DeepSeek claims a significant AI performance boost by bypassing CUDA, the typical programming interface for Nvidia GPUs, and instead coding directly in PTX, a lower-level assembly-like language. This approach, they argue, allows for greater hardware control and optimization, leading to substantial speed improvements in their inference engine, Coder, specifically for large language models. While promising increased efficiency and reduced costs, DeepSeek's approach requires more specialized expertise and hasn't yet been independently verified. They are making their Coder software development kit available for developers to test these claims.
The author's Chumby 8, a vintage internet appliance, consistently ran at 100% CPU usage due to a kernel bug affecting the way the CPU's clock frequency was handled. The original kernel expected a constant clock speed, but the Chumby's CPU dynamically scaled its frequency. This discrepancy caused the kernel's timekeeping functions to malfunction, leading to a busy loop that consumed all available CPU cycles. Upgrading to a newer kernel, compiled with the correct configuration for a variable clock speed, resolved the issue and brought CPU usage back to normal levels.
The Hacker News comments primarily focus on the surprising complexity and challenges involved in the author's quest to upgrade the kernel of a Chumby 8. Several commenters expressed admiration for the author's deep dive into the embedded system's inner workings, with some jokingly comparing it to a software archaeological expedition. There's also discussion about the prevalence of inefficient browser implementations on embedded devices, contributing to high CPU usage. Some suggest alternative approaches, like using a lightweight browser or a different operating system entirely. A few commenters shared their own experiences with similar embedded devices and the difficulties in optimizing their performance. The overall sentiment reflects appreciation for the author's detailed troubleshooting process and the interesting technical insights it provides.
Summary of Comments ( 1 )
https://news.ycombinator.com/item?id=42859909
Hacker News commenters are skeptical of DeepSeek's claims of a "breakthrough." Many suggest that using PTX directly isn't novel and question the performance benefits touted, pointing out potential downsides like portability issues and increased development complexity. Some argue that CUDA already optimizes and compiles to PTX, making DeepSeek's approach redundant. Others express concern about the lack of concrete benchmarks and the heavy reliance on marketing jargon in the original article. Several commenters with GPU programming experience highlight the difficulties and limited advantages of working with PTX directly. Overall, the consensus seems to be that while interesting, DeepSeek's approach needs more evidence to support its claims of superior performance.
The Hacker News post titled "DeepSeek's AI breakthrough bypasses industry-standard CUDA, uses PTX" generated a moderate amount of discussion, with several commenters expressing skepticism and raising important questions about the claims made in the Tom's Hardware article.
A recurring theme in the comments is the questioning of whether this truly constitutes a "breakthrough." Several users pointed out that PTX is not a new technology and is, in fact, an intermediate representation used by CUDA. They argued that bypassing CUDA and using PTX directly is unlikely to yield significant performance improvements, and might even lead to performance degradation due to the loss of CUDA's optimizations. One commenter likened it to claiming a "breakthrough" by writing assembly code instead of C, highlighting the fact that while possible, it's often less efficient and more complex.
Some users also questioned the benchmark results presented in the article, expressing concerns about their validity and whether they accurately reflect real-world performance gains. They called for more rigorous and transparent benchmarking methodologies to substantiate the claims. The lack of publicly available code or data for independent verification was also noted as a reason for skepticism.
Another point of discussion revolved around the potential advantages and disadvantages of using PTX directly. While some acknowledged the potential for finer-grained control and optimization, others highlighted the increased development complexity and the risk of introducing errors. The general consensus seemed to be that the benefits of using PTX directly would need to be substantial to outweigh the added complexity.
A few commenters also discussed the implications for the broader AI hardware landscape, with some suggesting that this approach could potentially open doors for more specialized hardware acceleration. However, this was not a dominant theme in the discussion.
Overall, the comments on Hacker News express a healthy dose of skepticism towards the claims made in the Tom's Hardware article. Many users highlighted the fact that PTX is not a new technology and questioned the actual performance benefits of bypassing CUDA. The lack of transparency and independent verification further fueled this skepticism. While the possibility of specialized hardware acceleration was briefly touched upon, the primary focus remained on the practicality and potential benefits of the approach described in the article.