High Energy Physics Forum for Computational Excellence: Working Group Reports (I. Applications Software II. Software Libraries and Tools III. Systems)

Computing plays an essential role in all aspects of high energy physics. As computational technology evolves rapidly in new directions, and data throughput and volume continue to follow a steep trend-line, it is important for the HEP community to develop an effective response to a series of expected challenges. In order to help shape the desired response, the HEP Forum for Computational Excellence (HEP-FCE) initiated a roadmap planning activity with two key overlapping drivers -- 1) software effectiveness, and 2) infrastructure and expertise advancement. The HEP-FCE formed three working groups, 1) Applications Software, 2) Software Libraries and Tools, and 3) Systems (including systems software), to provide an overview of the current status of HEP computing and to present findings and opportunities for the desired HEP computational roadmap. The final versions of the reports are combined in this document, and are presented along with introductory material.Comment: 72 page

A Taxonomy of Data Grids for Distributed Data Sharing, Management and Processing

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 high-performance networking and wide-area storage management techniques. In this paper, we discuss the key concepts behind Data Grids and compare them with other data sharing and distribution paradigms such as content delivery networks, peer-to-peer networks and distributed databases. We then provide comprehensive taxonomies that cover various aspects of architecture, data transportation, data replication and resource allocation and scheduling. Finally, we map the proposed taxonomy to various Data Grid systems not only to validate the taxonomy but also to identify areas for future exploration. Through this taxonomy, we aim to categorise existing systems to better understand their goals and their methodology. This would help evaluate their applicability for solving similar problems. This taxonomy also provides a "gap analysis" of this area through which researchers can potentially identify new issues for investigation. Finally, we hope that the proposed taxonomy and mapping also helps to provide an easy way for new practitioners to understand this complex area of research.Comment: 46 pages, 16 figures, Technical Repor

Developing a Digital Twin at Building and City Levels: A Case Study of West Cambridge Campus

A digital twin (DT) refers to a digital replica of physical assets, processes, and systems. DTs integrate artificial intelligence, machine learning, and data analytics to create living digital simulation models that are able to learn and update from multiple sources as well as represent and predict the current and future conditions of physical counterparts. However, current activities related to DTs are still at an early stage with respect to buildings and other infrastructure assets from an architectural and engineering/construction point of view. Less attention has been paid to the operation and maintenance (O&M) phase, which is the longest time span in the asset life cycle. A systematic and clear architecture verified with practical use cases for constructing a DT would be the foremost step for effective operation and maintenance of buildings and cities. According to current research about multitier architectures, this paper presents a system architecture for DTs that is specifically designed at both the building and city levels. Based on this architecture, a DT demonstrator of the West Cambridge site of the University of Cambridge in the UK was developed that integrates heterogeneous data sources, supports effective data querying and analysis, supports decision-making processes in O&M management, and further bridges the gap between human relationships with buildings/cities. This paper aims at going through the whole process of developing DTs in building and city levels from the technical perspective and sharing lessons learned and challenges involved in developing DTs in real practices. Through developing this DT demonstrator, the results provide a clear roadmap and present particular DT research efforts for asset management practitioners, policymakers, and researchers to promote the implementation and development of DT at the building and city levels

Distributed computing and farm management with application to the search for heavy gauge bosons using the ATLAS experiment at the LHC (CERN)

