2nd GSI/FAIR AI Workshop

This talk gives a brief overview of the IT infrastructure supporting AI activities at GSI/FAIR, with a particular focus on emerging inference and LLM services. It will also present the GSI strategy for the professional use of AI-related tools, including considerations around data protection, trade secrets and information security, and outline how infrastructure and institutional guidance can...

We present a versatile production-grade LLM inference and document intelligence server designed for GSI/FAIR computing infrastructure.

The system employs a modern containerized architecture built on vLLM for GPU-accelerated inference, OpenWebUI for user interaction, and Milvus vector database for Retrieval-Augmented Generation (RAG) capabilities.
The deployment utilizes several specialized...

PowerSched presents a proof of concept for deep reinforcement learning-based, price-aware capacity control in high-performance computing clusters, dynamically adjusting powered resources in response to fluctuating electricity prices and workload demand. Evaluations using realistic price and workload traces demonstrate that measurable cost savings can be achieved while maintaining complete job...

The configuration parameters of the SLURM guide, the working of the scheduler. The efficiency of the cluster where SLURM is run can be optimised with the help of SLURM simulator adapted and tailored to the work load of the HPC cluster.

We investigate fast methods for evaluating particle hypotheses by combining signals from multiple detectors during the early readout stage of the data-acquisition (DAQ) chain. As a first use case, we consider straight particle trajectories reconstructed from straw-tube drift-time measurements and time-of-flight (TOF) wall responses, initially neglecting energy loss. A compact multilayer...

Artificial intelligence methods developed for one scientific domain often struggle to generalize to other fields that produce irregular and high dimensional detector data, creating a need for domain agnostic reconstruction approaches.
BRAID is a research consortium developing machine learning models particularly graph neural network and attention-based architectures, that can work across this...