Real-time data processing in the ALICE High Level Trigger at the LHC

doi:10.1016/j.cpc.2019.04.011

Cited by 21 publications

(7 citation statements)

References 32 publications

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“…there are automated actions in place. For instance, several shutdown procedures are performed when the node temperature reaches critical values to prevent damage to the servers [12]. SuperKEKB uses Zabbix to monitor the Input/Output Controller (IOC) performance.…”

Section: Jinst 17 P11005mentioning

confidence: 99%

Control infrastructure monitoring system at the NSRL facility cluster

Qin

Li²,

Liu³

2022

J. Inst.

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The National Synchrotron Radiation Laboratory (NSRL) facility cluster is a collection of user facilities developed by NSRL, including the Hefei Light Source-II (HLS-II), Tunable Infrared Laser for Fundamental of Energy Chemistry (FELiChEM), and THz near-field high-flux material physical property test system (NFTHZ). User facilities generally have high operational availability requirements. The NSRL facility cluster relies on the control infrastructure to provide computing, network, and storage resources, as well as various services that must be available 24 hours a day, 7 days a week. The monitoring system is responsible for tracking the operational status of the control infrastructure, gathering information on faults, performance degradation and cybersecurity issues, and distributing alarm messages in time. It facilitates the operator troubleshooting problems efficiently to improve the availability of the user facilities. The monitoring system is developed by integrating several free and open source software tools. Zabbix is selected as the monitoring tool and collects metrics data from the control infrastructure. Three upper-layer applications are developed for data visualization. The dashboard shows the operational status of the network and various devices. The alarm system collects and distributes alarm messages via web-based GUI and WeChat. The reporting system periodically generates metrics and alarm reports. The monitoring system has been deployed since March 2022. The results indicate that the monitoring system can effectively identify hidden hazards in the control infrastructure.

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Section: Jinst 17 P11005mentioning

confidence: 99%

Control infrastructure monitoring system at the NSRL facility cluster

Qin

Li²,

Liu³

2022

J. Inst.

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“…Although the stochastic MF requires more computational resources, PCA allows to considerably reduce the computational cost. In the case of fast online operations [44,45], where a simple FIR filter is usually adopted, the signal detection and amplitude estimation may be performed by the deterministic MF approximation.…”

Section: Jinst 16 P02016mentioning

confidence: 99%

A matched filter based approach for high-energy estimation in calorimetry

et al. 2021

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In high-energy calorimeters, a crucial task is the reconstruction of the energy deposited by particle interactions. Standard techniques used in modern calorimeters rely on the energy estimation to select the signal with relevant information. This work presents a new approach, which performs the signal detection against noise as a first step, followed by the energy estimation task. The method is fully based on the Matched Filter (MF) theory, which is known to produce the optimum detection efficiency with respect to the signal-to-noise ratio. Furthermore, the MF output can be calibrated to estimate the signal amplitude and, thus, the energy. The proposed method is compared to different optimum filtering algorithms, which are currently being used for energy reconstruction in modern calorimeter systems. The results from simulated data show that the proposed method achieves better performance in terms of both signal detection efficiency and estimation error.

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“…Within the context of online computing, GPUs were first integrated into the 180 compute nodes of the HLT workflow of the ALICE experiment at the LHC to perform charged particle reconstruction. They were part of the ALICE operations during 2010-2013 and 2015-2018 [15]. More recently, GPUs are being considered for the HLT of the LHCb experiment [16,17].…”

Section: Related Workmentioning

confidence: 99%

GPU coprocessors as a service for deep learning inference in high energy physics

Krupa,

Lin,

Flechas

et al. 2020

Preprint

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In the next decade, the demands for computing in large scientific experiments are expected to grow tremendously. During the same time period, CPU performance increases will be limited. At the CERN Large Hadron Collider (LHC), these two issues will confront one another as the collider is upgraded for high luminosity running. Alternative processors such as graphics processing units (GPUs) can resolve this confrontation provided that algorithms can be sufficiently accelerated. In many cases, algorithmic speedups are found to be largest through the adoption of deep learning algorithms. We present a comprehensive exploration of the use of GPU-based hardware acceleration for deep learning inference within the data reconstruction workflow of high energy physics. We present several realistic examples and discuss a strategy for the seamless integration of coprocessors so that the LHC can maintain, if not exceed, its current performance throughout its running.

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Real-time data processing in the ALICE High Level Trigger at the LHC

Cited by 21 publications

References 32 publications

Control infrastructure monitoring system at the NSRL facility cluster

Control infrastructure monitoring system at the NSRL facility cluster

A matched filter based approach for high-energy estimation in calorimetry

GPU coprocessors as a service for deep learning inference in high energy physics

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