bluuebunny/crossref_metadata_2025 huggingface dataset:
HPC resource integration into CMS Computing via HEPCloud
Authors: Hufnagel Dirk , Holzman Burt , Mason David , Mhashilkar Parag , Timm Steven , Tiradani Anthony , Aftab Khan Farrukh , Gutsche Oliver , Bloom Kenneth
Published: September 2019

The higher energy and luminosity from the LHC in Run 2 have put increased pressure on CMS computing resources. Extrapolating to even higher luminosities (and thus higher event complexities and trigger rates) beyond Run 3, it becomes clear that simply scaling up the the current model of CMS computing alone will become economically unfeasible. High Performance Computing (HPC) facilities, widely used in scientific computing outside of HEP, have the potential to help fill the gap. Here we describe the U.S.CMS efforts to integrate US HPC resources into CMS Computing via the HEPCloud project at Fermilab. We present advancements in our ability to use NERSC resources at scale and efforts to integrate other HPC sites as well. We present experience in the elastic use of HPC resources, quickly scaling up use when so required by CMS workflows. We also present performance studies of the CMS multi-threaded framework on both Haswell and KNL HPC resources.
Evolution of HammerCloud to commission CERN Compute resources
Authors: Schovancová Jaroslava , Di Girolamo Alessandro , Fkiaras Aristeidis , Mancinelli Valentina
Published: September 2019

HammerCloud is a testing service and framework to commission, run continuous tests or on-demand large-scale stress tests, and benchmark computing resources and components of various distributed systems with realistic full-chain experiment workflows.

HammerCloud, used by the ATLAS and CMS experiments in production, has been a useful service to commission both compute resources and various components of the complex distributed systems of the LHC experiments, as well as integral partof the monitoring suite that is essential for the computing operations of the experiments and their automation.

In this contribution we review recent developments of the HammerCloud service that allow use of HammerCloud infrastructure to test Data Centre resources in the early phases of the infrastructure and services commissioning process. One of thebenefits we believe HammerCloud can provide is to be able to tune the commissioning of the new infrastructure, functional and also stress testing, as well as benchmarking with "standard candle" workflows, with experiment realistic workloads, that can be heavy for CPU, or I/O, or IOPS, or everything together. This extension of HammerCloud has been successfully usedin CERN IT during the prototype phase of the "BEER" Batch on EOS (Evaluation of Resources) project, and is being integrated with the continuous integration/continuous deployment suite for Batch service VMs.
The Future of Distributed Computing Systems in ATLAS: Boldly Venturing Beyond Grids
Authors: Barreiro Fernando , Benjamin Doug , Childers Taylor , De Kaushik , Elmsheuser Johannes , Filipcic Andrej , Klimentov Alexei , Lassnig Mario , Maeno Tadashi , Oleynik Danila , Panitkin Sergey , Wenaus Torre
Published: September 2019

Since 2010 the Production and Distributed Analysis system (PanDA) for the ATLAS experiment at the Large Hadron Colliderhas seen big changes to accommodate new types of distributed computing resources: clouds, HPCs, volunteer computers and other external resources. While PanDA was originally designed for fairly homogeneous resources available through the Worldwide LHC Computing Grid, the new resources are heterogeneous, at diverse scales and with diverse interfaces. Up to a fifth of the resources available to ATLAS are of such new types and require special techniques for integration into PanDA. In this talk, we present the nature and scale of these resources. We provide an overview of the various challenges faced, spanning infrastructure, software distribution, workload requirements, scaling requirements, workflow management, data management, network provisioning, and associated software and computing facilities. We describe the strategies for integrating these heterogeneous resources into ATLAS, and the new software components being developed in PanDA to efficiently use them. Plans for software and computing evolution to meet the needs of LHC operations and upgrade in the long term future will be discussed.
Multicore workload scheduling in JUNO[1]
Authors: Zhang Xiaomei , Li Kang , Hu Zhao Xiang , Yan Tian , Sun Yong
Published: September 2019

The Jiangmen Underground Neutrino Observatory (JUNO) is going to apply parallel computing in its software to accelerate JUNO data processing and fully use capability of multi-core and manycore CPUs. Therefore, it is necessary for the JUNO distributed computing system to explore the way to support single-core and multi-core jobs in a consistent way. To support multi-core jobs, a series of changes to the job descriptions, scheduling, monitoring needs to be considered, in which the pilot-based scheduling for a hybrid of single-core and multi-core jobs is the most complicated part. Two scheduling modes and their efficiency are presented and compared in this paper, and also a way to optimize efficiency is provided.
Adoption of ARC-CE and HTCondor at GridKa Tier 1
Authors: Fischer Max , Kuehn Eileen , Schnepf Matthias , Petzold Andreas , Heiss Andreas
Published: September 2019

