GPU-based Clustering Algorithm for the CMS High Granularity Calorimeter
GPU-based Clustering Algorithm for the CMS High Granularity Calorimeter
复制标题
基于 GPU 的 CMS 高粒度热量计聚类算法
DOI：
10.1051/epjconf/202024505005
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发表时间：
2020
影响因子：
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通讯作者：
M. Rovere
中科院分区：
文献类型：
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作者：
Ziheng Chen;A. Pilato;F. Pantaleo;M. Rovere
未来的高亮度LHC（HL-LHC）预计将提供比目前的LHC高5倍的瞬时亮度，导致每个束流交叉（PU 200）堆积多达200个相互作用。作为第二阶段升级计划的一部分，CMS合作正在开发一种新的端盖量热计系统，即高粒度量热计（HGCAL），具有高度分段的六边形硅传感器和具有超过600万个通道的散热器。对于每个事件，HGCAL聚类算法需要将超过105个命中分组到聚类中。HGCAL聚类算法由于其高堆积和高粒度的特点，面临着前所未有的计算量。CLUE（CLUSTERS OF ENERGY）是一种基于密度的快速可并行聚类算法，针对高粒度热量计中的高堆积场景进行了优化。在本文中，我们提出了CPU和GPU实现的CLUE的HGCAL聚类在CMS软件框架（CMSSW）的应用。与HGCAL聚类算法相比，CMSSW中CPU（GPU）上的CLUE在处理PU 200事件时的速度提高了30倍（180倍），而输出的聚类结果几乎相同。
The future High Luminosity LHC (HL-LHC) is expected to deliver about 5 times higher instantaneous luminosity than the present LHC, resulting in pile-up up to 200 interactions per bunch crossing (PU200). As part of the phase-II upgrade program, the CMS collaboration is developing a new endcap calorimeter system, the High Granularity Calorimeter (HGCAL), featuring highly-segmented hexagonal silicon sensors and scintillators with more than 6 million channels. For each event, the HGCAL clustering algorithm needs to group more than 105 hits into clusters. As consequence of both high pile-up and the high granularity, the HGCAL clustering algorithm is confronted with an unprecedented computing load. CLUE (CLUsters of Energy) is a fast fullyparallelizable density-based clustering algorithm, optimized for high pile-up scenarios in high granularity calorimeters. In this paper, we present both CPU and GPU implementations of CLUE in the application of HGCAL clustering in the CMS Software framework (CMSSW). Comparing with the previous HGCAL clustering algorithm, CLUE on CPU (GPU) in CMSSW is 30x (180x) faster in processing PU200 events while outputting almost the same clustering results.
HL-LHC CMS 高粒度热量计的离线重建算法
DOI：
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发表时间：
2018
期刊：
--
影响因子：
--
作者：
Chen Z
通讯作者：
Chen Z