Stories with Tag Kernels

• Surprisingly fast AI-generated kernels we didn't mean to publish yet

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  Posted: 2025-05-30 20:03:12

  Verbose tl;dr

  Researchers inadvertently discovered that large language models (LLMs) can generate surprisingly efficient low-level code, specifically computational kernels, often outperforming manually optimized code and even specialized compilers. They prompted LLMs like Codex with natural language descriptions of algorithms, along with performance constraints, and the models produced C++ code with competitive or even superior speed compared to highly optimized libraries. This unexpected capability opens up the possibility of using LLMs for tasks traditionally requiring specialized programming skills, potentially democratizing access to performance optimization and accelerating scientific computing.

  Researchers at the Center for Research on Foundation Models (CRFM) at Stanford University have inadvertently released a set of remarkably efficient computational kernels generated by artificial intelligence. These kernels, designed to perform fundamental mathematical operations at the heart of many computational tasks, exhibit surprising speed and efficiency, outperforming hand-optimized kernels in certain specific scenarios. The accidental publication stemmed from a routine automated synchronization process of their internal code repository.

  The team, while acknowledging the premature nature of the release, elaborated on the significance of this discovery. They had been exploring the potential of large language models (LLMs) to not only write code, but to optimize its performance at a low level. Traditionally, crafting highly optimized kernels requires specialized expertise and painstaking manual tuning, often involving intricate assembly language and a deep understanding of hardware architecture. The results achieved by their AI-generated kernels suggest that LLMs might hold the key to automating this complex and time-consuming process.

  The process employed by the researchers involved prompting the LLM with a high-level description of the desired kernel's functionality. The LLM subsequently generated not only the kernel code itself, but also an accompanying test harness to verify its correctness. Notably, the generated kernels incorporate advanced optimization techniques such as vectorization and loop unrolling, demonstrating the LLM's capacity to grasp and apply these concepts.

  The team highlighted instances where the AI-generated kernels exceeded the performance of highly optimized libraries like BLAS (Basic Linear Algebra Subprograms), a widely used set of routines for linear algebra operations. Specifically, they cited examples of matrix multiplication and convolution kernels where their AI-generated versions demonstrated notable speedups. However, they emphasized that these results are preliminary and the generalizability of this approach remains to be investigated further.

  While unexpected, this premature release provides a tantalizing glimpse into the potential of AI-driven code optimization and its potential to revolutionize performance-critical computing tasks. The researchers intend to conduct more rigorous benchmarking and analysis before formally publishing their findings. They also plan to explore the applicability of this technique to a wider range of kernels and hardware platforms, aiming to understand the limitations and potential broader implications of using LLMs for low-level code optimization.

  • AI
  • artificial intelligence
  • Kernels
  • performance
  • optimization
  • Code Generation
  • machine learning
  • deep learning
  • Premature Publication
  • Research
  • Stanford
  • CRFM

  Summary of Comments ( 146 )
  https://news.ycombinator.com/item?id=44139454

  Verbose tl;dr

  Hacker News users discussed the surprising speed of the accidentally published AI-generated kernels, with many expressing skepticism and seeking clarification on the benchmarking methodology. Several commenters questioned the comparison to other libraries like cuDNN and questioned if the kernels were truly optimized or simply benefited from specialization. Others pointed out the lack of source code and reproducible benchmarks, hindering proper evaluation and validation of the claims. The focus of the discussion revolved around the need for more transparency and rigorous testing to confirm the surprising performance results. Some also discussed the implications of AI-generated code for the future of software development, with some expressing excitement and others caution.

  The Hacker News post titled "Surprisingly fast AI-generated kernels we didn't mean to publish yet" (linking to a Stanford CRFM article about AI-generated CUDA kernels) generated a modest number of comments, mostly focused on the technical details and implications of the research.

  Several commenters expressed excitement and interest in the potential of AI-generated kernels, especially given the reported performance improvements. Some questioned the reproducibility of the results and the generalizability of the approach to different hardware or problem domains. The lack of open-source code at the time of the post was a recurring point of discussion, limiting the ability of the community to fully evaluate the claims.

  One compelling comment thread explored the possibility that the AI might be exploiting undocumented hardware features or quirks, leading to performance gains that wouldn't be achievable with traditional hand-tuned kernels. This led to a discussion about the potential for "black box" optimization and the challenges of understanding and verifying the behavior of AI-generated code.

  Another interesting comment chain focused on the methodology used to compare the AI-generated kernels against existing solutions. Commenters debated the fairness of the comparisons and the importance of comparing against highly optimized, state-of-the-art implementations. Some suggested that the AI might simply be rediscovering known optimization techniques, rather than inventing truly novel approaches.

  There was some skepticism about the long-term implications of the work. While acknowledging the impressive initial results, some commenters questioned whether the approach would scale to more complex kernels or adapt to evolving hardware architectures.

  Overall, the comments reflect a cautious optimism about the potential of AI-generated kernels. While the results are intriguing, there's a clear desire for more information, open-source code, and further research to validate the claims and explore the limitations of the approach. The discussion highlights the challenges and opportunities presented by applying AI to low-level performance optimization tasks.

