The Pixel Luminosity Telescope: a detector for luminosity measurement at CMS using silicon pixel sensors

The Pixel Luminosity Telescope is a silicon pixel detector dedicated to luminosity measurement at the CMS experiment at the LHC. It is located approximately 1.75 m from the interaction point and arranged into 16 “telescopes”, with eight telescopes installed around the beam pipe at either end of the detector and each telescope composed of three individual silicon sensor planes. The per-bunch instantaneous luminosity is measured by counting events where all three planes in the telescope register a hit, using a special readout at the full LHC bunch-crossing rate of 40 MHz. The full pixel information is read out at a lower rate and can be used to determine calibrations, corrections, and systematic uncertainties for the online and offline measurements. This paper details the commissioning, operational history, and performance of the detector during Run 2 (2015–18) of the LHC, as well as preparations for Run 3, which will begin in 2022

A Parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems

In this paper, we investigate the problem of scheduling precedence-constrained parallel applications on heterogeneous computing systems (HCSs) like cloud computing infrastructures. This kind of application was studied and used in many research works. Most of these works propose algorithms to minimize the completion time (makespan) without paying much attention to energy consumption. We propose a new parallel bi-objective hybrid genetic algorithm that takes into account, not only makespan, but also energy consumption. We particularly focus on the island parallel model and the multi-start parallel model. Our new method is based on dynamic voltage scaling (DVS) to minimize energy consumption. In terms of energy consumption, the obtained results show that our approach outperforms previous scheduling methods by a significant margin. In terms of completion time, the obtained schedules are also shorter than those of other algorithms. Furthermore, our study demonstrates the potential of DVS.12 page(s

A Parallel island-based hybrid genetic algorithm for precedence-constrained applications to minimize energy consumption and makespan

Task scheduling algorithms are designed mostly with the sole goal of minimizing makespan (completion time). Almost all research works related to this kind of algorithms do not pay much attention to energy consumption. In this paper, we investigate the energy issue in task scheduling particularly on high-performance computing systems (HCSs). We propose a new island-based bi-objective hybrid algorithm that takes into account, not only makespan, but also energy consumption. The proposed approach uses dynamic voltage scaling (DVS) to minimize energy consumption. Our study provides the significance and potential of DVS. The proposed approach is powerful as it profits from the cooperative paradigm of the island model. Indeed, the obtained results show that our approach outperforms previous scheduling methods, in terms of energy consumption, by a noticeable margin. The obtained schedules are also shorter, in terms of completion time, than those of other algorithms.8 page(s