Adapting Large Language Models to Efficiently Optimize Program Performance

Core Concepts

Large language models can be effectively adapted to automatically optimize program performance through techniques like retrieval-based few-shot prompting, performance-conditioned generation, and synthetic data augmentation.

Abstract

The paper introduces a novel benchmark called Performance Improving Edits (PIE) to enable the evaluation of large language models (LLMs) for program optimization. The PIE dataset consists of over 77,000 pairs of C++ programs where one program is a performance-improving edit of the other, along with extensive unit tests and execution time annotations obtained using the gem5 CPU simulator.
The authors evaluate a variety of prompting and fine-tuning strategies for adapting pre-trained LLMs like CODELLAMA and GPT-3.5 to optimize program performance:

Prompting approaches, including instruction-only, few-shot, and chain-of-thought prompting, show limited effectiveness without leveraging the PIE dataset.
Retrieval-based few-shot prompting, where relevant optimization examples are dynamically retrieved from the training set, significantly improves performance.
Fine-tuning strategies, such as using a smaller high-quality subset of the PIE dataset, performance-conditioned generation, and synthetic data augmentation via self-play, further boost optimization capabilities.
The best-performing model, a fine-tuned version of GPT-3.5 augmented with synthetic data, achieves an average speedup of 6.86x on the test set, outperforming the fastest human solutions (4.06x average speedup). The authors also provide a detailed analysis of the types of optimizations performed by the models, including algorithmic changes, input/output optimizations, and data structure modifications.

Stats

The fastest human solutions achieve an average speedup of 4.06x.
The fine-tuned GPT-3.5 model augmented with synthetic data achieves an average speedup of 6.86x.

Quotes

"With the waning of Moore's law, optimizing program performance has become a major focus of software research."
"To address this challenge, we measure program performance using the gem5 (Binkert et al., 2011) full system detailed microarchitectural simulator of state-of-the-art processors."
"Our best model, GPT-3.5 augmented with synthetic data obtained from self-play, achieves an average speedup of 6.86×, and optimizes 87.68% of the test set by at least 10%."

Key Insights Distilled From

Learning Performance-Improving Code Edits

by Alexander Sh... at arxiv.org 04-29-2024

https://arxiv.org/pdf/2302.07867.pdf

Deeper Inquiries

How can the PIE dataset be expanded to cover a wider range of programming languages and problem domains beyond competitive programming?

Expanding the PIE dataset to cover a wider range of programming languages and problem domains beyond competitive programming can be achieved through several strategies:

Diversifying Programming Languages: Include submissions and edits in languages like Python, Java, JavaScript, and others commonly used in software development. This can be done by sourcing submissions from platforms that support a variety of languages or by crowdsourcing submissions from a diverse pool of programmers.

Incorporating Real-World Projects: Instead of focusing solely on competitive programming tasks, incorporate edits and optimizations from real-world software projects. This can provide a more practical and varied set of optimizations that are relevant to industry scenarios.

Collaboration with Industry Partners: Partnering with industry organizations or open-source projects can provide access to a broader range of codebases and optimizations. This collaboration can help in collecting a more diverse set of performance-improving edits across different domains.

Crowdsourcing and Community Contributions: Engage with the programming community to contribute edits and optimizations from a wide range of programming languages and problem domains. Platforms like GitHub or programming forums can be utilized to gather a diverse set of edits.

Automated Data Collection: Utilize automated tools to scrape and collect code edits and optimizations from various sources on the internet. This can help in scaling up the dataset collection process and covering a wider range of languages and domains.

By implementing these strategies, the PIE dataset can be expanded to encompass a more comprehensive set of programming languages and problem domains, enabling a broader evaluation of performance optimization capabilities across different contexts.

How can the insights from this work on program optimization be applied to other areas of software engineering, such as automated bug fixing or code generation?

The insights from the work on program optimization can be leveraged in other areas of software engineering, such as automated bug fixing and code generation, in the following ways:

Automated Bug Fixing:

Performance-Driven Bug Fixing: Similar to performance optimization, models can be trained to identify and fix code segments that lead to performance issues or bugs.
Fine-Tuning for Bug Detection: Fine-tuning pre-trained models on datasets of buggy code can enable them to detect and suggest fixes for common bugs.

Code Generation:

Optimized Code Generation: Models can be trained to generate optimized code snippets or functions based on performance metrics, similar to the performance-conditioned generation approach in the paper.
Domain-Specific Code Generation: Tailoring models to generate code specific to certain domains or tasks can improve efficiency and accuracy in code generation tasks.

Transfer Learning: Insights from adapting large language models for program optimization can be applied through transfer learning to enhance models for bug fixing and code generation tasks. By transferring knowledge and strategies learned from performance optimization, models can improve their capabilities in these areas.

Data Augmentation Techniques: Synthetic data generation techniques used for performance optimization can be adapted for bug fixing and code generation tasks to enhance model training and improve generalization.

By applying the learnings and methodologies from program optimization to automated bug fixing and code generation, software engineering tasks can benefit from improved efficiency, accuracy, and performance in identifying and resolving code issues.

What other techniques, beyond the ones explored in this paper, could be used to further improve the performance optimization capabilities of large language models?

To further enhance the performance optimization capabilities of large language models beyond the techniques explored in the paper, the following additional strategies can be considered:

Multi-Task Learning: Incorporating multiple related tasks, such as code optimization, bug fixing, and code generation, into a unified learning framework can help models learn more robust representations and improve overall performance.

Ensemble Learning: Utilizing ensemble methods by combining predictions from multiple models can enhance the model's ability to generalize and make more accurate performance optimizations.

Adversarial Training: Introducing adversarial training techniques can help models learn to generate optimized code by exposing them to adversarial examples that challenge their optimization capabilities.

Interactive Learning: Implementing interactive learning approaches where models receive feedback from users or domain experts on generated optimizations can improve the quality and relevance of the generated code edits.

Domain-Specific Fine-Tuning: Fine-tuning models on domain-specific datasets or tasks can tailor the model's optimization capabilities to specific application areas, leading to more effective performance improvements.

Dynamic Prompting Strategies: Developing dynamic prompting strategies that adaptively adjust prompts based on model performance and feedback can enhance the model's ability to generate optimized code edits.

By exploring these additional techniques in conjunction with the methods discussed in the paper, the performance optimization capabilities of large language models can be further refined and optimized for a wide range of software engineering tasks.