The GridKa Tier 1 data and computing center hosts a significant share of WLCG processing resources. Providing these resources to all major LHC and other VOs requires efficient, scalable and reliable cluster management. To satisfy this, GridKa has recently migrated its batch resources from CREAM-CE and PBS to ARC-CE and HTCondor. This contribution discusses the key highlights of the adoption of this middleware at the scale of a European Tier 1 center: As the largest WLCG Tier 1 using the ARC-CE plus HTCondor stack, GridKa is exemplary for migrating more than 20 000 cores over the time span of only a few weeks. Supporting multiple VOs, we have extensively studied the constraints and possibilities of scheduling jobs of vastly different requirements. We present a robust and maintainable optimization of resource utilization which still respects constraints desired by VOs. Furthermore, we explore the dynamic extension of our batch system, integrating cloud resources with a lightweight configuration mechanism.
Improving the Scheduling Efficiency of a Global Multi-Core HTCondor Pool in CMS
Authors: Bockelman Brian Paul , Foyo Diego Davila , Hurtado Anampa Kenyi , Ivanov Todor Trendafilov , Khan Farrukh Aftab , Kotobi Amjad , Larson Krista , Letts James , Mascheroni Marco , Mason David , Pérez-Calero Yzquierdo Antonio
Published: September 2019

Scheduling multi-core workflows in a global HTCondor pool is a multi-dimensional problem whose solution depends on the requirements of the job payloads, the characteristics of available resources, and the boundary conditions such as fair share and prioritization imposed on the job matching to resources. Within the context of a dedicated task force, CMS has increased significantly the scheduling efficiency of workflows in reusable multi-core pilots by various improvements to the limitations of the GlideinWMS pilots, accuracy of resource requests, efficiency and speed of the HTCondor infrastructure, and job matching algorithms.
IceProd - a dataset management system for IceCube: update
Authors: Schultz David , Díaz Vélez Juan Carlos
Published: September 2019

IceCube is a cubic kilometer neutrino detector located at the south pole. IceProd is IceCube’s internal dataset management system, keeping track of where, when, and how jobs run. It schedules jobs from submitted datasets to HTCondor, keeping track of them at every stage of the lifecycle. Many updates have happened in the last years to improve stability and scalability, as well as increase user access. Along the way, the IceProd codebase switched from Python 2 to Python 3.
BESIII Data Management System
Authors: Ma Qiumei , Zhang Yao
Published: September 2019

The BESIII experiment has got about 1PB raw data and 1PB DST data from 2009 to date, so how to manage these data and condition data well is very important. The BESIII data managment system has run successfully for ten years, which has offered a full-featured, time-tested approach to BESIII offline and physics users. We designed an almost perfect structure for the system and had good backup and maintance strategy.
Managing data recovery for Long Term Data Preservation
Authors: Dal Pra Stefano , Falabella Antonio , Fattibene Enrico , Paolo Ricci Pier
Published: September 2019

In the latest years, CNAF worked at a project of Long Term Data Preservation (LTDP) for the CDF experiment, that ran at Fermilab after 1985. A part of this project has the goal of archiving data produced during Run I into recent and reliable storage devices, in order to preserve their availability for further access through not obsolete technologies. In this paper, we report and explain the work done to manage the process of retrieving the aforementioned data, which were stored into about four thousands 2.5/5GB 8mm tape cartridges of different producers, which were widely popular in the nineties. The hardware setup for tape reading is briefly detailed. Particular focus is on describing in-house software tools and backend database that have been set up to drive and orchestrate the tape readers and to deal with the high number of possible problems arising during the process of reading data from hardly reliable media. The outcome of each operation is accounted into the database, making possible to monitor the overall progress and to retry unsuccessful read attempts at a later stage. The implemented solution has proved effective at reading a first 20% of the total amount. The process is currently ongoing. Even though a few aspects of this work are strictly dependant on how the CDF experiment organized its datasets, we believe that several decisions taken and the overall organization still make sense on a variety of use cases, where a relevant amount of data has to be retrieved from obsolete media.
The Software Defined Online Storage System at the GridKa WLCG Tier-1 Center
Authors: Erik Sundermann Jan , Bubeliene Jolanta , Obholz Ludmilla , Petzold Andreas
Published: September 2019

The computing center GridKa is serving the ALICE, ATLAS, CMS and LHCb experiments as one of the biggest WLCG Tier-1 centers world wide with compute and storage resources. It is operated by the Steinbuch Centre for Computing at Karlsruhe Institute of Technology in Germany. In April 2017 a new online storage system was put into operation. In its current stage of expansion it offers the HEP experiments a capacity of 34 PB of online storage. The whole storage is partitioned into few large file systems, one for each experiment, using IBM Spectrum Scale as software-defined-storage base layer. The system offers a combined read-write performance of 100 GB/s. It can be scaled transparently both in size and performance allowing to fulfill the growing needs especially of the LHC experiments for online storage in the coming years. In this article we discuss the general architecture of the storage system and present first experiences with the performance of the system in production use.
Architecture and prototype of a WLCG data lake for HL-LHC
Authors: Bird Ian , Campana Simone , Girone Maria , Espinal Xavier , McCance Gavin , Schovancová Jaroslava
Published: September 2019

