arXiv:2501.18916v2 Announce Type: replace Abstract: Recent work has demonstrated the potential of large language models (LLMs) for program optimization, a key challenge in programming languages. We propose a blackbox adaptation method called Retrieval Augmented Search (RAS) that performs beam search over candidate optimizations; at each step, it retrieves in-context examples from a given training dataset of slow-fast program pairs to guide the LLM. Critically, we find that performing contextual retrieval based on an LLM-generated natural language description significantly outperforms retrieval
The rapid advancement of large language models and the increasing demand for efficient software development and AI model optimization are converging, making program optimization a critical area for innovation.
This development indicates a significant step towards more autonomous and efficient software development, potentially accelerating AI capabilities and reducing the need for manual optimization in complex systems.
LLMs can now perform more sophisticated and context-aware program optimizations through retrieval-augmented search, moving beyond basic code generation to actual performance enhancement.
- · AI software developers
- · Cloud computing providers
- · High-performance computing (HPC) sector
- · Manual program optimizers
- · Less agile software development firms
This research directly improves the efficiency and performance of programs developed with or optimized by LLMs.
Better program optimization could lead to a significant acceleration in AI model training and deployment, making advanced AI capabilities more accessible.
The automation of optimization could reduce the economic friction of developing complex software, potentially broadening the applications and accessibility of advanced computing.
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Read at arXiv cs.LG