Welcome and introduction Room: 31/3-004 - IT Amphitheatre

Evaluation of Erasure Coding & other features of Hadoop 3

• Nazerke Seidan

Room: 31/3-004 - IT Amphitheatre Apache Hadoop is a set of 2 domains: data computation such as Spark, MapReduce, Flink, etc and data storage - HDFS. HDFS is a distributed file system. Current HDFS provides 3x replication for data redundancy and availability. But it has 200% storage overhead. However there is a big improvement in Hadoop 3 for replication which is Erasure Coding (EC). Erasure Coding gives the same level of fault tolerance as 3x replication but with much less storage space. My project aims to evaluate the performance of Erasure Coding.

Automate Cloud Infrastructure deployment for Oracle Cloud and Openstack

• Priyanshu Khandelwal

Room: 31/3-004 - IT Amphitheatre

Neuromorphic Computing in High Energy Physics

• Bartlomiej Pawel Borzyszkowski

Room: 31/3-004 - IT Amphitheatre Spiking neural networks are an interesting candidate for signal processing at the High-Luminosity LHC, the next stage of the LHC upgrade. For HL-LHC, new particle detectors will be built, what will allow to take a time-sequence of snapshots for a given collision. This additional information will allow to separate the signal belonging to the interesting collision from those generated parasitic collisions occurring at the same time (in-time pileup) or before/after the interesting one (out-of-time pileup). By powering the LHC real-time processing with spiking neural networks, one could be able to apply advance and accurate signal-to-noise discrimination algorithms in real time, without affecting the overall system latency beyond the given tolerance. This project is investigating the potential of Spiking neural networks deployed on neuromorphic chips as a technological solution to increase the precision of the upgraded CMS detector for HL-LHC. We propose to focus on the characterization of a particle type (classification) based on the recorded time profile of the signal, and to determine the arrival time of the particle on the detector (regression). These informations can be used to determine if a particle belongs to the interesting collision or to one of the parasitic collisions.

Evaluate ElastAlert for IT-DB use cases

• Dimitra Chatzichrysou

Room: 31/3-004 - IT Amphitheatre

Explorations in heterogeneous computing

• Davide Marcato

Room: 31/3-004 - IT Amphitheatre Exploring the use of cupla to write accelerator-independent code.

Chaos Monkey

• Elizaveta Svitanko

Room: 31/3-004 - IT Amphitheatre Chaos Monkey

Comparing different approaches for hit finding and trigger generation

• Jan Niklas Bohm

Room: 31/3-004 - IT Amphitheatre

Portable Early Prediction of Sepsis from Clinical Data on Intel Myriad X

• Priyanka Kumar Mathur

Room: 31/3-004 - IT Amphitheatre

FaaS on Kubernetes with Knative

• Juan Carlos Gallegos Dupuis

Room: 31/3-004 - IT Amphitheatre Knative is a relatively new technology that extends the Kubernetes API to support deployment of server-less apps. On the CERN cloud team, we are investigating Knative as a candidate technology for offering Function-as-a-Service (FaaS) infrastructure to CERN cloud users.

Machine Learning and Kubernetes

• Fedor Kitashov

Room: 31/3-004 - IT Amphitheatre Fedor Kitashov

Anomaly Detection in the Elasticsearch Service

• Jennifer Rosina Andersson

Room: 31/3-004 - IT Amphitheatre

Performance Study of Parquet Codecs

• Javier Garcia Rubio

Room: 31/3-004 - IT Amphitheatre Performance Study of Parquet Codecs

Performance monitoring using intel performance counters for HEP applications

• Khadidja Hadj Henni

Room: 31/3-004 - IT Amphitheatre

Graph Neural Network(GNN) Inference of FPGA

• Kazi Ahmed Asif Fuad

Room: 31/3-004 - IT Amphitheatre

Modeling HEP Workloads under Multidimensional Restrictions

• Riccardo Maganza

Room: 31/3-004 - IT Amphitheatre

Progressive GAN for satellite image generation

• Yoann Boget

Room: 31/3-004 - IT Amphitheatre

support JS rendering websites in The CERN Search Crawler

• Khanssa El Amrouni (Openlab Summer Student)

Room: 31/3-004 - IT Amphitheatre support JS rendering websites in The CERN Search Crawler

Benchmarking and optimising large scale parallel workflows

• Jesus Perales Hernandez

Room: 31/3-004 - IT Amphitheatre

Building effective Restful APIs with Oracle Rest Data Services (ORDS) 19

• Iheb Eddine Imad

Room: 31/3-004 - IT Amphitheatre

Big data analysis and machine learning in the cloud

• Michal Bien

Room: 31/3-004 - IT Amphitheatre

Accelerating TMVA Deep Learning - Integration of the NVIDIA cuDNN Library

• Joana Niermann

Room: 31/3-004 - IT Amphitheatre

Farewell and best lightning talk drink openlab summer students Room: 31/3-004 - IT Amphitheatre