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.
In a potentially disruptive move for the artificial intelligence hardware landscape, a company named DeepSeek claims to have achieved significant performance enhancements in AI inference by circumventing the ubiquitous CUDA programming model typically employed for GPU acceleration. Instead of relying on CUDA, DeepSeek's approach involves programming directly in Parallel Thread Execution (PTX), a low-level, assembly-like language that serves as an intermediate representation for NVIDIA GPUs. This strategy, while more complex and demanding from a development perspective, grants DeepSeek finer-grained control over the underlying hardware, allowing for optimizations not readily achievable within the higher-level abstractions of CUDA.
DeepSeek asserts that this direct engagement with PTX enables them to bypass CUDA's inherent overhead, resulting in notable improvements in both latency and throughput for inference tasks. Their initial benchmarks, focused on transformer models like BERT and Stable Diffusion, purportedly demonstrate up to a fivefold increase in throughput compared to CUDA-based implementations. This performance boost stems from meticulous hand-optimization of PTX code, tailored specifically for the targeted hardware and model architecture.
The implications of DeepSeek's method are far-reaching. While CUDA has long been the industry standard for GPU programming in deep learning, its abstraction layers, while simplifying development, can introduce performance bottlenecks. By working directly at the PTX level, DeepSeek exposes a potential path towards squeezing greater efficiency from existing hardware. However, this approach carries its own set of challenges. PTX programming is significantly more intricate and labor-intensive than CUDA, requiring specialized expertise and potentially limiting portability across different GPU architectures. Furthermore, maintaining and updating PTX code can be a complex undertaking.
Despite these complexities, DeepSeek's preliminary results suggest that the performance gains might outweigh the developmental overhead, particularly for inference workloads where latency and throughput are critical. Their focus on optimizing transformer models, a dominant force in modern AI, further underscores the potential impact of this technology. If DeepSeek’s claims are substantiated by independent testing and can be scaled to broader applications, this PTX-based approach could represent a significant shift in how AI inference is accelerated, potentially challenging CUDA’s long-standing dominance. However, the long-term viability of this method will depend on DeepSeek's ability to navigate the challenges of PTX development and demonstrate sustained performance advantages across diverse AI workloads. Further investigation and independent verification will be crucial in determining the true significance of this purported breakthrough.
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.