The Fly.io blog post "We Were Wrong About GPUs" admits their initial prediction that smaller, cheaper GPUs would dominate the serverless GPU market was incorrect. Demand has overwhelmingly shifted towards larger, more powerful GPUs, driven by increasingly complex AI workloads like large language models and generative AI. Customers prioritize performance and fast iteration over cost savings, willing to pay a premium for the ability to train and run these models efficiently. This has led Fly.io to adjust their strategy, focusing on providing access to higher-end GPUs and optimizing their platform for these demanding use cases.
This blog post details a method for realistically simulating shallow water flow over terrain. The author utilizes a heightmap to represent the terrain and employs a simplified shallow water equations model to govern water movement. This model calculates water height and velocity, accounting for factors like terrain slope and gravity. The simulation iteratively updates the water's state using numerical integration, allowing for dynamic changes in water distribution and flow patterns based on the underlying terrain. Visualization is achieved through a simple rendering technique that adjusts terrain color based on water depth, creating a visually convincing representation of shallow water flowing over varied terrain.
Commenters on Hacker News largely praised the clarity and educational value of the blog post on simulating water over terrain. Several appreciated the author's focus on intuitive explanation and avoidance of overly complex mathematics, making the topic accessible to a wider audience. Some pointed out the limitations of the shallow water equations used, particularly regarding their inability to model breaking waves, while others suggested alternative approaches or resources for further exploration, such as smoothed-particle hydrodynamics (SPH) and the book "Fluid Simulation for Computer Graphics." A few commenters also shared their own experiences and projects related to fluid simulation. Overall, the discussion was positive and focused on the technical aspects of the simulation.
Summary of Comments ( 421 )
https://news.ycombinator.com/item?id=43053844
HN commenters largely agreed with the author's premise that the difficulty of utilizing GPUs effectively often outweighs their potential benefits for many applications. Several shared personal experiences echoing the article's points about complex tooling, debugging challenges, and ultimately reverting to CPU-based solutions for simplicity and cost-effectiveness. Some pointed out that specific niches, like machine learning and scientific computing, heavily benefit from GPUs, while others highlighted the potential of simpler GPU programming models like CUDA and WebGPU to improve accessibility. A few commenters offered alternative perspectives, suggesting that managed services or serverless GPU offerings could mitigate some of the complexity issues raised. Others noted the importance of right-sizing GPU instances and warned against prematurely optimizing for GPUs. Finally, there was some discussion around the rising popularity of ARM-based processors and their potential to offer a competitive alternative for certain workloads.
The Hacker News post "We were wrong about GPUs" (linking to a fly.io blog post) generated a moderate amount of discussion, with several commenters offering interesting perspectives on the original article's claims.
A recurring theme is the nuance of GPU suitability for different tasks. Several comments challenge the blanket statement of being "wrong" about GPUs, highlighting their continued dominance in specific areas like machine learning training and scientific computing. One commenter pointed out that GPUs excel when data parallelism is high and control flow is relatively simple, which is often the case in these domains. Another echoes this, stating that GPUs are still the best choice for highly parallelizable tasks where the overhead of transferring data to the GPU is outweighed by the speed gains.
Some commenters discuss the complexities of utilizing GPUs effectively. One individual mentions the challenges of managing GPU memory and the difficulties in programming for them, contrasting this with the relative ease of using CPUs for more general-purpose tasks. This reinforces the idea that GPUs are not a universal solution and require careful consideration of the specific workload.
Another thread of discussion revolves around the rising prominence of alternative hardware, specifically mentioning TPUs and FPGAs. One commenter suggests that the article might be better titled "GPUs aren't the only future" acknowledging their ongoing relevance while highlighting the potential of other specialized hardware for specific tasks. Another points out that while GPUs are good at what they do, certain workloads, like database queries, might benefit more from specialized hardware or even optimized CPU implementations.
Several commenters provide anecdotal experiences. One shares their experience of struggling with GPUs for a specific image processing task, ultimately finding a CPU-based solution to be more efficient. This further emphasizes the importance of evaluating hardware choices based on individual project needs.
Finally, some comments focus on the cost aspect of GPUs, especially within the context of smaller companies or individual developers. The high cost of entry can be a significant barrier, making alternative solutions like CPUs or cloud-based GPU instances more appealing depending on the project's scale and budget.
Overall, the comments paint a picture of nuanced agreement and disagreement with the original article. While acknowledging the limitations and complexities of GPU usage, they generally agree that GPUs are not a panacea but remain a powerful tool for specific workloads. The discussion highlights the importance of careful hardware selection based on individual project requirements and the exciting potential of alternative hardware solutions.