Beginning in 2021, the upgraded LHCb experiment will use a triggerless readout system collecting data at an event rate of 30 MHz. A software-only High Level Trigger will enable unprecedented flexibility for trigger selections. During the first stage (HLT1), a sub-set of the full offline track reconstruction for charged particles is run to select particles of interest based on single or two-track selections. Track reconstruction at 30 MHz represents a significant computing challenge, requiring an evaluation of the most suitable hardware to be used as well as algorithms optimized for this hardware. In this talk we present work based on an R&D project in the context of the LHCb Upgrade I exploring the approach of executing the full HLT1 on GPUs. This includes decoding the raw data, clustering of hits, pattern recognition, as well as track fitting, ghost track recjection with machine learning techniques and finally event selections. We will discuss the development of algorithms optimized for many-core architectures. Both the physics performance and event throughput of the entire HTL1 application running on GPUs will be presented.
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