Project Proposals by CERN in 2019

At CERN, the European Organization for Nuclear Research, physicists and engineers are probing the fundamental structure of the universe. They use the world’s largest and most complex scientific instruments to study the basic constituents of matter – the fundamental particles.

Project List

• Implementation and optimisation in Acts of algorithms exposed in TrackML challenge

• Real-time conditions data distribution for the Online data processing of the ALICE experiment

• Python Analysis Package for AWAKE

• Improving the precision of algorithms to hunt particles

• GPU Race for Particle Hunting

• Kalman Filter in Rust

• SIMT to SPMD code translation

• Implement Event based Seeding and Multi-Threading

• CERNBox: Bring Your Own Application

• Porting the CernVM File System Client to Windows

• Tessellated geometry support for DD4hep and ROOT/TGeo

• Monitoring DIRAC components

• Distributed Computing Resources: aggregation, usage, monitoring

• VectorFlow - a vector processing service

• Go-HEP/hifact - Create a HistFactory compatible Go package for statistical models

• Testing framework for Jupyter notebooks

• Molr - Operational

• Implementation of an HDF5 IO layer for PODIO

• Improve support for CUDA in Clang

• Optimisation of small matrix operations using Eigen on GPU

• Phoenix, interactive data visualization - Development of an experiment independent javascript event display framework and data format

• Implement a GlobalModuleIndex in ROOT and Cling

• Ipopt (Interior Point OPTimizer) for ROOT

• Apache Parquet support for ROOT’s RDataFrame

• Next generation Big Data Analysis monitoring tools with ROOT

• Rucio - Exascale data management

• Rucio - WebDAV/HTTP-based transfer orchestration

• Package manager for SWAN/Jupyterlab

• Creation and usage of disposable Spark on Kubernetes clusters from notebook service (SWAN) for distributed physics analysis

• Hardware acceleration of scattering routines in SixTrack

• Generative Adversarial Networks for Particle Physics Applications

• Development of LSTM and GRU layers in TMVA

• Optimization of Neural Network Operations

• Integrate of cuDNN library in TMVA

• Feature Extraction in TMVA

• Implementation of ONNX operators for Tensor Manipulation and Deep Learning

• Improved usage of Intel Embree ray-tracing kernels in VecGeom

• Persistency for VecGeom geometry

• Ansible module for the SIMPLE Grid Framework

• Python Components for the SIMPLE Grid Framework