Assessing carbon footprint and performance of the Kalman filter track fitter in the CBM experiment

The CBM experiment at FAIR (GSI, Germany) is among the most significant upcoming projects in heavy-ion physics. It is designed to investigate the properties of dense baryonic matter under extreme conditions. A key feature of the experiment is the high interaction rate, reaching up to 1 0 7 collisions per second, resulting in the production of substantial volumes of experimental data that must be processed and analyzed in real time. To meet this computational challenge, the CBM experiment employs a high-performance tracking algorithm based on the Cellular Automaton for track finding and the Kalman Filter for track fitting. The algorithm is designed for efficient parallel execution on modern multicore and GPU architectures. We evaluate the performance and energy efficiency of the Kalman Filter-based fitting algorithm on both CPUs and GPUs. The GPU implementation demonstrates up to a threefold improvement in energy efficiency, resulting in a proportional reduction in power consumption and associated CO 2 emissions during data processing. These results highlight the significance of energy-efficient computing in high-rate heavy-ion experiments. The analysis provides a quantitative estimate of the carbon footprint associated with track reconstruction and demonstrates how hardware choices influence overall emissions in large-scale data processing workflows.

Abstract licence: CC BY

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