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CERN openlab Technical Workshop

23–24 Jan 2019
CERN
Europe/Zurich timezone

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Contribution List

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  1. 1. Welcome and goals of the workshop
    Maria Girone (CERN)
    23/01/2019, 09:30
  2. 10. Intel big data analytics
    Vaggelis Motesnitsalis (CERN)
    23/01/2019, 09:45
  3. 11. Oracle Data Analytics and Autonomous Data Warehouse service on the Cloud
    Manuel Martin Marquez (CERN)
    23/01/2019, 10:05
  4. 12. Siemens Data Analytics and SCADA evolution status report
    Filippo Maria Tilaro (CERN)
    23/01/2019, 10:25
  5. 25. Partner talk Siemens: AI on the machine level in industrial automation
    Ingo Thon (Siemens), Jose Soler Garrido (Siemens)
    23/01/2019, 11:15

    Artificial intelligence, with all its different facets, makes a considerable contribution, especially in industry, toward reducing the usual expense of programming and engineering, making the control logic more agile and flexible with regard to changes in the ambient conditions and structuring production processes with greater flexibility and precision. With Future of Automation, Siemens is...

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  6. 6. Intel fast simulation
    Federico Carminati (CERN)
    23/01/2019, 11:35
  7. 36. IBM Evaluation of power architectures for machine learning
    Ahmad Siar Hesam (Technische Universiteit Delft (NL)), Daniel Hugo Campora Perez (Universidad de Sevilla (ES))
    23/01/2019, 11:55
  8. 13. Micron: Exploring Accelerated Machine Learning for Experiment Data Analytics
    Emilio Meschi (CERN), Maurizio Pierini (CERN)
    23/01/2019, 13:30
  9. 42. Partner talk Micron: Advanced Computing Solutions
    Mark Hur (Micron)
    23/01/2019, 13:50

    Micron introduces machine learning products designed to accelerate deep learning algorithm in hardware.
    Coupled with FWDNXT Inference Engine, the programmable logic products from Micron offer the ability to execute complex neural networks in hardware.
    We present expertise in neural network design, compiler and hardware acceleration. We present FWDNXT SDK software as an alternative to graphic...

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  10. 14. E4: A Testbed for GPU Accelerated Applications
    Felice Pantaleo (CERN)
    23/01/2019, 14:10
  11. 41. Status of the DEEP-EST project and outlook
    Viktor Khristenko (CERN)
    23/01/2019, 14:30
  12. 15. Intel: Fast deep neural network inference on FPGAs
    Jennifer Ngadiuba (CERN)
    23/01/2019, 14:50
  13. 37. Storage Services evolution at CERN
    Alberto Pace (CERN)
    23/01/2019, 15:50
  14. 21. Comtrade EOS productization
    Luca Mascetti (CERN)
    23/01/2019, 16:10
  15. 16. Oracle Management Cloud: A unified monitoring platform
    Aimilios Tsouvelekakis (Ministere des affaires etrangeres et europeennes (FR))
    23/01/2019, 16:30
  16. 20. Running JAVA application servers on Kubernetes
    Antonio Nappi (CERN)
    23/01/2019, 16:50
  17. 43. Oracle partner talk: Making databases smarter and faster: innovations enabled by engineering software and hardware together
    Cris Pedregal (Oracle)
    23/01/2019, 17:10

    We present examples that illustrate how jointly designing a database and its underlying hardware enables innovations that overcome substantial technological challenges. Some are fundamental advances in the state of the art, and all yield reliability and performance improvements that discrete component (“converged”) computer systems can rarely attain. For example, tailoring internal network...

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  18. 45. Poster 1: Machine learning pipelines with Apache Spark and Intel Big DL
    Matteo Migliorini (Universita e INFN, Padova (IT)), Viktor Khristenko (CERN)
    23/01/2019, 17:35
  19. 46. Poster 2: DAQDB: a Key-value store for data acquisition
    Danilo Cicalese (CERN)
    23/01/2019, 17:45
  20. 47. Poster 3: Monitoring JAVA application servers
    Viktor Kozlovszky (CERN)
    23/01/2019, 17:55

    Successful operations require constant awareness about the system state. Time to time, different obstacles show up, which have to be resolved as fast as possible, because user satisfaction highly depends on time. The prompt problem resolution depends on two steps: localization of the malfunctioning component and the recovery action. Monitoring aims to strike down the exploration time and...

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  21. 48. Poster 4: Physics data analysis and data reduction at scale with Apache Spark
    Vaggelis Motesnitsalis (CERN)
    23/01/2019, 18:05
  22. 49. Poster 5: Physics data processing and machine learning in the Cloud
    Luca Canali (CERN), Riccardo Castellotti (CERN)
    23/01/2019, 18:15
  23. 44. Partner talk Intel: 2019 technology innovation
    Andrea Luiselli (Intel), Francisco Perez (Intel)
    24/01/2019, 09:00
  24. 27. Intel high performance cloud caching technologies
    Danilo Cicalese (CERN)
    24/01/2019, 09:20
  25. 26. Extreme Networks Flow optimizer
    Stefan Nicolae Stancu (CERN)
    24/01/2019, 09:40
  26. 28. Huawei OpenStack Cloud
    Ms Surya Seetharaman (CERN)
    24/01/2019, 10:00
  27. 29. Rackspace: Cloud storage performance
    Julien Collet (CERN)
    24/01/2019, 10:20
  28. 40. Introduction to Multi-disciplinary Platforms for Science
    Alberto Di Meglio (CERN)
    24/01/2019, 11:10
  29. 34. Mobility
    Fons Rademakers (CERN)
    24/01/2019, 11:25
  30. 30. Biodynamo
    Lukas Breitwieser (openlab)
    24/01/2019, 11:40
  31. 32. Medical Data Analysis
    Alberto Di Meglio (CERN)
    24/01/2019, 11:55
  32. 31. Satellite Image Analysis for UNOSAT
    Taghi Aliyev (Universiteit Maastricht (NL))
    24/01/2019, 12:10
  33. 33. Smart platforms for science
    Taghi Aliyev (Universiteit Maastricht (NL))
    24/01/2019, 12:25
  34. 22. Quantum computing and CERN openlab
    Federico Carminati (CERN)
    24/01/2019, 14:00
  35. 23. Machine Learning in quantum computing
    Dr Sofia Vallecorsa (Gangneung-Wonju National University (KR))
    24/01/2019, 14:20
  36. 24. Applying IBM quantum computing to LHC physics analysis Higgs coupling to two top quarks
    Wen Guan (University of Wisconsin (US))
    24/01/2019, 14:40
  37. 2. Wrap up and best poster
    24/01/2019, 15:00
  38. 39. CMS applications to data quality and data certification
  39. 8. IBM CMS Simulation
  40. 7. IBM LHCb Simulation
  41. 35. IBM Simulation
  42. 3. Intel big data
  43. 9. Lunch and poster session
  44. 17. Oracle ADWS
  45. 4. Oracle data analytics
  46. 5. Siemens data analytics for industrial controls
  47. 38. Welcome