• Convolutions, Polynomials and Flipped Kernels

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  Posted: 2025-05-21 04:35:21

  Verbose tl;dr

  This post explains the connection between convolutions and polynomial multiplication. It demonstrates how discrete convolution can be interpreted as multiplying two polynomials where one polynomial's coefficients represent the input signal and the other represents the convolution kernel (filter). The seemingly strange "flipping" of the kernel in the typical convolution operation arises naturally from the process of aligning terms with the same exponent during polynomial multiplication. By viewing convolution through this polynomial lens, the author illuminates the underlying mathematical structure and provides a clearer intuition for why the kernel is flipped. This perspective also bridges the gap between the discrete and continuous forms of convolution, highlighting their fundamental similarity.

  Eli Bendersky's blog post, "Convolutions, Polynomials, and Flipped Kernels," meticulously explores the deep connection between the mathematical concept of convolution and its application in signal processing, particularly within the realm of convolutional neural networks (CNNs). The post begins by establishing a clear definition of discrete convolution, emphasizing the characteristic "flipping" of the kernel (a small matrix or vector of weights) before sliding it across the input signal (another vector or matrix representing data like an image) and performing element-wise multiplication and summation at each position. This operation produces the convolved output signal.

  Bendersky then delves into the fascinating relationship between convolution and polynomial multiplication. He explains that if the input signal and the kernel are viewed as coefficients of polynomials, then the convolution operation is equivalent to multiplying those polynomials. This equivalence is demonstrated through a detailed example, carefully illustrating how the coefficients of the resulting product polynomial correspond to the output of the convolution. This perspective offers a powerful and intuitive way to understand why the kernel is flipped during convolution. When multiplying polynomials, terms are combined based on matching exponents, which effectively mirrors the sliding and aligning process of the flipped kernel in the convolution operation.

  The blog post proceeds to connect this polynomial interpretation to the concept of the z-transform. The z-transform represents a discrete-time signal as a polynomial in the complex variable 'z'. By taking the z-transforms of the input signal and the kernel, their convolution in the time domain translates to a simple multiplication of their respective z-transforms in the z-domain. This transformation simplifies the analysis and manipulation of signals and systems.

  Further elaborating on the practical implications, Bendersky discusses the significance of flipped kernels in CNNs. He explains how this flipping operation, inherent in the mathematical definition of convolution, contributes to the network's ability to detect patterns and features within the input data. The flipping ensures that the kernel effectively scans the input for specific arrangements of values, enabling the network to learn meaningful representations.

  Finally, the post acknowledges a common practice in some CNN implementations where the kernel is not explicitly flipped. This is explained by noting that since the kernel weights are learned during the training process, the network can effectively learn a "pre-flipped" version of the kernel, thus implicitly incorporating the flip operation. This optimization doesn't change the fundamental mathematical principle but rather represents a computational shortcut. The post concludes by reiterating the important link between convolution, polynomial multiplication, and the concept of flipped kernels, providing a comprehensive mathematical understanding of this fundamental operation in signal processing and machine learning.

  • convolution
  • Polynomials
  • Kernels
  • Signal Processing
  • image processing
  • computer vision
  • mathematics
  • Digital Signal Processing
  • filtering
  • Kernel Flipping
  • Convolution Theorem

  Summary of Comments ( 23 )
  https://news.ycombinator.com/item?id=44048306

  Verbose tl;dr

  Commenters on Hacker News largely praised the article for its clear explanation of the relationship between convolutions and polynomial multiplication. Several highlighted the insightful connection made between flipping the kernel in convolution and the order of coefficients in polynomial multiplication. One commenter appreciated the focus on discrete convolution, noting its importance in computer science applications. Another pointed out the practical implications for understanding signal processing, while others discussed extensions of these concepts to areas like generating functions. A few commenters also shared resources for further exploration of related topics like fast convolution algorithms and the Fourier transform.

  The Hacker News post "Convolutions, Polynomials and Flipped Kernels" has generated a modest discussion with several insightful comments.

  One commenter highlights the significance of the article's explanation of the "flip" in convolutions, stating that it finally helped them understand the concept after years of confusion. They appreciate the clear connection made between polynomial multiplication and convolutions. This comment resonates with other users who express similar past struggles and praise the article for its clarity.

  Another commenter delves into the practical application of flipped kernels, particularly in computer graphics. They discuss how this concept relates to blurring and sharpening filters. This comment adds a practical dimension to the theoretical discussion.

  One user mentions the importance of convolutions in various fields, including image processing, signal processing, and probability theory, further emphasizing the wide applicability of the concepts discussed in the article.

  A further comment expands on the mathematical background, pointing out that the commutativity of convolution allows for flipping the kernel, which can be computationally advantageous in certain situations. This adds a deeper mathematical understanding of the "flip" operation.

  The discussion also touches on the use of FFT (Fast Fourier Transform) for efficient computation of convolutions, highlighting its practical significance in signal processing applications.

  Finally, a comment mentions the article's connection to generating functions, expanding the mathematical scope of the discussion and linking it to other areas of mathematics.

  Overall, the comments on the Hacker News post provide valuable perspectives on the article, ranging from personal experiences of understanding the "flip" to practical applications in various fields and deeper mathematical insights. They collectively enrich the understanding of convolutions, polynomials, and flipped kernels.