The Standard Model of particle physics describes the strong, weak, and electromagnetic forces between the fundamental particles of ordinary matter. However, it presents several problems and some questions remain unanswered so it cannot be considered a complete theory of fundamental interactions. Many extensions have been proposed in order to address these problems. Some important recent extensions are the Extra Dimensions theories. In the context of some models with Extra Dimensions of size about $1 TeV^{-}1$, in particular in the ADD model with only fermions confined to a D-brane, heavy Kaluza-Klein excitations are expected, with the same properties as SM gauge bosons but more massive. In this work, three hadronic decay modes of some of such massive gauge bosons, Z* and W*, are investigated using the ATLAS experiment at the Large Hadron Collider (LHC), presently under construction at CERN. These hadronic modes are more difficult to detect than the leptonic ones, but they should allow a measurement of the couplings between heavy gauge bosons and quarks. The events were generated using the ATLAS fast simulation and reconstruction MC program Atlfast coupled to the Monte Carlo generator PYTHIA. We found that for an integrated luminosity of $3 × 10^{5} pb^{-}1$ and a heavy gauge boson mass of 2 TeV, the channels Z*->bb and Z*->tt would be difficult to detect because the signal would be very small compared with the expected backgrou nd, although the significance in the case of Z*->tt is larger. In the channel W*->tb , the decay might yield a signal separable from the background and a significance larger than 5 so we conclude that it would be possible to detect this particular mode at the LHC. The analysis was also performed for masses of 1 TeV and we conclude that the observability decreases with the mass. In particular, a significance higher than 5 may be achieved below approximately 1.4, 1.9 and 2.2 TeV for Z*->bb , Z*->tt and W*->tb respectively. The LHC will start to operate in 2008 and collect data in 2009. It will produce roughly 15 Petabytes of data per year. Access to this experimental data has to be provided for some 5,000 scientists working in 500 research institutes and universities. In addition, all data need to be available over the estimated 15-year lifetime of the LHC. The analysis of the data, including comparison with theoretical simulations, requires an enormous computing power. The computing challenges that scientists have to face are the huge amount of data, calculations to perform and collaborators. The Grid has been proposed as a solution for those challenges. The LHC Computing Grid project (LCG) is the Grid used by ATLAS and the other LHC experiments and it is analised in depth with the aim of studying the possible complementary use of it with another Grid project. That is the Berkeley Open Infrastructure for Network C omputing middle-ware (BOINC) developed for the SETI@home project, a Grid specialised in high CPU requirements and in using volunteer computing resources. Several important packages of physics software used by ATLAS and other LHC experiments have been successfully adapted/ported to be used with this platform with the aim of integrating them into the LHC@home project at CERN: Atlfast, PYTHIA, Geant4 and Garfield. The events used in our physics analysis with Atlfast were reproduced using BOINC obtaining exactly the same results. The LCG software, in particular SEAL, ROOT and the external software, was ported to the Solaris/sparc platform to study it's portability in general as well. A testbed was performed including a big number of heterogeneous hardware and software that involves a farm of 100 computers at CERN's computing center (lxboinc) together with 30 PCs from CIEMAT and 45 from schools from Extremadura (Spain). That required a preliminary study, development and creation of components of the Quattor software and configuration management tool to install and manage the lxboinc farm and it also involved the set up of a collaboration between the Spanish research centers and government and CERN. The testbed was successful and 26,597 Grid jobs were delivered, executed and received successfully. We conclude that BOINC and LCG are complementary and useful kinds of Grid that can be used by ATLAS and the other LHC experiments. LCG has very good data distribution, management and storage capabilities that BOINC does not have. In the other hand, BOINC does not need high bandwidth or Internet speed and it also can provide a huge and inexpensive amount of computing power coming from volunteers. In addition, it is possible to send jobs from LCG to BOINC and vice versa. So, possible complementary cases are to use volunteer BOINC nodes when the LCG nodes have too many jobs to do or to use BOINC for high CPU tasks like event generators or reconstructions while concentrating LCG for data analysis

Databases in High Energy Physics: a critial review

The year 2000 is marked by a plethora of significant milestones in the history of High Energy Physics. Not only the true numerical end to the second millennium, this watershed year saw the final run of CERN's Large Electron-Positron collider (LEP) - the world-class machine that had been the focus of the lives of many of us for such a long time. It is also closely related to the subject of this chapter in the following respects: - Classified as a nuclear installation, information on the LEP machine must be retained indefinitely. This represents a challenge to the database community that is almost beyond discussion - archiving of data for a relatively small number of years is indeed feasible, but retaining it for centuries, millennia or more is a very different issue; - There are strong scientific arguments as to why the data from the LEP machine should be retained for a short period. However, the complexity of the data itself, the associated metadata and the programs that manipulate it make even this a huge challenge; - The story of databases in HEP is closely linked to that of LEP itself: what were the basic requirements that were identified in the early years of LEP preparation? How well have these been satisfied? What are the remaining issues and key messages? - Finally, the year 2000 also marked the entry of Grid architectures into the central stage of HEP computing. How has the Grid affected the requirements on databases or the manner in which they are deployed? Furthermore, as the LEP tunnel and even parts of the detectors that it housed are readied for re-use for the Large Hadron Collider (LHC), how have our requirements on databases evolved at this new scale of computing? A number of the key players in the field of databases - as can be seen from the author list of the various publications - have since retired from the field or else this world. Given the fallibility of human memory, the need for a record of the use of databases for physics data processing is clearly needed before memories fade completely and the story is lost forever. It is necessarily somewhat CERN-centric, although effort has been made to cover important developments and events elsewhere. Frequent reference is made to the Computing in High Energy Physics (CHEP) conference series - the most accessible and consistent record of this field