A Comparison of CPU and GPU Implementations for the LHCb Experiment Run 3 Trigger

The Large Hadron Collider beauty (LHCb) experiment at CERN is undergoing an upgrade in preparation for the Run 3 data collection period at the Large Hadron Collider (LHC). As part of this upgrade, the trigger is moving to a full software implementation operating at the LHC bunch crossing rate. We present an evaluation of a CPU-based and a GPU-based implementation of the first stage of the high-level trigger. After a detailed comparison, both options are found to be viable. This document summarizes the performance and implementation details of these options, the outcome of which has led to the choice of the GPU-based implementation as the baseline.

Have Academics’ Citation Patterns Changed in Response to the Rise of World University Rankings? A Test Using First-Citation Speeds

Academics may actively respond to the expectations of the academic status market, which have largely been shaped by the World University Rankings (WURs). This study empirically examines how academics’ citation patterns have changed in response to the rise of an “evaluation environment” in academia. We regard the WURs to be a macro-level trigger for cementing a bibliometric-based evaluation environment in academia. Our analyses of citation patterns in papers published in two higher education journals explicitly considered three distinct periods: the pre-WURs (1990–2003), the period of WURs implementation (2004–2010), and the period of adaption to WURs (2011–2017). We applied the nonparametric Kaplan–Meier method to compare first-citation speeds of papers published across the three periods. We found that not only has first-citation speed become faster, but first-citation probability has also increased following the emergence of the WURs. Applying Cox proportional hazard models to first-citation probabilities, we identified journal impact factors and third-party funding as factors influencing first-citation probability, while other author- and paper-related factors showed limited effects. We also found that the general effects of different factors on first-citation speeds have changed with the emergence of the WURs. The findings expand our understanding of the citation patterns of academics in the rise of WURs and provide practical grounds for research policy as well as higher education policy.

Development of FPGA-based neural network regression models for the ATLAS Phase-II barrel muon trigger upgrade

Effective selection of muon candidates is the cornerstone of the LHC physics programme. The ATLAS experiment uses a two-level trigger system for real-time selection of interesting collision events. The first-level hardware trigger system uses the Resistive Plate Chamber detector (RPC) for selecting muon candidates in the central (barrel) region of the detector. With the planned upgrades, the entirely new FPGA-based muon trigger system will be installed in 2025-2026. In this paper, neural network regression models are studied for potential applications in the new RPC trigger system. A simple simulation model of the current detector is developed for training and testing neural network regression models. Effects from additional cluster hits and noise hits are evaluated. Efficiency of selecting muon candidates is estimated as a function of the transverse muon momentum. Several models are evaluated and their performance is compared to that of the current detector, showing promising potential to improve on current algorithms for the ATLAS Phase-II barrel muon trigger upgrade.

A real-time FPGA-based cluster finding algorithm for LHCb silicon pixel detector

Starting from the next LHC run, the upgraded LHCb High Level Trigger will process events at the full LHC collision rate (averaging 30 MHz). This challenging goal, tackled using a large and heterogeneous computing farm, can be eased addressing lowest-level, more repetitive tasks at the earliest stages of the data acquisition chain. FPGA devices are very well-suited to perform with a high degree of parallelism and efficiency certain computations, that would be significantly demanding if performed on general-purpose architectures. A particularly time-demanding task is the cluster-finding process, due to the 2D pixel geometry of the new LHCb pixel detector. We describe here a custom highly parallel FPGA-based clustering algorithm and its firmware implementation. The algorithm implementation has shown excellent reconstruction quality during qualification tests, while requiring a modest amount of hardware resources. Therefore it can run in the LHCb FPGA readout cards in real time, during data taking at 30 MHz, representing a promising alternative solution to more common CPU-based algorithms.

FELIX: the Detector Interface for the ATLAS Experiment at CERN

The Front-End Link eXchange (FELIX) system is an interface between the trigger and detector electronics and commodity switched networks for the ATLAS experiment at CERN. In preparation for the LHC Run 3, to start in 2022, the system is being installed to read out the new electromagnetic calorimeter, calorimeter trigger, and muon components being installed as part of the ongoing ATLAS upgrade programme. The detector and trigger electronic systems are largely custom and fully synchronous with respect to the 40.08 MHz clock of the Large Hadron Collider (LHC). The FELIX system uses FPGAs on server-hosted PCIe boards to pass data between custom data links connected to the detector and trigger electronics and host system memory over a PCIe interface then route data to network clients, such as the Software Readout Drivers (SW ROD), via a dedicated software platform running on these machines. The SW RODs build event fragments, buffer data, perform detector-specific processing and provide data for the ATLAS High Level Trigger. The FELIX approach takes advantage of modern FPGAs and commodity computing to reduce the system complexity and effort needed to support data acquisition systems in comparison to previous designs. Future upgrades of the experiment will introduce FELIX to read out all other detector components.

The ATLAS electron and photon trigger performance in Run 2

Electron and photon triggers are an important part of many physics analyses at the ATLAS experiment, where electron and photon final states are considered. Understanding the performance of electron and photon triggers at the High Level trigger as well as the Level-1 trigger was crucial to improve and adapt the trigger during changing run conditions of the Large Hadron Collider in Run 2 (2015–2018).

Software for online reconstruction and filtering at the Belle II experiment

The Belle II experiment is designed to collect 50 times more data than its predecessor. For a smooth collection of high-quality data, a robust and automated data transport and processing pipeline has been established. We describe the basic software components employed by the high level trigger. It performs a reconstruction of all events using the same algorithms as offline, classifies the events according to physics criteria, and provides monitoring information. The improved system described in this paper has been deployed successfully since 2019.