基于并行结构的卡尔曼滤波跟踪
Kalman Filter Tracking on Parallel Architectures
摘要：
功率密度限制限制了现代CPU的性能改进。为了解决这个问题，我们已经看到了低功耗多核处理器的推出，但未来将更加令人兴奋。为了保持在功率密度限制内，但仍然获得摩尔定律性能/价格收益，将有必要并行化算法，以利用大量的轻量级内核和专用功能，如大型矢量单元。今天的示例技术包括Intel的Xeon Phi和GPGPU。径迹寻找和拟合是粒子物理中最具挑战性的计算问题之一。例如，在高亮度LHC中，这将是迄今为止的主要问题。对更大并行性的需求已经驱动了对非常不同的寻轨技术的研究，包括元胞自动机或返回到霍夫变换。然而，目前使用的最常见的跟踪查找技术是基于卡尔曼滤波器的那些。这些技术在实际跟踪探测器系统上积累了大量的经验，包括触发和离线。他们被称为提供高的物理性能，是强大的，正是那些正在使用的跟踪系统的设计今天的HL-LHC。我们之前的调查表明，使用优化的数据结构，卡尔曼滤波器的跟踪拟合可以在英特尔至强和至强融核中实现大幅加速。我们在这里报告我们的进一步进展，充分利用矢量化和并行化技术在一个现实的模拟设置的端到端的轨道重建算法。
英文摘要：
Power density constraints are limiting the performance improvements of modern CPUs. To address this we have seen the introduction of lower-power, multi-core processors, but the future will be even more exciting. In order to stay within the power density limits but still obtain Moore's Law performance/price gains, it will be necessary to parallelize algorithms to exploit larger numbers of lightweight cores and specialized functions like large vector units. Example technologies today include Intel's Xeon Phi and GPGPUs. Track finding and fitting is one of the most computationally challenging problems for event reconstruction in particle physics. At the High Luminosity LHC, for example, this will be by far the dominant problem. The need for greater parallelism has driven investigations of very different track finding techniques including Cellular Automata or returning to Hough Transform. The most common track finding techniques in use today are however those based on the Kalman Filter. Significant experience has been accumulated with these techniques on real tracking detector systems, both in the trigger and offline. They are known to provide high physics performance, are robust and are exactly those being used today for the design of the tracking system for HL-LHC. Our previous investigations showed that, using optimized data structures, track fitting with Kalman Filter can achieve large speedup both with Intel Xeon and Xeon Phi. We report here our further progress towards an end-to-end track reconstruction algorithm fully exploiting vectorization and parallelization techniques in a realistic simulation setup.
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