| Topic: | Automated process monitoring with workflow management software towards sustainable computing |
| Summary: |
In High Energy Physics many calculations are done. 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.
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| 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: | Input feature optimization for Graph Neural Network track reconstruction at Belle II |
| Summary: |
This thesis project focuses on optimizing input features for Graph Neural Network (GNN) track reconstruction at Belle II.
As a bachelor student, you will explore techniques to enhance the performance of the GNNs developed within our Machine Learning team by refining the selection and representation of input features using simulated and real data of the large volume drift chamber of the Belle II experiment.
Your tasks will involve analyzing the impact of different feature sets on track reconstruction efficiency and resolution.
By the project's conclusion, you will have contributed to advancing track reconstruction techniques at Belle II through improved input feature optimization.
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| You learn: | advanced track reconstruction techniques in particle physics |
| Prerequisites: | good proficiency in English is required, good knowledge in Python 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: | Lea Reuter |
| Last update: | 08.05.2024 |
| 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.
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| You learn: | advanced track reconstruction techniques in particle physics |
| 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: | 08.05.2024 |
| Topic: | Advanced data visualization for Machine Learning algorithms |
| Summary: |
Data visualization plays a crucial role in understanding and interpreting complex phenomena in particle physics experiments.
This thesis project focuses on leveraging advanced visualization techniques using Plotly, a powerful Python library for interactive plotting.
As a bachelor student undertaking this thesis, you will explore the capabilities of Plotly to create dynamic and interactive visualizations tailored to the needs of particle physics analyses.
Your tasks will include developing visualization tools to display detector geometries, particle trajectories, energy deposits, and other relevant data collected from the Belle II experiment with focus on Machine Learning reconstruction.
Throughout the project, you will collaborate closely with physicists and data analysts to identify key visualization requirements and design intuitive interfaces for data exploration.
By the conclusion of this project, you will have gained valuable experience in advanced data visualization techniques and made contributions to enhancing the visualization tools available for particle physics research.
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| You learn: | interactive data visualization techniques |
| Prerequisites: | good proficiency in English is required, good knowledge in Python 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, interest in graphical representation is required |
| Supervisor: | Prof. Dr. Torben Ferber |
| Contact: | Isabel Haide |
| Last update: | 08.05.2024 |
| 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.
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| 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: | 08.05.2024 |
| 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.
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| 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: | 08.05.2024 |