Speeding up Particle Track Reconstruction using a Parallel Kalman Filter Algorithm
Steven Lantz, Kevin McDermott, Michael Reid, Daniel Riley, Peter Wittich, Sophie Berkman, Giuseppe Cerati, Matti Kortelainen, Allison Reinsvold Hall, Peter Elme… 展开作者
Steven Lantz, Kevin McDermott, Michael Reid, Daniel Riley, Peter Wittich, Sophie Berkman, Giuseppe Cerati, Matti Kortelainen, Allison Reinsvold Hall, Peter Elmer, Bei Wang, Leonardo Giannini, Vyacheslav Krutelyov, Mario Masciovecchio, Matevž Tadel, Frank Würthwein, Avraham Yagil, Brian Gravelle, Boyana Norris
One of the most computationally challenging problems expected for the High-Luminosity Large Hadron Collider (HL-LHC) is determining the trajectory of charged particles during event reconstruction. Algorithms used at the LHC today rely on Kalman filtering, which builds physical trajectories incrementally while incorporating material effects and error estimation. Recognizing the need for faster computational throughput, we have adapted Kalman-filter-based methods for highly parallel, many-core SIMD architectures that are now prevalent in high-performance hardware. In this paper, we discuss the design and performance of the improved tracking algorithm, referred to as mkFit. A key piece of the algorithm is the Matriplex library, containing dedicated code to optimally vectorize operations on small matrices. The physics performance of the mkFit algorithm is comparable to the nominal CMS tracking algorithm when reconstructing tracks from simulated proton-proton collisions within the CMS detector. We study the scaling of the algorithm as a function of the parallel resources utilized and find large speedups both from vectorization and multi-threading. mkFit achieves a speedup of a factor of 6 compared to the nominal algorithm when run in a single-threaded application within the CMS software framework.
计算中心，包括那些用于处理高能物理数据和模拟的计算中心，越来越多地通过x86 CPU以外的硬件架构提供其计算资源的重要部分，GPU是一种常见的替代方案。GPU可以以良好的价格点为可以适当并行化的任务提供出色的计算性能。带电粒子（径迹）重建是HEP数据重建的计算上昂贵的组件，并且因此需要以有效的方式使用可用资源。本文提出了一种基于卡尔曼滤波器的航迹拟合算法，该算法采用CUDA实现，并在GPU上运行。这利用了一个通用跟踪软件（A Common Tracking Software）工具包;一个用于轨道重建的开源和独立于实验的工具包。的实施细节和并行化的方法进行了描述，随着这样的实施的具体挑战。详细的性能基准测试结果进行了讨论，这表明令人鼓舞的性能增益超过了基于CPU的实施代表性的配置。最后，这些研究的挑战和未来的发展方向的角度进行了概述。其中包括可以研究的更复杂和更现实的情况，以及可能为更大的灵活性和改进的性能开辟可能性的软件框架和标准的预期发展。
Calorimeter-less gamma-ray telescopes: Optimal measurement of charged particle momentum from multiple scattering by Bayesian analysis of Kalman filtering innovations
