论文标题
线索：高能量物理学中高粒度热量计的快速平行聚类算法
CLUE: A Fast Parallel Clustering Algorithm for High Granularity Calorimeters in High Energy Physics
论文作者
论文摘要
高粒度量热量计的挑战之一，例如为覆盖HL-LHC的CMS-2期升级中的端盖区域而构建的挑战之一是，在重组重建阶段，大量通道会导致计算负载的激增。在本文中，我们提出了一种快速且完全可行的基于密度的聚类算法，该算法针对高占用场景进行了优化，其中簇的数量远大于集群中的平均命中次数。该算法使用网格空间索引来快速查询邻居及其正时尺度，并与所考虑范围内的命中次数线性缩放。我们还展示了CPU和GPU实现的性能的比较，证明了在高能物理学中的异质计算时代，算法并行化的力量。
One of the challenges of high granularity calorimeters, such as that to be built to cover the endcap region in the CMS Phase-2 Upgrade for HL-LHC, is that the large number of channels causes a surge in the computing load when clustering numerous digitised energy deposits (hits) in the reconstruction stage. In this article, we propose a fast and fully-parallelizable density-based clustering algorithm, optimized for high occupancy scenarios, where the number of clusters is much larger than the average number of hits in a cluster. The algorithm uses a grid spatial index for fast querying of neighbours and its timing scales linearly with the number of hits within the range considered. We also show a comparison of the performance on CPU and GPU implementations, demonstrating the power of algorithmic parallelization in the coming era of heterogeneous computing in high energy physics.
