Álvaro Fernández Casani

arXiv2026

Energy efficiency of a GPU-based computing system for High Energy Physics experiments

In this paper we introduce the energy efficiency as a new metric for evaluating both hardware platforms based on Graphic Processor Units (GPU), and algorithm optimisations at High Energy Physics (HEP) experiments. We develop a method to compute the energy efficiency for the case of the first high level trigger (HLT1) of the LHCb experiment, relating the throughput with GPU specifications such as the number of cores, clock frequency, memory bandwidth and thermal design power. The model can be extended to other HEP experiments to make decisions and reach sustainable computing ecosystems.

hep-ex↗

arXiv2026

Seeding and Matching algorithms for the first GPU-based High Level Trigger of the LHCb experiment

We describe the GPU implementation of the Seeding and Matching algorithms, developed for the first level trigger of the LHCb experiment and key to reconstruct long and very displaced tracks at 40 MHz. The algorithms have been participating in the data taking during the full Run 3 of LHCb with a very high throughput, increasing the physics reach of the experiment. The Seeding is a standalone pattern recognition algorithm aiming at finding charged particle trajectories in the most forward tracker of LHCb. These trajectories are then extrapolated backward by the Matching algorithm which combines them with stubs formed from hits in the first tracker in order to form what we call Long tracks. Hits in the second tracker are then searched for to better define the trajectory and improve the track momentum resolution. This backward approach, complementary to the approach of extrapolating the stubs in the first detector to the forward tracker through the magnetic field, improves the Long track efficiency at low transverse momenta, increasing the potential of key physics decay channels.

physics.ins-det↗