<p><strong>CHEP 2016 Conference, San Francisco, October 8-14, 2016 </strong></p><p>Monday, 10 October 2016 - Friday, 14 October 2016 <br>San Francisco Marrioꢀ Marquis </p><p><strong>Book of Abstracts </strong></p><p>ii </p><p><strong>Contents </strong></p><p>Experim ent Managem ent System&nbsp;for the SND Detector 0 .&nbsp;. . . . . . . . . . . . . . . . . Reconstruction soſtware of the silicon tracker of DAMPE m ission 2 .&nbsp;. . . . . . . . . . . HEPData - a repository for high energy physics data exploration 3 .&nbsp;. . . . . . . . . . . . Reconstruction of Micropaꢀern Detector Signals using Convolutional Neural Networks 4 <br>1123<br>Federated data storage system&nbsp;prototype for LHC experim ents and data intensive science </p><ul style="display: flex;"><li style="flex:1">6 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . </li><li style="flex:1">3</li></ul><p>4566<br>BelleII@hom e: Integrate volunteer com puting resources into DIRAC in a secure way 7&nbsp;. Reconstruction and calibration of MRPC endcap TOF of BESIII 8 .&nbsp;. . . . . . . . . . . . . RootJS: Node.js Bindings for ROOT 6 9 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . C++ Soſtware ꢁality in the ATLAS experim ent: Tools and Experience 10 .&nbsp;. . . . . . . . An autom ated m eta-m onitoring m obile application and frontend interface for the WLCG </p><ul style="display: flex;"><li style="flex:1">com puting m odel 11 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . </li><li style="flex:1">7</li></ul><p>Experience of Google’s latest Deep Learning library, TensorFlow, with Docker in a WLCG </p><ul style="display: flex;"><li style="flex:1">cluster 12 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . </li><li style="flex:1">8</li></ul><p>89<br>Flexible online m onitoring for high-energy physics with Pyram e 13 .&nbsp;. . . . . . . . . . . Sim ulation of orientational coherent effects via Geant4 14 .&nbsp;. . . . . . . . . . . . . . . . . Detector control system&nbsp;for the AFP detector in ATLAS experim ent at CERN 15 .&nbsp;. . . .&nbsp;10 e InfiniBand based Event Builder im plem entation for the LHCb upgrade 16 .&nbsp;. . . . . .&nbsp;11 JavaScript ROOT v4 17 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .&nbsp;12 e evolution of m onitoring system : the INFN-CNAF case study 18 .&nbsp;. . . . . . . . . . .&nbsp;13 Statistical and Data Analysis Package in SWIFT 19 .&nbsp;. . . . . . . . . . . . . . . . . . . . .&nbsp;13 Analysis Tools in Geant4 10.2 20 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .&nbsp;14 Online &amp; Offline Storage and Processing for the upcom ing European XFEL Experim ents 21&nbsp;15 Future approach to tier-0 extension 22 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . .&nbsp;15 iii <br>Internal security consulting, reviews and penetration testing at CERN 23 .&nbsp;. . . . . . . .&nbsp;16 Web technology detection - for asset inventory and vulnerability m anagem ent 24 .&nbsp;. . .&nbsp;16 Integrating Containers in the CERN Private Cloud 25 .&nbsp;. . . . . . . . . . . . . . . . . . .&nbsp;17 CERN Com puting in Com m ercial Clouds 26 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . .&nbsp;18 Real-tim e com plex event processing for cloud resources 27 .&nbsp;. . . . . . . . . . . . . . . .&nbsp;18 Benchm arking cloud resources 28 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .&nbsp;19 RapidIO as a m ulti-purpose interconnect 29 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . .&nbsp;20 Evolution of CERN Print Services 30 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . . .&nbsp;20 Windows Term inal Server Orchestration 31 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . .&nbsp;21 Update on CERN Search based on SharePoint 2013 33 .&nbsp;. . . . . . . . . . . . . . . . . . .