arXiv:2606.17683v1 Announce Type: new Abstract: While large language models (LLMs) have greatly advanced the functional correctness of automated code translation systems, the runtime efficiency of translated programs has received comparatively little attention. With the waning of Moore's law, runtime efficiency has become increasingly important for program quality, alongside functional correctness. Our preliminary study reveals that LLM-translated programs often run slower than human-written ones, and this issue cannot be remedied through prompt engineering alone. Therefore, our work proposes
The increasing focus on LLMs for code generation, coupled with the end of Moore's Law, makes runtime efficiency a critical, previously underexplored, constraint for AI-assisted software development.
This highlights a significant limitation in current LLM capabilities for code generation, suggesting that while functional correctness is improving, practical deployment requires addressing efficiency for real-world applications.
The focus for LLM-based code translation is shifting beyond just functional accuracy to include critical performance metrics, which will drive new research and development in optimization techniques.
- · Compilers and runtime optimization experts
- · Companies with expertise in high-performance computing
- · Developers of specialized LLMs for code efficiency
- · LLM providers focused solely on functional correctness
- · Generic LLM code generation approaches
- · Developers neglecting performance metrics
Demand will increase for robust performance benchmarking and optimization tools specifically for LLM-generated code.
This issue may lead to hybrid approaches, combining LLMs for initial generation with traditional compilers or human experts for optimization, maintaining competitive performance.
The necessity for efficient code could eventually influence hardware design, with specialized architectures becoming more optimized for the computational patterns of LLM-generated programs.
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