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

ALICE HLT TPC Tracking of Pb-Pb Events on GPUs