&nbsp;21 First experience with the new .CERN top level dom ain 34 .&nbsp;. . . . . . . . . . . . . . . . .&nbsp;22 Flash is Dead. Finally. 35 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .&nbsp;22 Access to WLCG resources: e X509-free pilot 36 .&nbsp;. . . . . . . . . . . . . . . . . . . . .&nbsp;23 Deploying FTS with Docker Swarm&nbsp;and Openstack Magnum&nbsp;37 .&nbsp;. . . . . . . . . . . . .&nbsp;24 DNS Load Balancing in the CERN Cloud 38 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . .&nbsp;24 Custom ization of the general fiꢀing tool genfit2 in PandaRoot 39 .&nbsp;. . . . . . . . . . . . .&nbsp;25 A world-wide databridge supported by a com m ercial cloud provider 40 .&nbsp;. . . . . . . . .&nbsp;26 DPM Evolution: a Disk Operations Managem ent Engine for DPM 41 .&nbsp;. . . . . . . . . . .&nbsp;27 Making the m ost of cloud storage - a toolkit for exploitation by WLCG experim ents. 42&nbsp;. 27 EOS Cross Tier Federation 43 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .&nbsp;28 EOS Developm ents 44 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .&nbsp;28 Using HEP Com puting Tools, Grid and Supercom puters for Genom e Sequencing Studies <br>45 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .&nbsp;29 </p><p>Support Vector Machines in HEP 46 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . . .&nbsp;30 Production Managem ent System&nbsp;for AMS Rem ote Com puting Centers 48 .&nbsp;. . . . . . . .&nbsp;31 Evolution of Monitoring System&nbsp;for AMS Science Operation Center 49 .&nbsp;. . . . . . . . . .&nbsp;31 Storage Strategy of AMS Science Data at Science Operation Center at CERN 50 .&nbsp;. . . . .&nbsp;32 AMS Data Production Facilities at Science Operation Center at CERN 51 .&nbsp;. . . . . . . .&nbsp;33 Context-aware distributed cloud com puting using CloudScheduler 52 .&nbsp;. . . . . . . . . .&nbsp;33 XrootdFS, a posix file system&nbsp;for XrootD 53 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . .&nbsp;34 iv <br>Research and application of OpenStack in Chinese Spallation Neutron Source Com puting environm ent 54 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .&nbsp;34 </p><p>Next Generation high perform ance, m ulti-dim ensional scalable data transfer 55 .&nbsp;. . . .&nbsp;35 Globally Distributed Soſtware Defined Storage 56 .&nbsp;. . . . . . . . . . . . . . . . . . . . .&nbsp;36 An efficient, m odular and sim ple tape archiving solution for LHC Run-3 57 .&nbsp;. . . . . . .&nbsp;36 Data Center Environm ental Sensor for safeguarding the CERN Data Archive 58 .&nbsp;. . . .&nbsp;37 PaaS for web applications with OpenShiſt Origin 59 .&nbsp;. . . . . . . . . . . . . . . . . . . .&nbsp;38 40-Gbps Data-Acquisition System&nbsp;for NectarCAM, a cam era for the Medium&nbsp;Size Telescopes of the Cherenkov Telescope Array 60 .&nbsp;. . . . . . . . . . . . . . . . . . . . . .&nbsp;38 </p><p>Model-independent partial wave analysis using a m assively-parallel fiꢀing fram ework 61&nbsp;39 CERN openlab Researched Technologies at Might Becom e Gam e Changers in Soſtware <br>Developm ent 62 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .&nbsp;40 </p><p>CERN openlab Knowledge Transfer and Innovation Projects 64 .&nbsp;. . . . . . . . . . . . . .&nbsp;40 CERN data services for LHC com puting 66 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . .&nbsp;41 CERN AFS Replacem ent project 67 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .&nbsp;41 From Physics&nbsp;to industry: EOS outside HEP 68 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . .&nbsp;42 Big Data Analytics for the Future Circular Collider Reliability and Availability Studies 69&nbsp;42 CERNBox: the data hub for data analysis 70 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . .&nbsp;43 Tape SCSI m onitoring and encryption at CERN 71 .