ALICE HLT TPC Tracking of Pb-Pb Events on GPUs

TL;DR

This paper presents a GPU-accelerated implementation of the TPC tracking algorithm for the ALICE experiment, achieving a threefold speedup over CPU and maintaining high accuracy for real-time processing of Pb-Pb collision events.

Contribution

The paper introduces a GPU-based, pipelined tracking algorithm with optimized phases for real-time particle trajectory reconstruction in high-rate collision events.

Findings

GPU tracker is three times faster than CPU version.

Achieves 99.999% concordance with CPU tracking.

Supports real-time processing at 200 Hz for Pb-Pb events.

Abstract

The online event reconstruction for the ALICE experiment at CERN requires processing capabilities to process central Pb-Pb collisions at a rate of more than 200 Hz, corresponding to an input data rate of about 25 GB/s. The reconstruction of particle trajectories in the Time Projection Chamber (TPC) is the most compute intensive step. The TPC online tracker implementation combines the principle of the cellular automaton and the Kalman filter. It has been accelerated by the usage of graphics cards (GPUs). A pipelined processing allows to perform the tracking on the GPU, the data transfer, and the preprocessing on the CPU in parallel. In order for CPU pre- and postprocessing to keep step with the GPU the pipeline uses multiple threads. A splitting of the tracking in multiple phases searching for short local track segments first improves data locality and makes the algorithm suited to run on a GPU. Due to special optimizations this course of action is not second to a global approach. Because of non-associative floating-point arithmetic a binary comparison of GPU and CPU tracker is infeasible. A track by track and cluster by cluster comparison shows a concordance of 99.999%. With current hardware, the GPU tracker outperforms the CPU version by about a factor of three leaving the processor still available for other tasks.