Speaker
Description
The instantaneous luminosity at the High-Luminosity LHC (HL-LHC) will reach unprecedented levels, boosting the physics reach at the LHC. To cope with the resulting challenging pile-up condition and fully exploit the new high-granularity Inner Tracker (ITk), a major upgrade of the ATLAS Trigger and Data Acquisition (TDAQ) system is ongoing, with track reconstruction in the Event Filter being a critical component. Achieving an online tracking performance close to that of offline algorithms is essential to ensure a successful physics program at HL-LHC, providing the required trigger efficiency while maintaining sustainable trigger rates. Over the past years, an extensive R&D effort has been carried out to design a heterogeneous computing system, exploring possible integrations of CPU cores with GPU or FPGA accelerators at different stages of the tracking workflow, to identify the technology with the highest potential in terms of throughput, power consumption, cost, and tracking performance. This contribution will focus on the remarkable tracking performance achieved across the different technologies, demonstrating the strong potential of tracking at the Event Filter level.