基于CMS检测器的多核粒子径迹重构算法
Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Architectures with the CMS Detector
摘要：
在高亮度大型强子对撞机（HL-LHC）中，最具挑战性的计算问题之一是在事件重建过程中寻找和拟合带电粒子径迹。LHC目前使用的方法是基于卡尔曼滤波器。这样的方法已被证明是鲁棒的，并且在触发和离线两者中提供良好的物理性能。为了提高计算性能，我们探索了基于卡尔曼滤波器的航迹发现和拟合方法，适用于众核SIMD和SIMT架构。我们的基于卡尔曼滤波器的软件已经使用这样的处理器获得了显著的并行加速，例如，英特尔至强融核、英特尔至强SP（可扩展处理器）和（在有限程度上）NVIDIA GPU。最近，一项努力已经开始对我们的软件集成到CMS软件框架，鉴于其开发的运行III的LHC。先前的报告表明，我们的软件实际上允许一些显着的改进，在现有的框架在计算性能与可比的物理性能方面，即使应用于现实的探测器配置和事件的复杂性。在这里，我们证明，在这样的条件下，物理性能可以进一步提高相对于我们以前的报告，同时保留计算性能的改善，通过利用知识的检测器及其几何形状。
英文摘要：
In the High-Luminosity Large Hadron Collider (HL-LHC), one of the most challenging computational problems is expected to be finding and fitting charged-particle tracks during event reconstruction. The methods currently in use at the LHC are based on the Kalman filter. Such methods have shown to be robust and to provide good physics performance, both in the trigger and offline. In order to improve computational performance, we explored Kalman-filter-based methods for track finding and fitting, adapted for many-core SIMD and SIMT architectures. Our adapted Kalman-filter-based software has obtained significant parallel speedups using such processors, e.g., Intel Xeon Phi, Intel Xeon SP (Scalable Processors) and (to a limited degree) NVIDIA GPUs. Recently, an effort has started towards the integration of our software into the CMS software framework, in view of its exploitation for the Run III of the LHC. Prior reports have shown that our software allows in fact for some significant improvements over the existing framework in terms of computational performance with comparable physics performance, even when applied to realistic detector configurations and event complexity. Here, we demonstrate that in such conditions physics performance can be further improved with respect to our prior reports, while retaining the improvements in computational performance, by making use of the knowledge of the detector and its geometry.
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