Can Large Language Models Recover Semantic Optimization Opportunities That Compilers Miss?
Can AI spot ways to speed up code that traditional compilers miss?
Large language models can recover hidden semantic information from C/C++ code that compilers typically overlook, enabling performance optimizations the compilers would otherwise miss. In tests on 120 real and synthetic cases, the best-performing model generated correct optimization suggestions 94.8% of the time and delivered measurable speed improvements in 83.3% of cases.
Compilers today are limited by what they can formally prove about code structure and behavior. If LLMs can reliably suggest valid optimizations that compilers can't find — and if those suggestions are verified before use — they could become a practical tool for making software faster without requiring programmers to manually rewrite their code. This is particularly valuable for performance-critical applications where even modest speed gains matter.