&nbsp;. . . . . . . . . . . . . . . . . . . . .&nbsp;44 Wi-Fi service enhancem ent at CERN 72 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . .&nbsp;44 A graphical perform ance analysis and exploration tool for Linux-perf 73 .&nbsp;. . . . . . . .&nbsp;45 e role of dedicated com puting centers in the age of cloud com puting 74 .&nbsp;. . . . . . . .&nbsp;45 Next Generation Monitoring 75 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .&nbsp;46 e m erit of data processing application elasticity 76 .&nbsp;. . . . . . . . . . . . . . . . . . . .&nbsp;47 Identifying m em ory allocation paꢀerns in HEP soſtware 77 .&nbsp;. . . . . . . . . . . . . . . .&nbsp;48 Finding unused m em ory allocations with FOM-tools 78 .&nbsp;. . . . . . . . . . . . . . . . . .&nbsp;48 SDN-NGenIA A Soſtware Defined Next Generation integrated Architecture for HEP and <br>Data Intensive Science 79 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .&nbsp;49 </p><p>A Com parison of Deep Learning Architectures with GPU Acceleration and eir Applications 80 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .&nbsp;50 </p><p>CERN’s Ceph infrastructure: OpenStack, NFS, CVMFS, CASTOR, and m ore! 81 .&nbsp;. . . . .&nbsp;51 LHCb Kalm an Filter cross architectures studies 82 .&nbsp;. . . . . . . . . . . . . . . . . . . . .&nbsp;51 v<br>Unified Monitoring Architecture for IT and Grid Services 83 .&nbsp;. . . . . . . . . . . . . . .&nbsp;52 Developm ent of DAQ Soſtware for CULTASK Experim ent 84 .&nbsp;. . . . . . . . . . . . . . .&nbsp;53 e new ATLAS Fast Calorim eter Sim ulation 85 .&nbsp;. . . . . . . . . . . . . . . . . . . . . .&nbsp;54 ATLAS com puting on Swiss Cloud SWITCHengines 86 .&nbsp;. . . . . . . . . . . . . . . . . .&nbsp;55 Global EOS: exploring the 300-m s-latency region 87 .&nbsp;. . . . . . . . . . . . . . . . . . . .&nbsp;55 ATLAS and LHC com puting on CRAY 88 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . .&nbsp;56 Analysis of em pty ATLAS pilot jobs and subsequent resource usage on grid sites 89 .&nbsp;. .&nbsp;56 e ATLAS com puting challenge for HL-LHC 90 .&nbsp;. . . . . . . . . . . . . . . . . . . . . .&nbsp;57 Networks in ATLAS 91 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .&nbsp;58 Production Experience with the ATLAS Event Service 92 .&nbsp;. . . . . . . . . . . . . . . . .&nbsp;58 Com puting shiſts to m onitor ATLAS distributed com puting infrastructure and operations <br>93 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .&nbsp;59 </p><p>Consolidation of Cloud Com puting in ATLAS 95 .&nbsp;. . . . . . . . . . . . . . . . . . . . . .&nbsp;60 Giving pandas ROOT to chew on: experiences with the XENON1T Dark Maꢀer experim ent <br>96 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .&nbsp;61 </p><p>Advancing data m anagem ent and analysis in different scientific disciplines 97 .&nbsp;. . . . .&nbsp;62 Interfacing HTCondor-CE with OpenStack 98 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . .&nbsp;62 PODIO - Applying plain-old-data for defining physics data m odels 100 .&nbsp;. . . . . . . . .&nbsp;63 Distributed Metadata Managem ent of Mass Storage System&nbsp;in High Energy of Physics 102&nbsp;64 SCEAPI: A Unified Restful Web APIs for High-Perform ance Com puting 103 .&nbsp;. . . . . . .&nbsp;65 Message ꢁeues for Online Reconstruction on the Exam ple of the PANDA Experim ent 104&nbsp;65 XROOT developm ent update - support for m etalinks and extrem e copy 106 .&nbsp;. . . . . . .&nbsp;66 e DAQ system&nbsp;for the AEgIS experim ent 107 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . .&nbsp;67 e Muon Ionization Cooling Experim ent Analysis User Soſtware 108 .&nbsp;. . . . . . . . . .&nbsp;67 e future of academ ic com puting security 109 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . .