# Evaluating Large Language Models for Workload Mapping and Scheduling in Heterogeneous HPC Systems

arXiv: https://arxiv.org/abs/2511.11612 · HTML: https://mlsanity.com/paper/2511.11612/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Test-time compute and efficient thinking, one of 17 directions in LLM reasoning.
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 4 Nov 2025.

## Placed in

- LLM reasoning / [Test-time compute and efficient thinking](https://mlsanity.com/lr/test-time-compute-and-efficient-thinking/)

## Builds on (most cited first, 4 of 4)

- [Do Large Language Models Understand Performance Optimization?](https://mlsanity.com/paper/2503.13772/) (2025)
- [Evaluating the Efficacy of LLM-Based Reasoning for Multiobjective HPC Job Scheduling](https://mlsanity.com/paper/2506.02025/) (2025)
- [GrapheonRL: A Graph Neural Network and Reinforcement Learning Framework for Constraint and Data-Aware Workflow Mapping and Scheduling in Heterogeneous HPC Systems](https://mlsanity.com/paper/2506.00260/) (2025)
- [Workflow-Driven Modeling for the Compute Continuum: An Optimization Approach to Automated System and Workload Scheduling](https://mlsanity.com/paper/2505.12184/) (2025)

## Abstract (arXiv)

Large language models (LLMs) are increasingly explored for their reasoning capabilities, yet their ability to perform structured, constraint-based optimization from natural language remains insufficiently understood. This study evaluates twenty-one publicly available LLMs on a representative heterogeneous high-performance computing (HPC) workload mapping and scheduling problem. Each model received the same textual description of system nodes, task requirements, and scheduling constraints, and was required to assign tasks to nodes, compute the total makespan, and explain its reasoning. A manually derived analytical optimum of nine hours and twenty seconds served as the ground truth reference. Three models exactly reproduced the analytical optimum while satisfying all constraints, twelve achieved near-optimal results within two minutes of the reference, and six produced suboptimal schedules with arithmetic or dependency errors. All models generated feasible task-to-node mappings, though only about half maintained strict constraint adherence. Nineteen models produced partially executable verification code, and eighteen provided coherent step-by-step reasoning, demonstrating strong interpretability even when logical errors occurred. Overall, the results define the current capability boundary of LLM reasoning in combinatorial optimization: leading models can reconstruct optimal schedules directly from natural language, but most still struggle with precise timing, data transfer arithmetic, and dependency enforcement. These findings highlight the potential of LLMs as explainable co-pilots for optimization and decision-support tasks rather than autonomous solvers.