The reconstruction of particle trajectories, tracking, is a central process in the reconstruction of particle collisions in High Energy Physics detectors. At the LHCb detector in the Large Hadron Collider, bunches of particles collide 30 million times per second. These collisions produce about 109 particle trajectories per second that need to be reconstructed in real time, in order to filter and store data. Upcoming improvements in the LHCb detector will deprecate the hardware filter in favour of a full software filter, posing a computing challenge that requires a renovation of current algorithms and the underlying hardware.
We present Search by triplet, a local tracking algorithm optimized for parallel architectures. We design our algorithm reducing Read-After-Write dependencies as well as conditional branches, incrementing the potential for parallelization. We analyze the complexity of our algorithm and validate our results. We show the scaling of our algorithm for an increasing number of collision events.
We show sustained tests for our algorithm sequence given a simulated dataflow. We develop CPU and GPU implementations of our work, and hide the transmission times between device and host by executing a multi-stream pipeline.
Our results provide a reliable basis for an informed assessment on the feasibility of LHCb event reconstruction on parallel architectures, enabling us to develop cost models for upcoming technology upgrades. The created software infrastructure is extensible and permits the addition of subsequent reconstruction algorithms.