arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2001.09761physics.ins-dethep-ex
收藏

CLUE: A Fast Parallel Clustering Algorithm for High Granularity Calorimeters in High Energy Physics

Marco Rovere, Ziheng Chen, Antonio Di Pilato, Felice Pantaleo, Chris Seez

英文摘要:

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.

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。

↑