PANDA is a state-of-the-art hadron physics experiment currently under construction at FAIR, Darmstadt. In order to select events for offline analysis, PANDA will use a software-based triggerless online reconstruction, performed with a data rate of 200 GB/s.
To process the raw data rate of the detector in realtime, we design and implement a GPU version of the Triplet Finder, a fast and robust first-stage tracking algorithm able to reconstruct tracks with good quality, specially designed for the Straw Tube Tracker sub-detector of PANDA. We reduce the algorithmic complexity of processing many hits together by splitting them into bunches, which can be processed independently. We evaluate different ways of processing bunches, GPU dynamic parallelism being one of them. We also propose an optimized technique for associating hits with reconstructed track candidates.
The evaluation of our GPU implementation demonstrates that the Triplet Finder can process almost 6 Mhits/s on a single K20X GPU, making it a promising algorithm for the online event filtering scheme of PANDA.