&nbsp;68 Optim izing ROOT’s Perform ance Using C++ Modules 110 .&nbsp;. . . . . . . . . . . . . . . . .&nbsp;68 Enabling Federated Access for HEP 111 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . .&nbsp;69 Full and Fast Sim ulation Fram ework for the Future Circular Collider Studies 112 .&nbsp;. . . .&nbsp;70 FPGA based data processing in the ALICE High-Level Trigger in LHC Run 2 113 .&nbsp;. . . .&nbsp;71 Events visualisation in ALICE - current status and strategy for Run 3 114 .&nbsp;. . . . . . . .&nbsp;72 vi <br>Kalm an filter tracking on parallel architectures 115 .&nbsp;. . . . . . . . . . . . . . . . . . . . .&nbsp;72 Perform ance of the CMS Event Builder 116 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . .&nbsp;73 An interactive and com prehensive working environm ent for high-energy physics soſtware with Python and jupyter notebooks 117 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . .&nbsp;74 </p><p>SND DAQ system&nbsp;evolution 118 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .&nbsp;75 Using ALFA for high throughput, distributed data transm ission in ALICE O2 system&nbsp;119 76 Design of the data quality control system&nbsp;for the ALICE O2 120 .&nbsp;. . . . . . . . . . . . .&nbsp;76 A perform ance study of WebDav access to storages within the Belle II collaboration. 121&nbsp;77 A lightweight federation of the Belle II storages through dynafed 122 .&nbsp;. . . . . . . . . .&nbsp;78 Integration of Oracle and Hadoop: hybrid databases affordable at scale 123 .&nbsp;. . . . . . .&nbsp;78 e ATLAS Production System&nbsp;Evolution. New&nbsp;Data Processing and Analysis Paradigm for the LHC Run2 and High-Lum inosity 124 .&nbsp;. . . . . . . . . . . . . . . . . . . . . .&nbsp;79 </p><p>Large scale soſtware building with CMake in ATLAS 125 .&nbsp;. . . . . . . . . . . . . . . . .&nbsp;80 Monitoring of Com puting Resource Use of Active Soſtware Releases at ATLAS 127 .&nbsp;. . .&nbsp;81 Developm ent and perform ance of track reconstruction algorithm s at the energy frontier with the ATLAS detector 128 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .&nbsp;82 </p><p>Prim ary Vertex Reconstruction with the ATLAS experim ent 130 .&nbsp;. . . . . . . . . . . . .&nbsp;82 Using m achine learning algorithm s to forecast network and system&nbsp;load m etrics for ATLAS <br>Distributed Com puting 131 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .&nbsp;83 </p><p>Autom atic rebalancing of data in ATLAS distributed data m anagem ent 132 .&nbsp;. . . . . . .&nbsp;84 ATLAS Sim ulation using Real Data: Em bedding and Overlay 133 .&nbsp;. . . . . . . . . . . . .&nbsp;84 Metadata for fine-grained processing at ATLAS 135 .&nbsp;. . . . . . . . . . . . . . . . . . . .&nbsp;85 e ATLAS EventIndex General Dataflow and Monitoring Infrastructure 136 .&nbsp;. . . . . .&nbsp;86 ACTS: from&nbsp;ATLAS soſtware towards a com m on track reconstruction soſtware 137&nbsp;. . .&nbsp;86 ATLAS soſtware stack on ARM64 138 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . .&nbsp;87 Modernizing the ATLAS Sim ulation Infrastructure 139 .&nbsp;. . . . . . . . . . . . . . . . . . .&nbsp;88 PanDA for ATLAS distributed com puting in the next decade 140 .&nbsp;. . . . . . . . . . . . .&nbsp;88 Mem ory handling in the ATLAS subm ission system&nbsp;from job&nbsp;definition to sites lim its 141&nbsp;89 Evolution and experience with the ATLAS Sim ulation at Point1 Project 143 .&nbsp;. . . . . . .&nbsp;90 C3PO - A Dynam ic Data Placem ent Agent for ATLAS Distributed Data Managem ent 144&nbsp;91 Rucio WebUI - e Web Interface for the ATLAS Distributed Data Managem ent 145 .&nbsp;. .&nbsp;91 vii <br>Object-based storage integration within the ATLAS DDM system&nbsp;146 .&nbsp;. . . . . . . . . .&nbsp;92 Experiences with the new ATLAS Distributed Data Managem ent System&nbsp;147 .