Segment Linking: A Highly Parallelizable Track Reconstruction Algorithm for HL-LHC

TL;DR

This paper introduces Segment Linking, a GPU-optimized, highly parallelizable track reconstruction algorithm designed for the HL-LHC's complex collision environment, achieving efficiency comparable to multi-CPU methods.

Contribution

The paper presents a novel bottom-up track reconstruction algorithm optimized for GPUs, enabling efficient, parallel processing suitable for HL-LHC conditions.

Findings

Achieves track reconstruction efficiency comparable to multi-CPU algorithms.

Demonstrates high parallelization capability on NVIDIA Tesla V100 GPU.

Reduces computational cost for HL-LHC data processing.

Abstract

The High Luminosity upgrade of the Large Hadron Collider (HL-LHC) will produce particle collisions with up to 200 simultaneous proton-proton interactions. These unprecedented conditions will create a combinatorial complexity for charged-particle track reconstruction that demands a computational cost that is expected to surpass the projected computing budget using conventional CPUs. Motivated by this and taking into account the prevalence of heterogeneous computing in cutting-edge High Performance Computing centers, we propose an efficient, fast and highly parallelizable bottom-up approach to track reconstruction for the HL-LHC, along with an associated implementation on GPUs, in the context of the Phase 2 CMS outer tracker. Our algorithm, called Segment Linking (or Line Segment Tracking), takes advantage of localized track stub creation, combining individual stubs to progressively form higher level objects that are subject to kinematical and geometrical requirements compatible with genuine physics tracks. The local nature of the algorithm makes it ideal for parallelization under the Single Instruction, Multiple Data paradigm, as hundreds of objects can be built simultaneously. The computing and physics performance of the algorithm has been tested on an NVIDIA Tesla V100 GPU, already yielding efficiency and timing measurements that are on par with the latest, multi-CPU versions of existing CMS tracking algorithms.