Optimizing charged-particle track reconstruction algorithms is crucial for efficient event reconstruction in Large Hadron Collider (LHC) experiments due to their significant computational demands. Existing track reconstruction algorithms have been adapted to run on massively parallel coprocessors, such as graphics processing units (GPUs), to reduce processing time. Nevertheless, challenges remain in fully harnessing the computational capacity of coprocessors in a scalable and non-disruptive manner. This paper proposes an inference-as-a-service approach for particle tracking in high energy physics experiments. To evaluate the efficacy of this approach, two distinct tracking algorithms are tested: Patatrack, a rule-based algorithm, and Exa$.$TrkX, a machine learning-based algorithm. The as-a-service implementations show enhanced GPU utilization and can process requests from multiple CPU cores concurrently without increasing per-request latency. The impact of data transfer is minimal and insignificant compared to running on local coprocessors. This approach greatly improves the computational efficiency of charged particle tracking, providing a solution to the computing challenges anticipated in the High-Luminosity LHC era.

翻译：优化带电粒子轨迹重建算法对于大型强子对撞机（LHC）实验中的高效事例重建至关重要，因为这些算法具有显著的计算需求。现有的轨迹重建算法已适配于在大规模并行协处理器（如图形处理器，GPU）上运行，以减少处理时间。然而，以可扩展且非破坏性的方式充分利用协处理器的计算能力仍面临挑战。本文提出了一种面向高能物理实验中粒子追踪的推理即服务方法。为评估该方法的有效性，测试了两种不同的追踪算法：基于规则的Patatrack算法和基于机器学习的Exa$.$TrkX算法。即服务实现显示出更高的GPU利用率，并且能够并发处理来自多个CPU核心的请求，而不会增加单请求延迟。与在本地协处理器上运行相比，数据传输的影响微乎其微且不显著。该方法极大提高了带电粒子轨迹重建的计算效率，为高亮度大型强子对撞机时代预期的计算挑战提供了一种解决方案。