The computing strategy document for HL-LHC identifies storage as one of the main WLCG challenges in one decade from now. In the naive assumption of applying today's computing model, the ATLAS and CMS experiments will need one order of magnitude more storage resources than what could be realistically provided by the funding agencies at the same cost of today. The evolution of the computing facilities and the way storage will be organized and consolidated will play a key role in how this possible shortage of resources will be addressed. In this contribution we will describe the architecture of a WLCG data lake, intended as a storage service geographically distributed across large data centers connected by fast network with low latency. Will present the experience with our first prototype, showing how the concept, implemented at different scales, can serve different needs, from regional and national consolidation of storage to an international data provisioning service. We will highlight how the system leverages its distributed nature, the economy of scale and different classes of storage to optimise the hardware and operational cost, through a set of policy driven decisions concerning data placement and data retention. We will discuss how the system leverages or interoperates with existing federated storage solutions. We will finally describe the possible data processing models in this environment and present our first benchmarks.
Performance and impact of dynamic data placement in ATLAS
Authors: Maier Thomas , Beermann Thomas , Duckeck Günter , Lassnig Mario , Legger Federica , Magoni Matteo , Vukotic Ilija
Published: September 2019

For high-throughput computing the efficient use of distributed computing resources relies on an evenly distributed workload, which in turn requires wide availability of input data that is used in physics analysis. In ATLAS, the dynamic data placement agent C3PO was implemented in the ATLAS distributed data management system Rucio which identifies popular data and creates additional, transient replicas to make data more widely and more reliably available. This proceedings presents studies on the performance of C3PO and the impact it has on throughput rates of distributed computing in ATLAS. Furthermore, results of a study on popularity prediction using machine learning techniques are presented.
Gaining insight from large data volumes with ease
Authors: Kuznetsov Valentin
Published: September 2019

Efficient handling of large data-volumes becomes a necessity in today’s world. It is driven by the desire to get more insight from the data and to gain a better understanding of user trends which can be transformed into economic incentives (profits, cost-reduction, various optimization of data workflows, and pipelines). In this paper, we discuss how modern technologies are transforming well established patterns in HEP communities. The new data insight can be achieved by embracing Big Data tools for a variety of use cases, from analytics and monitoring to training Machine Learning models on a terabyte scale. We provide concrete examples within the context of the CMS experiment where Big Data tools are already playing or would play a significant role in daily operations
The data management of heterogeneous resources in Belle II
Authors: Schram Malachi
Published: September 2019

The Belle II experiment at the SuperKEKB collider in Tsukuba, Japan, has started taking physics data in early 2018 and plans to accumulate 50 ab-1, which is approximately 50 times more data than the Belle experiment. The collaboration expects it will require managing and processing approximately 200 PB of data.

Computing at this scale requires efficient and coordinated use of the geographically distributed compute resources in North America, Asia and Europe and will take advantage of high-speed global networks. We present the general Belle II the distributed data management system and computing results from the first phase of data taking.
Disk failures in the EOS setup at CERN
Authors: Duellmann Dirk , Portabales Alfonso
Published: September 2019

The EOS deployment at CERN is a core service used for both scientific
data processing, analysis and as back-end for general end-user storage (eg
home directories/CERNBOX). The collected disk failure metrics over a period
of 1 year from a deployment size of some 70k disks allows a first systematic
analysis of the behaviour of different hard disk types for the large CERN usecases.

In this contribution we describe the data collection and analysis, summarise
the measured rates and compare them with other large disk deployments. We
further describe initial steps to use the collected failure and SMART metrics to
develop a machine learning model predicting imminent failures and hence avoid
service degradation and repair costs.
Performance of the Belle II Conditions Database
Authors: Wood Lynn , Bracko Marko , Elsethagen Todd , Fox Kevin , Gamboa Carlos Fernando , Kuhr Thomas , Ritter Martin
Published: September 2019

The Belle II experiment at KEK observed its first collisions in
the summer of 2018. Processing the large amounts of data that will be
produced requires conditions data to be readily available to systems
worldwide in a fast and efficient manner that is straightforward for both the
user and maintainer. This was accomplished by relying on industrystandard
tools and methods: the conditions database is built as an HTTP
REST service using tools such as Swagger for the API interface
development, Payara for the Java EE application server, and Squid for the
caching proxy. This article presents the design of the Belle II conditions
database production environment as well as details about the capabilities
and performance during both Monte Carlo campaigns and data
reprocessing.
LHCb Continuous Integration and Deployment system: a message based approach
Authors: Chitic Stefan-Gabriel , Couturier Ben , Clemencic Marco , Closier Joel
Published: September 2019

A continuous integration system is crucial to maintain the quality of the 6 millions lines of C++ and Python source code of the LHCb software in order to ensure consistent builds of the software as well as to run the unit and integration tests. Jenkins automation server is used for this purpose. It builds and tests around 100 configurations and produces in the order of 1500 built artifacts per day which are installed on the CVMFS file system or potentially on the developers’ machines.

