| Topic: | Automated process monitoring with workflow management software towards sustainable computing |
| Summary: |
In High Energy Physics many calculations are done on complex data. Since every calculation increases energy consumption, it is important to do these as sensible and efficient as possible. While some computing sites already provide data on the power usage of these calculations, it is still difficult to monitor the energy consumption of whole analysis workflows. In this project, an existing workflow management based on the software luigi will be extended to automatically collect data on the energy usage of its tasks.
|
| You learn: | workflow management software (luigi), monitoring and accounting of computing resources, databases |
| Prerequisites: | good proficiency in English is required, good knowledge in Linux is required, exam in ModEx1+2 is required, first experience in own software projects is a plus |
| Supervisor: | Prof. Dr. Torben Ferber |
| Contact: | Jonas Eppelt |
| Last update: | 03.06.2024 |
| Topic: | Performance optimization for Machine Learning reconstruction algorithms |
| Summary: |
This thesis project centers on optimizing the performance of Machine Learning (ML) reconstruction algorithms, particularly focusing on Python algorithms and their integration with C++ interfaces for the high-level trigger at Belle I running on a large computing cluster.
As a bachelor student, your primary objective will be to streamline the execution of ML reconstruction algorithms on CPUs and GPUs.
You will explore techniques such as algorithm parallelization, memory management, and code optimization to achieve optimal performance in both Python and C++ environments. Through rigorous benchmarking and profiling, you will evaluate the impact of your optimizations on the reconstruction speed and resource utilization.
By the end of your thesis, you will have contributed to the development of robust and efficient ML reconstruction pipelines, essential for high-level trigger systems in particle physics experiments.
|
| You learn: | advanced track reconstruction techniques in particle physics, advanced C++ optimization |
| Prerequisites: | good proficiency in English is required, good knowledge in Python and C++ programming (e.g. lecture and all exercises in "Rechnernutzung") is required, exam in ModEx1+2 is required, first experience in own software projects is a plus, basic knowledge in machine learning tools (e.g. Tensorflow or PyTorch) is a plus |
| Supervisor: | Prof. Dr. Torben Ferber |
| Contact: | Dr. Giacomo De Pietro |
| Last update: | 01.08.2024 |
| Topic: | Performance study of NN-Training with data streaming from remote storage |
| Summary: |
In High Energy Physics, datasets of several hundreds of GB are used for machine learning. Often, these datasets are stored at remote storage servers. Sometimes it is not possible to keep them on the computing machine. It could be an option to stream the data from the remote storage. It is possible to use a cache that keeps the data near the computing resource. That reduces the network traffic and the access latency for the files. The performance during streaming with and without cache should be studied.
|
| You learn: | concepts and usage of distributed computing, transfer protocol, and storage/caching software XRootD |
| Prerequisites: | good proficiency in English is required, good knowledge in Linux is required, exam in ModEx1+2 is required, first experience in own software projects is a plus, basic knowledge in machine learning tools (e.g. Tensorflow or PyTorch) is a plus |
| Supervisor: | Prof. Dr. Torben Ferber |
| Contact: | Dr. Matthias Schnepf |
| Last update: | 01.08.2024 |
| Topic: | Multi-core support for Belle II Grid |
| Summary: |
Belle II uses a distributed computing infrastructure called Grid. Currently, Belle II supports only one CPU core per application in the Grid. The use of multiple CPU cores would speedup applications and reduce memory footprint. You can improve the Belle II Grid computing by configuring, implementing, and testing the support for multiple CPU cores in the Belle II Grid.
|
| You learn: | usage and configuration of the workflow management system DIRAC, Python, bash, Grid computing |
| Prerequisites: | good proficiency in English is required, good knowledge in Linux is required, exam in ModEx1+2 is required, first experience in own software projects is a plus |
| Supervisor: | Prof. Dr. Torben Ferber |
| Contact: | Dr. Matthias Schnepf |
| Last update: | 01.08.2024 |