Review

HPC-AutoResearch: Adapting Autonomous Research Systems for HPC through Split-Phase Execution and LLM-Based Code Generation

T. Kotama Shun-ichiro Hayashi Daichi Mukunoki Rio Yokota Satoshi Ohshima Tetsuya Hoshino Takahiro Katagiri

· 0 citations · 15 references

TL;DR

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.

Semantic Scholar

View source

Similar papers

Book Open access Sep 2026

Execution-Driven Auto-Tuning of Static Schedules for Resource-Efficient HPC

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

Preprint Sep 2026

Towards Efficient HPC Systems for Agents: Challenges and Opportunities

Coding agents have become real users of high-performance computing (HPC) systems, yet today's HPC abstractions, interfaces, and policies remain designed for human-driven workflows. In our measurement, users running coding agents are only 19.5% of the observed population, but account for 55.8% of job submissions, 29.1%...

Yun-Jia Zheng, Bintang Dwi Marthen, Zachary Pan et al. · 0 citations

Preprint Sep 2026

RASER: Resilient Agent Scheduling and Execution Runtime for HPC Clusters

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.

Sima Attar-Khorasani, Matthias Lieber, Siavash Ghiasvand · 1 citation

On the co-design of runtimes, systems and programming interfaces for HPC

(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...

David Álvarez Robert · 0 citations

Preprint Aug 2026

Accelerated Genetic Programming Hyper-Heuristics for Simulation-Based Scheduling via Agentic AI

Python is widely used in scientific research because it enables rapid development and provides rich ecosystems for data analysis, artificial intelligence (AI), and machine learning. However, customized research code can become prohibitively slow as experiments scale. This challenge is particularly acute in discrete-eve...

H. Li, A. Pletzer, Yuan Tian et al. · 0 citations

#artificial intelligence Review Sep 2026

Is manual software optimization a thing of the past?

Scientific software is increasingly required to process larger datasets while maintaining acceptable execution times. Software optimization traditionally requires substantial expertise in programming, algorithms, and numerical methods. Recent advances in large language models (LLMs) offer the possibility of automating...

Pavlin G. Poličar, Martin Špendl, Tomaž Hočevar · 0 citations