Auto-research with codex: How I achieved a 232x Faster Kernel (sankalp.bearblog.dev)

Article summary

The article describes the author's experience with auto-research using Codex, a language model, to optimize a kernel for QR decomposition, achieving a 232x speedup over the baseline solution. The author used Codex to implement and optimize the kernel, leveraging its ability to understand and generate code. The process involved iterative refinement and testing, with the author providing guidance and feedback to Codex. The result was a highly optimized kernel that outperformed the original implementation.

Main themes

• Auto-research
• Kernel optimization
• Language models
• Codex
• QR decomposition
• GPU acceleration

What commenters say

• Using language models like Codex can significantly accelerate the optimization of kernels and other code, allowing for rapid exploration of different approaches and techniques.
• The effectiveness of language models in optimization tasks depends on the quality of the feedback and guidance provided to them, as well as the specific problem being tackled.
• Some commenters believe that large labs and corporations may have an unfair advantage in using language models for optimization due to their greater resources and access to more powerful models.
• Others argue that the use of language models for optimization can lead to exponential growth and self-improvement, but this may be limited by hardware constraints and the complexity of the problems being addressed.
• There are concerns that some language models may be intentionally limited or 'poisoned' to prevent them from being used for certain types of research or optimization, in order to protect the interests of their developers.
• The use of language models for optimization raises questions about the role of human expertise and the potential for automation of certain tasks, with some arguing that humans will still be needed to provide guidance and oversight.
• The discussion highlights the importance of understanding the strengths and limitations of language models, as well as the need for careful evaluation and testing of their outputs.
• Some commenters note that language models can be particularly useful for optimizing tasks that are difficult or time-consuming for humans, such as GPU kernel optimization and SIMD instruction generation.