Finding Missed Code Size Optimizations in Compilers using LLMs

This is a Plain English Papers summary of a research paper called Finding Missed Code Size Optimizations in Compilers using LLMs. If you like these kinds of analysis, you should subscribe to the AImodels.fyi newsletter or follow me on Twitter.

Overview

Research explores using LLMs to find missed compiler optimizations
Focuses on code size reduction opportunities in C/C++ compilers
Tested on LLVM compiler with 1089 real-world programs
Found potential 3.9% additional code size reduction
Developed automated system for finding optimization opportunities
Validated findings through manual expert review

Plain English Explanation

Modern software programs need to be as small and efficient as possible. Code optimization is like packing for a trip - you want to fit everything you need in the smallest suitcase possible. Compilers are the tools that pack this code, but sometimes they miss opportunities to make programs smaller.

This research used AI language models to spot these missed opportunities. Think of it like having an expert looking over the compiler's shoulder, pointing out where code could be shortened without changing what it does.

The team built a system that examines compiled code and suggests improvements. They tested it on real programs and found ways to make code about 4% smaller on average. While this might seem small, for large programs it can mean significant savings in memory and storage.

Key Findings

The research revealed several important discoveries about compiler optimization:

Found 25 unique types of missed optimizations
Achieved 3.9% code size reduction across test programs
89% of LLM suggestions were valid optimization opportunities
Most missed optimizations were due to complex pattern matching
Manual review confirmed findings were genuine improvements

Technical Explanation

The researchers built a system using GPT-4 to analyze compiler output. Their approach involved:

Collecting a dataset of real-world C/C++ programs
Developing prompts to guide the LLM in identifying optimizations
Creating verification tools to validate suggestions
Implementing automated testing of proposed optimizations

The LLM-based system was particularly effective at identifying patterns that traditional compiler optimization passes missed. It could spot opportunities for function inlining, constant propagation, and dead code elimination that existing compiler heuristics overlooked.

Critical Analysis

Several limitations deserve consideration:

LLM suggestions require manual verification
System focuses only on code size, not performance
Testing limited to specific compiler version and architecture
Compiler optimization suggestions may not generalize across different platforms

The research could benefit from expanded testing across different compiler versions and hardware architectures. Additionally, the trade-off between code size and execution speed needs further investigation.

Conclusion

This research demonstrates the potential of AI to enhance compiler optimization. The findings suggest that modern compilers still have room for improvement in code size reduction. The successful use of LLMs in this domain opens new possibilities for automated compiler optimization tools and highlights the value of combining AI with traditional compilation techniques.

The practical implications extend beyond just saving storage space - smaller code can lead to faster load times, reduced memory usage, and improved cache utilization. These benefits are particularly relevant for embedded systems and mobile devices where resources are constrained.

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