The PANDA experiment, currently under construction at the Facility for Antiproton and Ion Research (FAIR) in Darmstadt, Germany, addresses fundamental questions in hadron and nuclear physics via interactions of antiprotons with nuclei. It will be installed at the High Energy Storage Ring (HESR), which will provide an antiproton beam with a momentum range of 1.5 - 15 GeV/c and enables a high average interaction rate on the fixed target of 2 x 10^7 events/s. The PANDA experiment adopts a continuous data acquisition and the expected data rate transmitted to a high-bandwidth computing network will be in the order of 200 GB/s. However, in order to select very rare physics processes, an indiscriminate hardware trigger does not suffice. Instead, an online software-based data selection system will be used to achieve a data reduction of a factor 100 - 1000. This demands a highly advanced online analysis due to the high interaction rate which has to deal also with overlapping event data. Scalability and parallelization of the reconstruction algorithms are therefore a particular focus in the development process. An simulation framework called PandaRoot is used to develop and evaluate different reconstruction algorithms for event building, tracking and particle identification as well as to further optimize the detector performance. An overview about PandaRoot and the requirements on the event reconstruction algorithms is presented and algorithms for the event time reconstruction currently under development are discussed.

PANDA is a future hadron and nuclear physics experiment at the FAIR facility in construction in Darmstadt, Germany. Unlike the majority of current experiments, PANDA's strategy for data acquisition is based on online event reconstruction from free-streaming data, performed in real time entirely by software algorithms using global detector information. This paper reports on the status of the development of algorithms for the reconstruction of charged particle tracks, targeted towards online data processing applications, designed for execution on data-parallel processors such as GPUs (Graphic Processing Units). Two parallel algorithms for track finding, derived from the Circle Hough algorithm, are being developed to extend the parallelism to all stages of the algorithm. The concepts of the algorithms are described, along with preliminary results and considerations about their implementations and performance.