Summary Large Language Models for Compiler Optimization arxiv.org
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The document explores the application of Large Language Models (LLMs) in compiler optimization, specifically in compiler pass ordering, and introduces a 7B-parameter transformer model trained to optimize LLVM assembly for code size.
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Key Points
- Large Language Models (LLMs) are being explored for code optimization in compilers.
- LLMs can predict instruction counts and optimized code during training, improving optimization performance.
- LLM tokenizer achieves an average of 2.02 characters per token when encoding LLVM-IR.
- LLMs demonstrate a sophisticated understanding of LLVM-IR semantics and can perform optimizations without access to the compiler implementation.
- Challenges include generating correctly-optimized code without producing the necessary pass list and potential errors in program semantics.
- Research papers and projects related to LLMs and compiler optimization have been discussed, covering topics such as scaling transformers and extending context window.
Summaries
32 word summary
This document discusses the use of Large Language Models (LLMs) for compiler optimization, focusing on compiler pass ordering. A 7B-parameter transformer model is presented, trained to optimize LLVM assembly for code size.
40 word summary
This document explores the application of Large Language Models (LLMs) for compiler optimization, specifically targeting compiler pass ordering. The authors present a 7B-parameter transformer model trained from scratch to optimize LLVM assembly for code size. The LLM tokenizer achieves an
447 word summary
We explore the application of Large Language Models (LLMs) to code optimization. We present a 7B-parameter transformer model trained from scratch to optimize LLVM assembly for code size. The model predicts instruction counts and optimized code during training, improving optimization performance
The excerpt discusses the use of large language models (LLMs) for compiler optimization, specifically targeting compiler pass ordering. Compiler pass ordering involves selecting the optimizing transformations that will produce the best result for a given input code. Pass ordering has a significant impact on
Large Language Models (LLMs) are used for compiler optimization and achieve parity with -Oz, a fixed-size context window. The LLM tokenizer achieves an average of 2.02 characters per token when encoding LLVM-IR. The training data
The study evaluates the performance of large language models (LLMs) for compiler optimization. It focuses on the prediction of instruction counts and the quality of generated code. The prediction of instruction counts for unoptimized code is accurate, while the prediction of output instruction
This document explores the use of large language models for compiler optimization. The authors present an example IR function where the model suggests a better pass list than the autotuner, despite never having seen the code before. They compare their approach to three baselines
Table IV presents the results of the techniques evaluated in Figure 5, which shows that larger programs have more opportunities for improvement. AutoPhase and Coreset-NVP achieve overall improvements over -Oz, but less than the LLM with or without the
One challenge in evaluating semantic equivalency between intermediate representations (IRs) is that it is often unclear if their behavior is the same. Therefore, execution-based equivalence checks cannot be used. An example is shown where model-generated code has incorrect program semantics.
The document discusses the use of large language models (LLMs) for compiler optimization. It highlights two types of errors that can occur when using LLMs for optimization: generating correctly-optimized code but failing to produce the necessary pass list, and making
Large Language Models (LLMs) can be used for code optimization in compilers. The LLMs demonstrate a sophisticated understanding of LLVM-IR semantics and can perform optimizations without access to the compiler implementation. However, the computational overhead and resource requirements of L
This summary provides a concise version of the text excerpt, highlighting key points and preserving important details. The information is organized into separate paragraphs to distinguish distinct ideas for readability, while retaining the original order in which ideas were presented.
The excerpt includes references to various
This summary provides a list of research papers and projects related to large language models and compiler optimization. The papers discussed various topics such as scaling transformers, extending context window, length-extrapolatable transformers, chain-of-thought prompting, program-aided