We studied the effects of memory management, data movements, the choice and implementation of the Poisson solver, the use of...

190. Design and analysis of GPU-accelerated 2D Poisson solvers for plasma simulation

Bálint Tóth (University of Pannonia)

28/05/2026, 16:10

Lecture

Particle-in-Cell (PIC) simulation is an important tool in plasma science, where certain properties and behaviour can only be examined by simulations. Due to the large number of particles and simulation cycles, these simulations are extremely time-consuming and can be executed in acceptable time only with parallel implementations. A crucial step in the simulation is solving the Poisson equation...

194. Nuclear structure driven anisotropic flow coefficients in pO and OO collisions at the LHC energies

Suraj Prasad (HUN-REN Wigner Research Centre for Physics)

28/05/2026, 16:30

Lecture

One of the major open problems in the collider physics community is understanding the onset of quark–gluon plasma (QGP) signatures. Collisions of Oxygen nuclei provide a golden opportunity to probe the emergence of collective phenomena in collider experiments. Additionally, $^{16}$O nuclei are theorized to possess a clustered nuclear structure, where α-particles occupy the corners of a regular...

208. TBA

28/05/2026, 16:50

207. Towards Cost-Effective HEP Simulations Using GAN-Based Data Augmentation

Anisa Khatun

28/05/2026, 17:10

Lecture

Modern high-energy physics analyses rely heavily on large-scale Monte Carlo (MC)
simulations for machine-learning training, efficiency corrections, and systematic
studies. For rare-signal workflows, obtaining sufficiently large reconstructed-level
signal samples often require computationally expensive MC campaigns with large
CPU and storage demands.
This work explores the use of...