DeepGEMM is a highly optimized FP8 matrix multiplication (GEMM) library designed for efficiency and ease of integration. It prioritizes "clean" kernel code for better maintainability and portability while delivering competitive performance with other state-of-the-art FP8 GEMM implementations. The library features fine-grained scaling, allowing per-group or per-activation scaling factors, increasing accuracy for various models and hardware. It supports multiple hardware platforms, including NVIDIA GPUs and AMD GPUs via ROCm, and includes various utility functions to simplify integration into existing deep learning frameworks. The core design principles emphasize code simplicity and readability without sacrificing performance, making DeepGEMM a practical and powerful tool for accelerating deep learning computations with reduced precision arithmetic.
Ben Evans' post "The Deep Research Problem" argues that while AI can impressively synthesize existing information and accelerate certain research tasks, it fundamentally lacks the capacity for original scientific discovery. AI excels at pattern recognition and prediction within established frameworks, but genuine breakthroughs require formulating new questions, designing experiments to test novel hypotheses, and interpreting results with creative insight – abilities that remain uniquely human. Evans highlights the crucial role of tacit knowledge, intuition, and the iterative, often messy process of scientific exploration, which are difficult to codify and therefore beyond the current capabilities of AI. He concludes that AI will be a powerful tool to augment researchers, but it's unlikely to replace the core human element of scientific advancement.
HN commenters generally agree with Evans' premise that large language models (LLMs) struggle with deep research, especially in scientific domains. Several point out that LLMs excel at synthesizing existing knowledge and generating plausible-sounding text, but lack the ability to formulate novel hypotheses, design experiments, or critically evaluate evidence. Some suggest that LLMs could be valuable tools for researchers, helping with literature reviews or generating code, but won't replace the core skills of scientific inquiry. One commenter highlights the importance of "negative results" in research, something LLMs are ill-equipped to handle since they are trained on successful outcomes. Others discuss the limitations of current benchmarks for evaluating LLMs, arguing that they don't adequately capture the complexities of deep research. The potential for LLMs to accelerate "shallow" research and exacerbate the "publish or perish" problem is also raised. Finally, several commenters express skepticism about the feasibility of artificial general intelligence (AGI) altogether, suggesting that the limitations of LLMs in deep research reflect fundamental differences between human and machine cognition.
Researchers have trained a 1.5 billion parameter language model, DeepScaleR, using reinforcement learning from human feedback (RLHF). They demonstrate that scaling RLHF is crucial for performance improvements and that their model surpasses the performance of OpenAI's GPT-3 "O1-Preview" model on several benchmarks, including coding tasks. DeepScaleR achieves this through a novel scaling approach focusing on improved RLHF data quality and training stability, enabling efficient training of larger models with better alignment to human preferences. This work suggests that continued scaling of RLHF holds significant promise for further advancements in language model capabilities.
HN commenters discuss DeepScaleR's impressive performance but question the practicality of its massive scale and computational cost. Several point out the diminishing returns of scaling, suggesting that smaller, more efficient models might achieve similar results with further optimization. The lack of open-sourcing and limited details about the training process also draw criticism, hindering reproducibility and wider community evaluation. Some express skepticism about the real-world applicability of such a large model and call for more focus on robustness and safety in reinforcement learning research. Finally, there's a discussion around the environmental impact of training these large models and the need for more sustainable approaches.
Scaling WebSockets presents challenges beyond simply scaling HTTP. While horizontal scaling with multiple WebSocket servers seems straightforward, managing client connections and message routing introduces significant complexity. A central message broker becomes necessary to distribute messages across servers, introducing potential single points of failure and performance bottlenecks. Various approaches exist, including sticky sessions, which bind clients to specific servers, and distributing connections across servers with a router and shared state, each with tradeoffs. Ultimately, choosing the right architecture requires careful consideration of factors like message frequency, connection duration, and the need for features like message ordering and guaranteed delivery. The more sophisticated the features and higher the performance requirements, the more complex the solution becomes, involving techniques like sharding and clustering the message broker.
HN commenters discuss the challenges of scaling WebSockets, agreeing with the article's premise. Some highlight the added complexity compared to HTTP, particularly around state management and horizontal scaling. Specific issues mentioned include sticky sessions, message ordering, and dealing with backpressure. Several commenters share personal experiences and anecdotes about WebSocket scaling difficulties, reinforcing the points made in the article. A few suggest alternative approaches like server-sent events (SSE) for simpler use cases, while others recommend specific technologies or architectural patterns for robust WebSocket deployments. The difficulty in finding experienced WebSocket developers is also touched upon.
