Early Track Elimination in GPU Accelerated Algorithms for Track Finding in Particle Physics

AbstraktAbstract

Track finding in particle physics has been an increasing challenge over the past decades because the intensities of collisions in state-of-the-art particle colliders have increased enormously. The particle physics experiments ATLAS and CMS at the CERN LHC have been recording data since 2010. The analysis of the data includes finding the particle tracks. For this, dedicated track reconstruction algorithms were developed and applied. One of the track reconstruction steps is the track finding. For this, track seeds are determined and the tracks are successively reconstructed by adding more measurements. The track finding becomes more complex with an increasing number of concurrently existing tracks created by particles that need to be reconstructed. In the past decade, the intensities of the proton-proton collisions at the LHC increased by more than a factor of ten. The time needed for the track finding increased exponentially due to its combinatorial complexity. The next major challenge for track finding will be the HLLHC operation, with an anticipated 200 concurrent collisions every 25 nanoseconds compared to the current 60 collisions. In this paper, we explain the main ideas of the state-of-the-art GPU-accelerated implementation of the track finding algorithm and describe several optimisations focusing on an early elimination of fake tracks. We compared the time needed to find all concurrently existing tracks of the optimised version with the baseline algorithm. The optimisations reduced the wall time by up to 15 % and the GPU memory requirements by up to 65 % for the anticipated collision rate.

Track finding in particle physics has been an increasing challenge over the past decades because the intensities of collisions in state-of-the-art particle colliders have increased enormously. The particle physics experiments ATLAS and CMS at the CERN LHC have been recording data since 2010. The analysis of the data includes finding the particle tracks. For this, dedicated track reconstruction algorithms were developed and applied. One of the track reconstruction steps is the track finding. For this, track seeds are determined and the tracks are successively reconstructed by adding more measurements. The track finding becomes more complex with an increasing number of concurrently existing tracks created by particles that need to be reconstructed. In the past decade, the intensities of the proton-proton collisions at the LHC increased by more than a factor of ten. The time needed for the track finding increased exponentially due to its combinatorial complexity. The next major challenge for track finding will be the HLLHC operation, with an anticipated 200 concurrent collisions every 25 nanoseconds compared to the current 60 collisions. In this paper, we explain the main ideas of the state-of-the-art GPU-accelerated implementation of the track finding algorithm and describe several optimisations focusing on an early elimination of fake tracks. We compared the time needed to find all concurrently existing tracks of the optimised version with the baseline algorithm. The optimisations reduced the wall time by up to 15 % and the GPU memory requirements by up to 65 % for the anticipated collision rate.