CERN openlab Technical Workshop

Name: CERN openlab Technical Workshop
Start: 2019-01-23T09:30:00+01:00
End: 2019-01-24T17:40:00+01:00
Location: CERN

Jan 23 – 24, 2019

CERN

Europe/Zurich timezone

Contact

kristina.gunne@cern.ch

Contribution List

1. Welcome and goals of the workshop

Maria Girone (CERN)

1/23/19, 9:30 AM

10. Intel big data analytics

Vaggelis Motesnitsalis (CERN)

1/23/19, 9:45 AM

Computing architectures for machine learning, data acquisition and processing part 1

11. Oracle Data Analytics and Autonomous Data Warehouse service on the Cloud

Manuel Martin Marquez (CERN)

1/23/19, 10:05 AM

Computing architectures for machine learning, data acquisition and processing part 1

12. Siemens Data Analytics and SCADA evolution status report

Filippo Maria Tilaro (CERN)

1/23/19, 10:25 AM

Computing architectures for machine learning, data acquisition and processing part 1

25. Partner talk Siemens: AI on the machine level in industrial automation

Ingo Thon (Siemens), Jose Soler Garrido (Siemens)

1/23/19, 11:15 AM

Computing architectures for machine learning, data acquisition and processing part 1

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...

6. Intel fast simulation

Federico Carminati (CERN)

1/23/19, 11:35 AM

Computing architectures for machine learning, data acquisition and processing part 1

36. IBM Evaluation of power architectures for machine learning

Ahmad Siar Hesam (Technische Universiteit Delft (NL)), Daniel Hugo Campora Perez (Universidad de Sevilla (ES))

1/23/19, 11:55 AM

Computing architectures for machine learning, data acquisition and processing part 1

13. Micron: Exploring Accelerated Machine Learning for Experiment Data Analytics

Emilio Meschi (CERN), Maurizio Pierini (CERN)

1/23/19, 1:30 PM

Computing architectures for machine learning, data acquisition and processing part 2

42. Partner talk Micron: Advanced Computing Solutions

Mark Hur (Micron)

1/23/19, 1:50 PM

Computing architectures for machine learning, data acquisition and processing part 2

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...

14. E4: A Testbed for GPU Accelerated Applications

Felice Pantaleo (CERN)

1/23/19, 2:10 PM

Computing architectures for machine learning, data acquisition and processing part 2

41. Status of the DEEP-EST project and outlook

Viktor Khristenko (CERN)

1/23/19, 2:30 PM

Computing architectures for machine learning, data acquisition and processing part 2

15. Intel: Fast deep neural network inference on FPGAs

Jennifer Ngadiuba (CERN)

1/23/19, 2:50 PM

Computing architectures for machine learning, data acquisition and processing part 2

37. Storage Services evolution at CERN

Alberto Pace (CERN)

1/23/19, 3:50 PM

Data Center Technologies

21. Comtrade EOS productization

Luca Mascetti (CERN)

1/23/19, 4:10 PM

Data Center Technologies

16. Oracle Management Cloud: A unified monitoring platform

Aimilios Tsouvelekakis (Ministere des affaires etrangeres et europeennes (FR))

1/23/19, 4:30 PM

Data Center Technologies

20. Running JAVA application servers on Kubernetes

Antonio Nappi (CERN)

1/23/19, 4:50 PM

Data Center Technologies

43. Oracle partner talk: Making databases smarter and faster: innovations enabled by engineering software and hardware together

Cris Pedregal (Oracle)

1/23/19, 5:10 PM

Data Center Technologies

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...

45. Poster 1: Machine learning pipelines with Apache Spark and Intel Big DL

Matteo Migliorini (Universita e INFN, Padova (IT)), Viktor Khristenko (CERN)

1/23/19, 5:35 PM

Cocktail and poster session

46. Poster 2: DAQDB: a Key-value store for data acquisition

Danilo Cicalese (CERN)

1/23/19, 5:45 PM

Cocktail and poster session

47. Poster 3: Monitoring JAVA application servers

Viktor Kozlovszky (CERN)

1/23/19, 5:55 PM

Cocktail and poster session

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...

48. Poster 4: Physics data analysis and data reduction at scale with Apache Spark

Vaggelis Motesnitsalis (CERN)

1/23/19, 6:05 PM

Cocktail and poster session

49. Poster 5: Physics data processing and machine learning in the Cloud

Luca Canali (CERN), Riccardo Castellotti (CERN)

1/23/19, 6:15 PM

Cocktail and poster session

44. Partner talk Intel: 2019 technology innovation

Andrea Luiselli (Intel), Francisco Perez (Intel)

1/24/19, 9:00 AM

Data Center Technologies