Kimi K1.5 is a reinforcement learning (RL) system designed for scalability and efficiency by leveraging Large Language Models (LLMs). It utilizes a novel approach called "LLM-augmented world modeling" where the LLM predicts future world states based on actions, improving sample efficiency and allowing the RL agent to learn with significantly fewer interactions with the actual environment. This prediction happens within a "latent space," a compressed representation of the environment learned by a variational autoencoder (VAE), which further enhances efficiency. The system's architecture integrates a policy LLM, a world model LLM, and the VAE, working together to generate and evaluate action sequences, enabling the agent to learn complex tasks in visually rich environments with fewer real-world samples than traditional RL methods.
Hacker News users discussed Kimi K1.5's approach to scaling reinforcement learning with LLMs, expressing both excitement and skepticism. Several commenters questioned the novelty, pointing out similarities to existing techniques like hindsight experience replay and prompting language models with desired outcomes. Others debated the practical applicability and scalability of the approach, particularly concerning the cost and complexity of training large language models. Some highlighted the potential benefits of using LLMs for reward modeling and generating diverse experiences, while others raised concerns about the limitations of relying on offline data and the potential for biases inherited from the language model. Overall, the discussion reflected a cautious optimism tempered by a pragmatic awareness of the challenges involved in integrating LLMs with reinforcement learning.
Summary of Comments ( 60 )
https://news.ycombinator.com/item?id=43179478
Hacker News users discussed DeepGEMM's claimed performance improvements, expressing skepticism due to the lack of comparisons with established libraries like cuBLAS and doubts about the practicality of FP8's reduced precision. Some questioned the overhead of scaling and the real-world applicability outside of specific AI workloads. Others highlighted the project's value in exploring FP8's potential and the clean codebase as a learning resource. The maintainability of hand-written assembly kernels was also debated, with some preferring compiler optimizations and others appreciating the control offered by assembly. Several commenters requested more comprehensive benchmarks and comparisons against existing solutions to validate DeepGEMM's claims.
The Hacker News post "DeepGEMM: clean and efficient FP8 GEMM kernels with fine-grained scaling" (https://news.ycombinator.com/item?id=43179478) has generated a moderate amount of discussion, with several commenters focusing on various aspects of FP8 and its implementation within the DeepGEMM library.
One commenter highlights the complexity of FP8, particularly the E4M3 and E5M2 formats, emphasizing the numerous permutations possible with offset, scale, and bias. They express that the lack of a singular standard creates significant challenges for hardware and software developers. This complexity makes cross-platform compatibility difficult and contributes to the fragmented landscape of FP8 implementations. They conclude by questioning whether FP8 will ever become truly ubiquitous due to this inherent complexity.
Another commenter delves into the performance implications of FP8, suggesting that the real bottleneck might not be the matrix multiplication itself but rather the overhead associated with format conversion and scaling. They speculate that if a model is trained and runs inference entirely in FP8, significant performance gains could be realized. However, the need to frequently switch between FP8 and other formats, like FP16 or FP32, could negate these potential benefits.
A different user focuses on the practical implications of reduced precision, especially in the context of scientific computing. They point out that FP8 might be suitable for machine learning applications where small errors are tolerable, but it's generally unsuitable for scientific computations where high precision is crucial. They express skepticism about the widespread applicability of FP8 beyond specific niches like deep learning.
Another comment emphasizes the importance of standardized benchmarks for comparing different FP8 implementations. They suggest that without a common benchmark suite, evaluating the true performance and efficiency of libraries like DeepGEMM becomes challenging. The lack of standardization makes it difficult to objectively assess the claimed advantages of one implementation over another.
A further comment draws attention to the broader trend of reduced precision computing, highlighting the emergence of various low-bit formats like INT4, INT8, and FP8. They express the need for careful consideration of the trade-offs between precision and performance when choosing a specific format. They also suggest that the choice of format depends heavily on the specific application and the acceptable level of error.
Finally, one comment shifts the focus towards hardware support for FP8, stating that wider adoption of FP8 depends significantly on robust hardware acceleration. While DeepGEMM might offer optimized kernels, the lack of widespread hardware support could limit its real-world impact. They suggest that future hardware advancements specifically tailored for FP8 will be crucial for its mainstream adoption.
In summary, the comments discuss the complexities and potential benefits of FP8, touching upon standardization issues, performance bottlenecks, application-specific suitability, the need for benchmarks, and the importance of hardware acceleration. The overall sentiment seems to be one of cautious optimism, acknowledging the potential of FP8 while also highlighting the significant challenges that need to be addressed for its wider adoption.