基于并行结构的卡尔曼滤波传统跟踪算法
Traditional Tracking with Kalman Filter 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。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。我们报告的结果，我们的调查这些算法的潜力和局限性的新的并行硬件。
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 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. We report the results of our investigations into the potential and limitations of these algorithms on the new parallel hardware.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
