Related papers: First Experiences with LHC Grid Computing and Dist…

Monitoring and information services form a key component of a distributed system, or Grid. A quantitative study of such services can aid in understanding the performance limitations, advise in the deployment of the systems, and help…

The distributed computing is done on many systems to solve a large scale problem. The growing of high-speed broadband networks in developed and developing countries, the continual increase in computing power, and the rapid growth of the…

Data Grids have been adopted as the platform for scientific communities that need to share, access, transport, process and manage large data collections distributed worldwide. They combine high-end computing technologies with…

We review here the prospects of a long-term upgrade programme for the Large Hadron Collider (LHC), CERN laboratory's new proton-proton collider. The super-LHC, which is currently under evaluation and design, is expected to deliver of the…

Blockchain and Cloud Computing are two of the main topics related to the distributed computing paradigm, and in the last decade, they have seen exponential growth in their adoption. Cloud computing has long been established as the main…

Distributed computing platforms provide a robust mechanism to perform large-scale computations by splitting the task and data among multiple locations, possibly located thousands of miles apart geographically. Although such distribution of…

As the Grid evolves from a high performance cluster middleware to a multipurpose utility computing framework, a good understanding of Grid applications, their statistics and utilisation patterns is required. This study looks at job…

The efficient exploitation of worldwide distributed storage and computing resources available in the grids require a robust, transparent and fast deployment of experiment specific software. The approach followed by the CMS experiment at…

This document gives an overview of a Grid testbed architecture proposal for the NorduGrid project. The aim of the project is to establish an inter-Nordic testbed facility for implementation of wide area computing and data handling. The…

This paper addresses the problem of management and coordination of energy resources in a typical microgrid, including smart buildings as flexible loads, energy storages, and renewables. The overall goal is to provide a comprehensive and…

The ALICE Collaboration has just finished a major detector upgrade that increases the data-taking rate capability by two orders of magnitude and will allow to collect unprecedented data samples. For example, the analysis input for 1 month…

The LHCb experiment at the Large Hadron Collider (LHC) at CERN has successfully performed a large number of physics measurements during Runs 1 and 2 of the LHC. Monte Carlo simulation is key to the interpretation of these and future…

The IRIS-HEP Analysis Grand Challenge (AGC) is designed to be a realistic environment for investigating how analysis methods scale to the demands of the HL-LHC. The analysis task is based on publicly available Open Data and allows for…

The CMS experiment at the LHC accelerator at CERN relies on its computing infrastructure to stay at the frontier of High Energy Physics, searching for new phenomena and making discoveries. Even though computing plays a significant role in…

Computational Grids, emerging as an infrastructure for next generation computing, enable the sharing, selection, and aggregation of geographically distributed resources for solving large-scale problems in science, engineering, and commerce.…

Array operations are one of the most concise ways of expressing common filtering and simple aggregation operations that is the hallmark of the first step of a particle physics analysis: selection, filtering, basic vector operations, and…

The CMS experiment at CERN has released research-quality data from particle collisions at the LHC since 2014. Almost all data from the first LHC run in 2010-2012 with the corresponding simulated samples are now in the public domain, and…

Training and deploying deep learning models in real-world applications require processing large amounts of data. This is a challenging task when the amount of data grows to a hundred terabytes, or even, petabyte-scale. We introduce a hybrid…

The increasing complexity of the software/hardware stack of modern supercomputers results in explosion of parameters. The performance analysis becomes a truly experimental science, even more challenging in the presence of massive…

This paper was prepared by the HEP Software Foundation (HSF) PyHEP Working Group as input to the second phase of the LHCC review of High-Luminosity LHC (HL-LHC) computing, which took place in November, 2021. It describes the adoption of…