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Real-time data processing is one of the central processes of particle physicsexperiments which require large computing resources. The LHCb (Large HadronCollider beauty) experiment will be upgraded to cope with a particle bunchcollision rate of 30 million times per second, producing $10^9$ particles/s. 40Tbits/s need to be processed in real-time to make filtering decisions to storedata. This poses a computing challenge that requires exploration of modernhardware and software solutions. We present Compass, a particle trackingalgorithm and a parallel raw input decoding optimised for GPUs. It is designedfor highly parallel architectures, data-oriented and optimised for fast andlocalised data access. Our algorithm is configurable, and we explore thetrade-off in computing and physics performance of various configurations. A CPUimplementation that delivers the same physics performance as our GPUimplementation is presented. We discuss the achieved physics performance andvalidate it with Monte Carlo simulated data. We show a computing performanceanalysis comparing consumer and server grade GPUs, and a CPU. We show thefeasibility of using a full GPU decoding and particle tracking algorithm forhigh-throughput particle trajectories reconstruction, where our algorithmimproves the throughput up to 7.4$\times$ compared to the LHCb baseline.

A parallel-computing algorithm for high-energy physics particle tracking and decoding using GPU architectures