arXiv subjects

Matthias Lieber

Publications and source records attributed to Matthias Lieber.

2 recordsLinked to original sources

arXiv2026

JuPyLive: Seamless Migration of Jupyter Notebook Resources from Laptop to HPC

This work introduces JuPyLive, a migration mechanism that enables seamless transition of Jupyter notebooks between local resources of user's workstation and remote resources of high-performance computing~(HPC) environments, while preserving the user experience. JuPyLive eliminates the underlying complexities of migration process, enabling users to freely choose among available local and remote resources, directly within the familiar Jupyter notebook environment via a single click. JuPyLive leverages ElasticNotebook to manage in-memory state migration, it automates resource allocation on HPC cluster and orchestrates required remote communication channels between the source and destination to enable a bidirectional migration. Furthermore, HPC status monitor of JuPyLive provides a live overview of available remote resources, allowing users to make informed decisions on choosing the relevant resources before initiating a migration process. The proposed fully automatic mechanism requires no code changes or configurations by the end user, nor does it demand users to learn a new syntax, instead the migration process can be intuitively initiated and monitored using visual elements from within the Jupyter notebook. By bridging the gap between local workspace and remote resources, JuPyLive offers a seamless experience for scaling local resource-intensive workflows with minimal user intervention, thus further democratizing the usage of HPC clusters among the interdisciplinary researchers.

cs.DC↗

arXiv2026

RASER: Resilient Agent Scheduling and Execution Runtime for HPC Clusters

The emergence of modern agents powered by large language models has created a demand for executing long-horizon, autonomous workflows in various domains that require significant computational resources. While High Performance Computing clusters provide the ideal infrastructure for these computation-intensive workloads, traditional HPC job schedulers such as Slurm are not designed for dynamic, agentic workflows characterized by unpredictable task durations, external API calls, and fault tolerance requirements of modern agents. This work presents RASER, a user-space framework that enables seamless execution of agentic workflows on production HPC clusters by extending Slurm's internal primitives. RASER introduces agentic job arrays with work stealing via shared filesystem queues, user-space checkpointing through application-level state serialization combined with Slurm requeue, and Apptainer container-based isolation without requiring any image modifications. Evaluations demonstrate that RASER reduces makespan by nearly 39% compared to static partitioning while achieving near-full CPU utilization. RASER provides resilience against preemption and failures while maintaining minimal checkpoint/restore overhead. It requires no kernel privileges or external database infrastructure, making it an accessible solution for deploying agentic workflows on existing HPC infrastructure.

cs.PF↗