Particle physicists have successfully unveiled the fundamental structure of matter with quarks and leptons as elementary constituents. However, it still remains to be explained, how protons and neutrons, the building blocks of atomic nuclei, gain their mass from the interaction between their almost mass-less constituents. An elucidating answer to this problem can be obtained by studying composite systems (in particular “mesons”) containing a heavy “charm” quark or by demonstrating a predicted new exotic type of composite systems with yet unobserved quark combinations. Such systems can be produced in proton-antiproton collisions at the accelerator-complex FAIR in Germany, presently under construction. The particles produced in such powerful collisions will be examined by novel state-of-the-art detection systems. Since the probability to produce rare charm-meson states or even exotic systems is very low, the proton-antiproton collisions need to be performed at extremely high rates. Consequently, subsequent detector signals may overlap which would lead to a dramatic loss of vital information. Instead of well-proven conventional detector-readout systems, we have developed a novel “free-streaming” data readout with “online” event selection and recovery of overlapping signals. The new method was successfully applied in accelerator experiments. We have demonstrated that simple yet powerful algorithms can be developed for the “online” event selection during data transport. The implementation of the studied method in a novel large-scale readout system will allow operating the detector at high collision rates such that a large amount of data can be reliably and precisely processed and rare events can be discovered with high efficiency.

For many experiments, e.g. those at the LHC, design choices made a very long time ago for the compute and trigger model are still used today. The incoming experiments have the opportunity to make new choices based on the current state of computing technology and novel ways to design the reconstruction frameworks, using the experience from previous experiments as well as already existing software packages developed outside the collaboration and used and by a larger community. This talk will show the computing decisions and the current developments of the Panda experiment at FAIR - Darmstadt (Germany), which will take data starting from 2018. One of the key features is a modular software framework with a dynamic data structure based on ROOT, in common to other experiments outside FAIR, with the possibility to run simulation and analysis on grid but open to different middleware technologies such as the cloud. Due to the high data rate, a special attention is also given to the online reconstruction of the continuous data stream coming from a trigger-less DAQ system, where the pre-processing for event selection is done online and concurrency is the key feature to achieve the requested high performances; all the efforts to develop a modular multi-core architecture, supporting also FPGA and GPU units, will be here presented together with the results obtained so far.