arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2009.07515physics.ins-det
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LHC运行3的ALICE在线和离线重建概述

Overview of online and offline reconstruction in ALICE for LHC Run 3

David Rohr

摘要:

在LHC运行3中,ALICE将显著提高数据采集速率,以50 kHz连续读出最小偏压Pb-Pb碰撞。在线-离线计算升级的重建策略预见了在数据获取期间的第一同步在线重建阶段,使得能够进行检测器校准,以及后校准的异步重建阶段。主要挑战包括每秒处理和压缩比运行2中多50倍的事件,识别不用于物理的可移除TPC轨道和命中,跟踪连续读出的TPC数据,TPC空间电荷失真校准,以及与运行2相比,通常在线运行更多的重建步骤。ALICE将利用GPU来促进与可用资源的同步处理。为了获得最佳的GPU资源利用率,我们计划将异步重建的几个步骤也卸载到GPU上。为了独立于供应商,我们支持CUDA,OpenCL和HIP,并且我们维护了一个也在CPU上运行的通用C++源代码。我们将概述全局重建和跟踪策略,比较CPU和不同GPU模型的性能。我们将讨论重建与输入数据大小的缩放,以及在内存和处理能力方面所需资源的估计。

英文摘要:

In LHC Run 3, ALICE will increase the data taking rate significantly to 50 kHz continuous readout of minimum bias Pb--Pb collisions. The reconstruction strategy of the online-offline computing upgrade foresees a first synchronous online reconstruction stage during data taking enabling detector calibration, and a posterior calibrated asynchronous reconstruction stage. The main challenges include processing and compression of 50 times more events per second than in Run 2, identification of removable TPC tracks and hits not used for physics, tracking of TPC data in continuous readout, the TPC space-charge distortion calibrations, and in general running more reconstruction steps online compared to Run 2. ALICE will leverage GPUs to facilitate the synchronous processing with the available resources. For the best GPU resource utilization, we plan to offload also several steps of the asynchronous reconstruction to the GPU. In order to be vendor independent, we support CUDA, OpenCL, and HIP, and we maintain a common C++ source code that also runs on the CPU. We will give an overview of the global reconstruction and tracking strategy, a comparison of the performance on CPU and different GPU models. We will discuss the scaling of the reconstruction with the input data size, as well as estimates of the required resources in terms of memory and processing power.

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