HPC-AutoResearch is presented, a proof-of-concept system for the autonomous execution of compiled-code research workflows in HPC-like environments that divides this sub-pipeline into five phases—planning, environment setup, coding, compilation, and execution—localizing failures within each phase and enabling iterative repair via rootless Singularity container-based isolation.
Task-based HPC runtimes are typically tuned under a performance-first requirement with the assumption that the fastest configuration requires all available cores. This paper asks whether that assumption holds, and whether high performance can be achieved with fewer resources. We study this question using SWITCHES, a ta...
Hui-Min Zhang, Andreas Diavastos· Workshop Proceedings of the...· 0 citations
This work argues for co-design, that facilities should treat agents as first-class principals where agents become facility-aware tenants, and outlines the resulting challenges and opportunities in compute, storage, agent memory, and safety.
Yun-Jia Zheng, Bintang Dwi Marthen, Zachary Pan et al.· 0 citations
RASER is presented, a user-space framework that enables seamless execution of agentic workflows on production HPC clusters by extending Slurm's internal primitives and provides resilience against preemption and failures while maintaining minimal checkpoint/restore overhead.
(English) High-performance computing (HPC) platforms are evolving towards increasingly complex architectures: many-core CPUs with multi-level NUMA hierarchies, heterogeneity with multiple classes of accelerators and higher-capacity interconnects. The increasing complexity and variety of resources in these machines make...
This paper presents a systematic refactoring approach using Claude agentic AI on real-world project-scheduling workloads in a high-performance computing (HPC) environment, where the agent identifies bottlenecks, implements targeted optimizations, and evaluates their effects, while the researcher retains final control.
This work investigates whether LLM-based agents can autonomously achieve substantial performance improvements in scientific software, including mature implementations that have already been extensively optimized by human developers.
Pavlin G. Poličar, Martin Špendl, Tomaž Hočevar· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.