Faced with a large and growing number of configurations built every day, and in order to ease inter-operation between the continuous integration system and the developers, we decided to put in place a flexible messaging system. As soon as the built artifacts have been produced, the distributed system allows their deployment based on the priority of the configurations.

We will describe the architecture of the new system, which is based on RabbitMQ messaging system (and the pika Python client library), and uses priority queues to start the LHCb software integration tests and to drive the installation of the nightly builds on the CVMFS file system. We will also show how the introduction of an event based system can help with the communication of results to developers.
Software packaging and distribution for LHCb using Nix
Authors: Burr Chris , Clemencic Marco , Couturier Ben
Published: September 2019

Software is an essential and rapidly evolving component of modern high energy physics research. The ability to be agile and take advantage of new and updated packages from the wider data science community is allowing physicists to efficiently utilise the data available to them. However, these packages often introduce complex dependency chains and evolve rapidly introducing specific, and sometimes conflicting, version requirements which can make managing environments challenging. Additionally, there is a need to replicate old environments when generating simulated data and to utilise pre-existing datasets. Nix is a “purely functional package manager” which allows for software to be built and distributed with fully specified dependencies, making packages independent from those available on the host. Builds are reproducible and multiple versions/configurations of each package can coexist with the build configuration of each perfectly preserved. Here we will give an overview of Nix followed by the work that has been done to use Nix in LHCb and the advantages and challenges that this brings.
Testing of complex, large-scale distributed storage systems: a CERN disk storage case study
Authors: Makai Jozsef , Peters Andreas Joachim , Bitzes Georgios , Sindrilaru Elvin Alin , Simon Michal Kamil , Manzi Andrea
Published: September 2019

Complex, large-scale distributed systems are frequently used to solve extraordinary computing, storage and other problems. However, the development of these systems usually requires working with several software components, maintaining and improving a large codebase and also providing a collaborative environment for many developers working together. The central role that such complex systems play in mission critical tasks and also in the daily activity of the users means that any software bug affecting the availability of the service has far reaching effects.

Providing an easily extensible testing framework is a pre-requisite for building both confidence in the system but also among developers who contribute to the code. The testing framework can address concrete bugs found in the odebase thus avoiding any future regressions and also provides a high degree of confidence for the people contributing new code. Easily incorporating other people's work into the project greatly helps scaling out manpower so that having more developers contributing to the project can actually result in more work being done rather then more bugs added.

In this paper we go through the case study of EOS, the CERN disk storage system and introduce the methods and mechanisms of how to achieve all-automatic regression and robustness testing along with continuous integration for such a large-scale, complex and critical system using a container-based environment.
Evolution of the ALICE Software Framework for Run 3
Authors: Eulisse Giulio , Konopka Piotr , Krzewicki Mikolaj , Richter Matthias , Rohr David , Wenzel Sandro
Published: September 2019

ALICE is one of the four major LHC experiments at CERN. When the accelerator enters the Run 3 data-taking period, starting in 2021, ALICE expects almost 100 times more Pb-Pb central collisions than now, resulting in a large increase of data throughput. In order to cope with this new challenge, the collaboration had to extensively rethink the whole data processing chain, with a tighter integration between Online and Offline computing worlds. Such a system, code-named ALICE O2, is being developed in collaboration with the FAIR experiments at GSI. It is based on the ALFA framework which provides a generalized implementation of the ALICE High Level Trigger approach, designed around distributed software entities coordinating and communicating via message passing.

We will highlight our efforts to integrate ALFA within the ALICE O2 environment. We analyze the challenges arising from the different running environments for production and development, and conclude on requirements for a flexible and modular software framework. In particular we will present the ALICE O2 Data Processing Layer which deals with ALICE specific requirements in terms of Data Model. The main goal is to reduce the complexity of development of algorithms and managing a distributed system, and by that leading to a significant simplification for the large majority of the ALICE users.
Spack-Based Packaging and Development for HEP
Authors: Green Chris , Amundson James , Garren Lynn , Gartung Patrick , Paterno Marc
Published: September 2019

The art event-based analysis framework is used by multiple Intensity Frontier and other experiments and projects in High Energy Physics (HEP). A system of tools and scripts based around a Fermilab-originated package management system called UNIX Product Support (UPS) has evolved to provide development, packaging, release management, and distribution of the art suite, related experimental software and external dependencies in relocatable binary form.

