arXiv:2512.25065v2 Announce Type: replace-cross Abstract: Systems resource management tasks rely primarily on hand-designed heuristics. However, growing hardware heterogeneity and workload diversity require heuristics specialized to particular deployment instances, making manual design expensive and difficult to scale. In this paper, we explore how to synthesize systems heuristics using LLMs. The main challenge is ensuring that generated heuristics execute safely, integrate correctly with the surrounding system, and still achieve strong performance. We propose Vulcan, a framework that identifi
The increasing complexity of hardware and diverse workloads necessitates automated, specialized system management beyond manual heuristic design, driving innovation in LLM applications for systems optimization.
This development indicates a significant step towards autonomous system resource management, potentially leading to more efficient and adaptable computing infrastructure for various applications, easing the burden of hardware heterogeneity.
Traditional reliance on hand-designed heuristics for resource management is beginning to be augmented by verifiable, LLM-generated solutions, shifting how systems are optimized and controlled.
- · AI/ML developers
- · Cloud infrastructure providers
- · Hardware manufacturers
- · High-performance computing (HPC) sectors
- · Manual systems optimizers
- · Legacy system administrators
LLMs can now generate and verify system heuristics, automating a previously manual and complex task in resource management.
This automation could lead to more efficient and dynamic allocation of computing resources across vast and heterogeneous hardware landscapes.
The ability to rapidly synthesize and verify specialized system heuristics via AI might accelerate the development and deployment of complex AI models and applications, reducing operational overhead.
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Read at arXiv cs.AI