论文标题
在爱丽丝的LHC运行3
Overview of online and offline reconstruction in ALICE for LHC Run 3
论文作者
论文摘要
在LHC Run 3中，爱丽丝将使数据的最小偏差PB-PB碰撞的连续读数显着提高到50 kHz。在线计算升级的重建策略可以预见到在数据实现检测器校准的数据期间的第一个同步在线重建阶段，以及后验校准的异步重建阶段。主要的挑战包括每秒的处理和压缩50倍的事件比Run 2中多50倍，可移动的TPC轨道和未用于物理的命令的识别，在连续读数中跟踪TPC数据，TPC空间递减型校准校准校准，以及一般在线运行更多的在线运行2。与Alice 2相比，与Alice相比，请与GPUS相比，将GPUS与Synor for Gpus相比。为了获得最佳的GPU资源利用，我们计划还将异步重建的几个步骤卸载到GPU。为了成为供应商独立的，我们支持CUDA，OPENCL和HIP，并且我们维护了CPU上也在CPU上运行的常见C ++源代码。我们将概述全球重建和跟踪策略，比较CPU和不同GPU模型的性能。我们将通过输入数据大小讨论重建的缩放，以及在内存和处理能力方面对所需资源的估计。
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.