The existing system has limitations in environments such as macOS with System Integrity Protection (SIP) enabled and the various High Performance Computing (HPC) systems that the field of HEP is increasingly looking to leverage. This has led to a search for other ways of providing experiments with a packaging and build system that meets their needs. We describe our efforts to develop a packaging and release management system based on the Spack HPC package management tool to replace the existing UPS-based system. We additionally describe a companion system: SpackDev and cetmodules, allowing simultaneous development of multiple HEP software packages in a consistent environment. A successful implementation of the intended system would have applicability across HEP rather than merely to the users of the art framework.
Writing ROOT Data in Parallel with TBufferMerger
Authors: Amadio Guilherme , Canal Philippe , Guiraud Enrico , Piparo Danilo
Published: September 2019

Experiments at the Large Hadron Collider (LHC) produce tens of petabytes of new data in ROOT format per year that need to be processed and analysed. In the next decade, following the planned upgrades of the LHC and its detectors, this data production rate is expected to increase at least ten-fold. Therefore, optimizing the ROOT I/O subsystem is of critical importance to the success of the LHC physics programme. This contribution presents ROOT’s approach for writing data from multiple threads to a single output file in an efficient way. Technical aspects of the implementation—the TBufferMerger class—and programming model examples are described. Measurements of runtime performance and the overall improvement relative to the case of serial data writing are also discussed.
Exploring server/web-client event display for CMS
Authors: Mrak Tadel Alja , Tadel Matevz , Yagil Avi , Kovalskyi Dmytro , Linev Sergey
Published: September 2019

The divergence of windowing systems among modern Linux distributions and OSX is making the current mode of event display operations difficult to maintain. In order to continue to support the CMS experiment event display, Fireworks, we need to explore other options beyond the current distribution model of centrally built tarballs.
C++-server web-client event display is a promising direction that can maintain the full functionality of Fireworks, including operation from the full experiment framework. In addition, it brings new features like multi-user debugging and the possibility to implement more elaborate visualization of non-event data through remote access to independent services.
We have been exploring mainly in the direction of Fireworks-based C++ server and thin web-client user interface as it allows for a large degree of reuse of existing algorithms as well as for full access to CMS data formats and accompanying functions that are crucial for the correct physics interpretation of event data. This paper presents the basic architecture of the system, discusses the communication protocol between server and client, and shows existing prototypes that demonstrate the feasibility of advanced event display features.
New developments in the ROOT fitting classes
Authors: Valls Xavier , Moneta Lorenzo , Amadio Guilherme , Tsang Arthur
Published: September 2019

The ROOT Mathematical and Statistical libraries have been recently improved both to increase their performance and to facilitate the modelling of parametric functions that can be used for performing maximum likelihood fits to data sets to estimate parameters and their uncertainties.
First, we report on the new functionalities introduced in ROOT’s TFormula and TF1 classes to build these models in a convenient way for the users. We show how function objects, represented in ROOT by TF1 classes, can be used as probability density functions and how they can be combined together—via an addition operator—to perform extended likelihood fit of several normalized components. We also describe the new operators introduced to perform the convolution of two functions.
Finally, we report on the improvements in the performance of the ROOT fitting algorithm, by using SIMD vectorization when evaluating the model function on large data sets and by exploiting multi-thread parallelization when computing the likelihood function.
Software training for the next generation of physicists: joint experience of LHCb and ALICE
Authors: Berzano Dario , Burr Chris , Beck Hans , Bellée Violaine , Bertens Redmer Alexander , Navarro Albert Puig
Published: September 2019

The need for good software training is essential in the HEP community. Unfortunately, current training is non-homogeneous and the definition of a common baseline is unclear, making it difficult for newcomers to proficiently join large collaborations such as ALICE or LHCb. In the last years, both collaborations have started separate efforts to tackle this issue through training workshops, via Analysis Tutorials (organized by the ALICE Juniors since 2014) and the Starterkit (organized by LHCb students since 2015). In 2017, ALICE and LHCb have for the first time joined efforts to provide combined training by identifying common topics, such as version control systems (Git) and programming languages (e.g. Python). Given the positive experience and feedback, this collaboration will be repeated in the future. We will illustrate the teaching methods, experience and feedback from our first common training workshop. We will also discuss our efforts to extend our format to other HEP experiments for future iterations.
Machine Learning Techniques in the CMS Search for Higgs Decays to Dimuons
Authors: Bourilkov Dimitri , Acosta Darin , Bortignon Pierluigi , Brinkerhoff Andrew , Carnes Andrew , Gleyzer Sergei , Regnery Brendan
Published: September 2019

With the accumulation of large collision datasets at a center-of-mass energy of 13 TeV, the LHC experiments can search for rare processes, where the extraction of signal events from the copious Standard Model backgrounds poses an enormous challenge. Multivariate techniques promise to achieve the best sensitivities by isolating events with higher signal-to-background ratios. Using the search for Higgs bosons decaying to two muons in the CMS experiment as an example, we describe the use of Boosted Decision Trees coupled with automated categorization for optimal event classification, bringing an increase in sensitivity equivalent to 50% more data.
Generative models for fast cluster simulations in the TPC for the ALICE experiment
Authors: Deja Kamil , Trzcin´ski Tomasz , Graczykowski Łukasz
Published: September 2019