&nbsp;. . . . . .&nbsp;93 Rucio Auditor - Consistency in the ATLAS Distributed Data Managem ent System&nbsp;148 .&nbsp;. 93 How To Review 4 Million Lines of ATLAS Code 149 .&nbsp;. . . . . . . . . . . . . . . . . . . .&nbsp;94 Frozen-shower sim ulation of electrom agnetic showers in the ATLAS forward calorim eters <br>150 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .&nbsp;95 </p><p>ATLAS World-cloud and networking in PanDA 151 .&nbsp;. . . . . . . . . . . . . . . . . . . .&nbsp;95 Data intensive ATLAS workflows in the Cloud 152 .&nbsp;. . . . . . . . . . . . . . . . . . . . .&nbsp;96 Volunteer Com puting Experience with ATLAS@Hom e 153 .&nbsp;. . . . . . . . . . . . . . . .&nbsp;97 Assessm ent of Geant4 Maintainability with respect to Soſtware Engineering References 154&nbsp;97 Migrating the Belle II Collaborative Services and Tools 155 .&nbsp;. . . . . . . . . . . . . . . .&nbsp;98 ROOT and new program m ing paradigm s 156 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . .&nbsp;99 Status and Evolution of ROOT 157 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 Com puting Perform ance of GeantV Physics Models 158 .&nbsp;. . . . . . . . . . . . . . . . . . 101 Soſtware Aspects of the Geant4 Validation Repository 159 .&nbsp;. . . . . . . . . . . . . . . . . 102 MPEXS: A CUDA MonteCarlo of the sim ulaiton of electrom agnetic interactions 160 .&nbsp;. . 103 Multi-threaded Geant4 on Intel Many Integrated Core architectures 161 .&nbsp;. . . . . . . . . 103 CEPHFS: a new generation storage platform&nbsp;for Australian high energy physics 162 .&nbsp;. . 104 Evaluation of lightweight site setups within WLCG infrastructure 165 .&nbsp;. . . . . . . . . . 105 e Cherenkov Telescope Array production system&nbsp;for Monte Carlo sim ulations and analysis 166 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 </p><p>Processing and ꢁality Monitoring for the ATLAS Tile Hadronic Calorim eter data 167 .&nbsp;. 107 Data acquisition and processing in the ATLAS Tile Calorim eter Phase-II Upgrade Dem onstrator 168 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 </p><p>Validation of Electrom agnetic Physics Models for Parallel Com puting Architectures in the <br>GeantV project 169 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 </p><p>Stochastic optim isation of GeantV code by use of genetic algorithm s 170 .&nbsp;. . . . . . . . 109 GeantV phase 2: developing the particle transport library 171 .&nbsp;. . . . . . . . . . . . . . . 110 New Directions in the CernVM File System&nbsp;172 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . 111 Toward a CERN Virtual Visit Service 173 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . 111 Perform ance and evolution of the DAQ system&nbsp;of the CMS experim ent for Run-2 174&nbsp;. . 112 viii <br>Upgrading and Expanding Lustre Storage for use with the WLCG 175 .&nbsp;. . . . . . . . . . 113 XRootD Popularity on Hadoop Clusters 176 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . 114 e Vacuum&nbsp;Platform 177&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 e Machine/Job Features m echanism&nbsp;178 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . 115 Integration of grid and local batch system&nbsp;resources at DESY 179 .&nbsp;. . . . . . . . . . . . . 116 Web Proxy Auto Discovery for WLCG 180 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . 116 Visualization of historical data for the ATLAS detector controls 181 .&nbsp;. . . . . . . . . . . 117 e detector read-out in ALICE during Run 3 and 4 182 .&nbsp;. . . . . . . . . . . . . . . . . . 118 Grid Access with Federated Identities 183 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . 119 Recent progress of Geant4 electrom agnetic physics for LHC and other applications 184&nbsp;. 