On Tuesday 13 August and Thursday 15 August, the 2019 CERN openlab summer students will present their work at a dedicated public Lighting Talk sessions.
In a 5-minute presentation, each student will introduce the audience to their project, explain the technical challenges they have faced and describe the results of what they have been working on for the past few weeks.
It will be a great opportunity for the students to showcase the progress they have made so far and for the audience to be informed about various information-technology projects, the solutions that the students have come up with and the potential future challenges they have identified.
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.
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.
Exploring the use of cupla to write accelerator-independent code.
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.
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