Simulating the detector response is a key component of every highenergy physics experiment. The methods used currently for this purpose provide high-fidelity results. However, this precision comes at a price of a high computational cost. In this work, we introduce our research aiming at fast generation of the possible responses of detector clusters to particle collisions. We present the results for the real-life example of the Time Projection Chamber in the ALICE experiment at CERN. The essential component of our solution is a generative model that allows to simulate synthetic data points that bear high similarity to the real data. Leveraging recent advancements in machine learning, we propose to use conditional Generative Adversarial Networks. In this work we present a method to simulate data samples possible to record in the detector based on the initial information about particles. We propose and evaluate several models based on convolutional or recursive networks. The main advantage offered by the proposed method is a significant speed-up in the execution time, reaching up to the factor of 102 with respect to the currently used simulation tool. Nevertheless, this speed-up comes at a price of a lower simulation quality. In this work we adapt available methods and show their quantitative and qualitative limitations.
Binary classifier metrics for optimizing HEP event selection
Authors: Valassi Andrea
Published: September 2019

I discuss the choice of evaluation metrics for binary classifiers in High Energy Physics (HEP) event selection and I point out that the Area Under the ROC Curve (AUC) is of limited relevance in this context, after discussing its use in other domains. I propose new metrics based on Fisher information, which can be used for both the evaluation and training of HEP event selection algorithms in statistically limited measurements of a parameter.
The Scikit-HEP Project
Authors: Rodrigues Eduardo
Published: September 2019

The Scikit-HEP project is a community-driven and community-oriented effort with the aim of providing Particle Physics at large with a Python scientific toolset containing core and common tools. The project builds on five pillars that embrace the major topics involved in a physicist’s analysis work: datasets, data aggregations, modelling, simulation and visualisation. The vision is to build a user and developer community engaging collaboration across experiments, to emulate scikit-learn’s unified interface with Astropy’s embrace of third-party packages, and to improve discoverability of relevant tools.
Application of Deep Learning on Integrating Prediction, Provenance, and Optimization
Authors: Schram Malachi , Tallent Nathan , Friese Ryan , Singh Alok , Altintas Ilkay
Published: September 2019

In this research, we investigated two approaches to detect job anomalies and/or contention for large scale computing efforts:
1. Preemptive job scheduling using binomial classification long short-term memory networks
2. Forecasting intra-node computing loads from the active jobs and additional job(s)
For approach 1, we achieved a 14% improvement in computational resources utilization and an overall classification accuracy of 85% on real tasks executed in a High Energy Physics computing workflow. For this paper, we present the preliminary results used in second approach.
Machine Learning based Global Particle Identification Algorithms at the LHCb Experiment
Authors: Derkach Denis , Hushchyn Mikhail , Kazeev Nikita
Published: September 2019

One of the most important aspects of data processing at flavor
physics experiments is the particle identification (PID) algorithm. In LHCb,
several different sub-detector systems provide PID information: the Ring Imaging
Cherenkov detectors, the hadronic and electromagnetic calorimeters, and
the muon chambers. The charged PID based on the sub-detectors response is
considered as a machine learning problem solved in different modes: one-vs-rest,
one-vs-one and multi-classification, which affect the models training and
prediction. To improve charged particle identification for pions, kaons, protons,
muons and electrons, neural network and gradient boosting models have been
tested. This paper presents these models and their performance evaluated on
Run 2 data and simulation samples. A discussion of the performances is also
presented.
Data Allocation Service ADAS for the Data Rebalancing of ATLAS
Authors: Vamosi Ralf , Lassnig Mario , Schikuta Erich
Published: September 2019

The distributed data management system Rucio manages all data of the ATLAS collaboration across the grid. Automation, such as data replication and data rebalancing are important to ensure proper operation and execution of the scientific workflow. In this proceedings, a new data allocation grid service based on machine learning is proposed. This learning agent takes subsets of the global datasets and proposes a better allocation based on the imposed cost metric, such as waiting time in the workflow. As a service, it can be modularized and can run independently of the existing rebalancing and replication mechanisms. Furthermore, it collects data from other services and learns better allocation while running in the background. Apart from the user selecting datasets, other data services may consult this meta-heuristic service for improved data placement. Network and storage utilization is also taken into account.
Systematic aware learning
Authors: Estrade Victor , Germain Cécile , Guyon Isabelle , Rousseau David
Published: September 2019

