Deploying a Hybrid PVFinder Algorithm for Primary Vertex Reconstruction in LHCb's GPU-Resident HLT1

TL;DR

This paper details the development and integration of a hybrid neural network-based primary vertex finder into LHCb's GPU-accelerated real-time trigger system, addressing real-time processing constraints and proposing future performance improvements.

Contribution

It introduces a novel inference engine for PVFinder optimized for GPU-based real-time processing within LHCb's trigger system, including a translation layer for data compatibility and a roadmap for performance enhancements.

Findings

Current CNN stage impacts throughput significantly

Successful integration within real-time constraints

Proposed improvements include mixed-precision and model compression

Abstract

LHCb's Run 3 upgrade introduced a fully software-based trigger system operating at 30~MHz, processing an average of 5.6 proton-proton collision vertices per bunch crossing (event). This work presents the development of an inference engine for PVFinder, a hybrid deep neural network for finding primary vertices, the proton-proton collision points from which all subsequent particle decays originate into Allen, LHCb's High Level Trigger (HLT1) framework. The integration addresses critical real-time constraints including fixed memory pools, single-stream execution, and sub-400~$\mu$s per-event processing budgets on NVIDIA GPUs. We introduce a translation layer that bridges Allen's Structure-of-Arrays (SoA) data layout with cuDNN's tensor format while maintaining zero-copy semantics and deterministic behavior. Current performance shows the CNN stage contributes significant throughput overhead. We present a roadmap targeting order-of-magnitude improvements through mixed-precision computing, model compression and other techniques.