119 Scaling Up a CMS Tier-3 Site with Cam pus Resources and a 100 Gb/s Network Connection: <br>What Could Go Wrong? 185 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 </p><p>Facilitating the deploym ent and exploitation of HEP Phenom enology codes using INDIGO- <br>Datacloud tools 186 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 </p><p>C2MON: a m odern open-source platform&nbsp;for data acquisition, m onitoring and control 187 122 ARC CE cache as a solution for lightweight Grid sites in ATLAS 188 .&nbsp;. . . . . . . . . . . 123 Exploiting Opportunistic Resources for ATLAS with ARC CE and the Event Service 189 .&nbsp;123 Evolution of user analysis on the Grid in ATLAS 190 .&nbsp;. . . . . . . . . . . . . . . . . . . . 124 AthenaMT: Upgrading the ATLAS Soſtware Fram ework for the Many-Core World with <br>Multi-reading 191 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 </p><p>An Oracle-based Event Index for ATLAS 192 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . 125 Collecting conditions usage m etadata to optim ize current and future ATLAS soſtware and processing 193 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 </p><p>Integration of the Titan supercom puter at OLCF with the ATLAS Production System&nbsp;194 127 First use of LHC Run 3 Conditions Database infrastructure for auxiliary data files in ATLAS <br>195 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128 </p><p>Multi-threaded ATLAS Sim ulation on Intel Knights Landing Processors 196 .&nbsp;. . . . . . . 128 e Fast Sim ulation Chain for ATLAS 197 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . 129 Integration of the Chinese HPC Grid in ATLAS Distributed Com puting 198 .&nbsp;. . . . . . . 130 How to keep the Grid full and working with ATLAS production and physics jobs 199&nbsp;. . 130 A study of data representations in Hadoop to optim ize data storage and search perform ance of the ATLAS EventIndex 200 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 </p><p>ix <br>AGIS: Integration of new technologies used in ATLAS Distributed Com puting 201 .&nbsp;. . . 131 A Roadm ap to Continuous Integration for ATLAS soſtware developm ent 202 .&nbsp;. . . . . . 132 ATLAS Metadata Interface (AMI), a generic m etadata fram ework 204 .&nbsp;. . . . . . . . . . 133 Deploying the ATLAS Metadata Interface (AMI) on the cloud with Jenkins 205 .&nbsp;. . . . . 133 ATLAS Fast Physics Monitoring: TADA 206 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . 134 A new m echanism&nbsp;to persistify the detector geom etry of ATLAS and serving it through an experim ent-agnostic REST API 207 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . 135 </p><p>ATLAS Distributed Com puting experience and perform ance during the LHC Run-2 208&nbsp;. 135 Benefits and perform ance of ATLAS approaches to utilizing opportunistic resources 209 .&nbsp;136 DIRAC universal pilots 211 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 e ATLAS Com puting Agora : a resource web site for citizen science projects 212 .&nbsp;. . . 138 HiggsHunters - a citizen science project for ATLAS 213 .&nbsp;. . . . . . . . . . . . . . . . . . 139 Big Data Analytics Tools as Applied to ATLAS Event Data 215 .&nbsp;. . . . . . . . . . . . . . 139 Event visualisation in ATLAS: current soſtware technologies / future prospects and trends <br>216 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 </p><p>DIRAC in Large Particle Physics Experim ents 217 .&nbsp;. . . . . . . . . . . . . . . . . . . . . 141 ATLAS Data Preparation in Run 2 218 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 Caching Servers for ATLAS 219 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 An Analysis of Reproducibility and Non-Determ inism&nbsp;in HEP Soſtware and ROOT Data <br>220 .&nbsp;. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 </p>