Experimental science often has to cope with systematic errors that coherently bias data. We analyze this issue on the analysis of data produced by experiments of the Large Hadron Collider at CERN as a case of supervised domain adaptation. Systematics-aware learning should create an efficient representation that is insensitive to perturbations induced by the systematic effects. We present an experimental comparison of the adversarial knowledge-free approach and a less data-intensive alternative.
Deployment of a Matrix Element Method code for the ttH channel analysis on GPU’s platform
Authors: Grasseau Gilles , Beaudette Florian , Perez Cristina Martin , Zabi Alexandre , Chiron Arnaud , Strebler Thomas , Hautreux Gabriel
Published: September 2019

The observation of the associated production of the Higgs boson with two top quarks in proton-proton collisions is one of the highlights of the LHC Run 2. Driven by the theoretical description of the physics processes, the Matrix Element Method (MEM) consists in computing a probability that an event is compatible with the signal hypothesis (ttH) or with one of the background hypotheses. It is a powerful classifying tool requiring high dimensional integral computations. The deployment of our MEM production code on GPU’s platform will be described. What follows will focus on the adaptation of the main components of the computations in OpenCL kernels, namely the Magraph matrix element code generator, VEGAS, and LHAPDF. Finally, the gain obtained on GPU’s platforms compared with classical CPU’s platforms will be assessed.
Using Big Data Technologies for HEP Analysis
Authors: Cremonesi Matteo , Bellini Claudio , Bian Bianny , Canali Luca , Dimakopoulos Vasileios , Elmer Peter , Fisk Ian , Girone Maria , Gutsche Oliver , Hoh Siew-Yan , Jayatilaka Bo , Khristenko Viktor , Luiselli Andrea , Melo Andrew , Motesnitsalis Evangelos , Olivito Dominick , Pazzini Jacopo , Pivarski Jim , Svyatkovskiy Alexey , Zanetti Marco
Published: September 2019

The HEP community is approaching an era were the excellent performances of the particle accelerators in delivering collision at high rate will force the experiments to record a large amount of information. The growing size of the datasets could potentially become a limiting factor in the capability to produce scientific results timely and efficiently. Recently, new technologies and new approaches have been developed in industry to answer to the necessity to retrieve information as quickly as possible to analyze PB and EB datasets. Providing the scientists with these modern computing tools will lead to rethinking the principles of data analysis in HEP, making the overall scientific process faster and smoother.

In this paper, we are presenting the latest developments and the most recent results on the usage of Apache Spark for HEP analysis. The study aims at evaluating the efficiency of the application of the new tools both quantitatively, by measuring the performances, and qualitatively, focusing on the user experience. The first goal is achieved by developing a data reduction facility: working together with CERN Openlab and Intel, CMS replicates a real physics search using Spark-based technologies, with the ambition of reducing 1 PB of public data in 5 hours, collected by the CMS experiment, to 1 TB of data in a format suitable for physics analysis.

The second goal is achieved by implementing multiple physics use-cases in Apache Spark using as input preprocessed datasets derived from official CMS data and simulation. By performing different end-analyses up to the publication plots on different hardware, feasibility, usability and portability are compared to the ones of a traditional ROOT-based workflow.
Pandas DataFrames for a FAST binned analysis at CMS
Authors: Krikler Benjamin Edward , Davignon Olivier , Kreczko Lukasz , Linacre Jacob , Olaiya Emmanuel Olatunji , Sakuma Tai
Published: September 2019

Binned data frames are a generalisation of multi-dimensional histograms, represented in a tabular format with one category per row containing the labels, bin contents, uncertainties and so on. Pandas is an industry-standard tool, which provides a data frame implementation complete with routines for data frame manipultion, persistency, visualisation, and easy access to “big data” scientific libraries and machine learning tools. FAST (the Faster Analysis Software Taskforce) has developed a generic approach for typical binned HEP analyses, driving the summary of ROOT Trees to multiple binned DataFrames with a yaml-based analysis description. Using Continuous Integration to run subsets of the analysis, we can monitor and test changes to the analysis itself, and deploy documentation automatically. This report describes this approach using examples from a public CMS tutorial and details the benefit over traditional methods.
Grid services in a box: container management in ALICE
Authors: Storetvedt Maxim , Litmaath Maarten , Betev Latchezar , Helstrup Håvard , Fanebust Hetland Kristin , Kileng Bjarte
Published: September 2019

Virtualization and containers are established tools for providing simplified deployment, elasticity and workflow isolation. These benefits are especially advantageous in containers, which dispense with the resource overhead associated with virtual machines in cases where virtualization of the full hardware stack is not considered necessary. Containers are also simpler to setup and maintain in production environments–deployed and currently operational systems serving end-users, where service disruptions should be avoided. This contribution addresses container configuration and deployment to run central and site services on the ALICE Grid system, specifically to achieve containerized VO-boxes. We describe the methods through which we minimize the manual interaction, while retaining the simplicity and scalability associated with container deployment, the so-called ”service in a box”. Furthermore, we explore ways to increase fault tolerance, aimed at reducing the risk of service downtime, and identify possible performance bottlenecks.
Facilitating Collaborative Analysis in SWAN
Authors: Bocchi Enrico , Castro Diogo , Gonzalez Hugo , Lamanna Massimo , Mato Pere , Moscicki Jakub , Piparo Danilo , Tejedor Enric
Published: September 2019

