Summary of Towards Llm-based Optimization Compilers. Can Llms Learn How to Apply a Single Peephole Optimization? Reasoning Is All Llms Need!, by Xiangxin Fang and Lev Mukhanov
Towards LLM-based optimization compilers. Can LLMs learn how to apply a single peephole optimization? Reasoning is all LLMs need!
by Xiangxin Fang, Lev Mukhanov
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Programming Languages (cs.PL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study investigates the errors produced by a fine-tuned Large Language Model (LLM) as it attempts to learn and apply a simple peephole optimization for AArch64 assembly code. The LLM, Llama2, is compared with state-of-the-art OpenAI models, GPT-4o and GPT-o1, which implement advanced reasoning logic. The results show that OpenAI GPT-o1 outperforms the fine-tuned Llama2 and GPT-4o, largely due to its chain-of-thought reasoning mechanism. This study highlights the potential benefits of using LLMs with enhanced reasoning mechanisms for code generation and optimization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how large language models can be used for compiler optimizations. It compares a special type of model called Llama2 to other advanced models from OpenAI. The researchers find that one of these OpenAI models, GPT-o1, does better than the others because it has a special way of thinking that helps it make good decisions. |
Keywords
» Artificial intelligence » Gpt » Large language model » Optimization