SWAN (Service for Web-based ANalysis) is a CERN service that allows users to perform interactive data analysis in the cloud, in a “software as a service” model. It is built upon the widely-used Jupyter notebooks, allowing users to write - and run - their data analysis using only a web browser. By connecting to SWAN, users have immediate access to storage, software and computing resources that CERN provides and that they need to do their analyses.

Besides providing an easier way of producing scientific code and results, SWAN is also a great tool to create shareable content. From results that need to be reproducible, to tutorials and demonstrations for outreach and teaching, Jupyter notebooks are the ideal way of distributing this content. In one single file, users can include their code, the results of the calculations and all the relevant textual information. By sharing them, it allows others to visualize, modify, personalize or even re-run all the code.

In that sense, this paper describes the efforts made to facilitate sharing in SWAN. Given the importance of collaboration in our scientific community, we have brought the sharing functionality from CERNBox, CERN’s cloud storage service, directly inside SWAN. SWAN users have available a new and redesigned interface where theycan share “Projects”: a special kind of folder containing notebooks and other files, e.g., like input datasets and images. When a user shares a Project with some other users, the latter can immediately see andwork with the contents of that project from SWAN.
Managing a heterogeneous scientific computing cluster with cloud-like tools: ideas and experience
Authors: Aldinucci Marco , Bagnasco Stefano , Concas Matteo , Lusso Stefano , Rabellino Sergio , Demarchi Danilo , Vallero Sara
Published: September 2019

Obtaining CPU cycles on an HPC cluster is nowadays relatively simple and sometimes even cheap for academic institutions. However, in most of the cases providers of HPC services would not allow changes on the configuration, implementation of special features or a lower-level control on the computing infrastructure, for example for testing experimental configurations. The variety of use cases proposed by several departments of the University of Torino, including ones from solid-state chemistry, computational biology, genomics and many others, called for different and sometimes conflicting configurations; furthermore, several R&D activities in the field of scientific computing, with topics ranging from GPU acceleration to Cloud Computing technologies, needed a platform to be carried out on. The Open Computing Cluster for Advanced data Manipulation (OCCAM) is a multi-purpose flexible HPC cluster designed and operated by a collaboration between the University of Torino and the Torino branch of the Istituto Nazionale di Fisica Nucleare. It is aimed at providing a flexible and reconfigurable infrastructure to cater to a wide range of different scientific computing needs, as well as a platform for R&D activities on computational technologies themselves. We describe some of the use cases that prompted the design and construction of the system, its architecture and a first characterisation of its performance by some synthetic benchmark tools and a few realistic use-case tests.
Improving WLCG Networks Through Monitoring and Analytics
Authors: Babik Marian , McKee Shawn , Bockelman Brian Paul , Fajardo Hernandez Edgar Mauricio , Martelli Edoardo , Vukotic Ilija , Weitzel Derek , Zvada Marian
Published: September 2019

WLCG relies on the network as a critical part of its infrastructure and therefore needs to guarantee effective network usage and prompt detection and resolution of any network issues, including connection failures, congestion and traffic routing. OSG Networking Area in partnership with WLCG has focused on collecting, storing and making available all the network related metrics for further analysis and discovery of issues that might impact network performance and operations. In order to help sites and experiments better understand and fix the networking issues, WLCG Network Throughput working group was formed, which works on the analysis and integration of the network-related monitoring data collected by the OSG/WLCG infrastructure and operates a support unit to help find and fix the network performance issues. This paper describes the current state of the OSG network measurement platform and summarises the activities taken by the working group, including updates on the higher level services that were recently developed, network performance incidents investigated as well as past and present analytical activities related to networking and their results.
Next Generation of HEP CPU Benchmarks
Authors: Giordano Domenico , Alef Manfred , Michelotto Michele
Published: September 2019

As of 2009, HEP-SPEC06 (HS06) is the benchmark adopted by the WLCG community to describe the computing requirements of the LHC experiments, to assess the computing capacity of the WLCG data centres and to procure new hardware. In the recent years, following the evolution of CPU architectures and the adoption of new programming paradigms, such as multi-threading and vectorization, it has turned out that HS06 is less representative of the relevant applications running on the WLCG infrastructure. Meanwhile, in 2017 a new SPEC generation of benchmarks for CPU intensive workloads has been released: SPEC CPU 2017. This report summarises the findings of the HEPiX Benchmarking Working Group in comparing SPEC CPU 2017 and other HEP benchmarks with the typical WLCG workloads mixes.