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Essential insights from Hacker News discussions

Ask HN: How to learn CUDA to professional level

Hacker News Discussion

Here's a breakdown of the key themes that emerged from the Hacker News discussion about learning CUDA, along with supporting quotes:

Ease of Entry vs. Mastery

A recurring theme is that while the basics of CUDA are relatively easy to pick up, achieving true mastery and optimization is a significant challenge. Many users highlight the steep learning curve beyond the initial stages.

  • "I found it easy to start... I'd say CUDA is pretty nice/fun to start with, and it's possible to get quite far for a novice programmer. However getting deeper and achieving real advantage over CPU is hard." (majke)
  • "Beginnings are nice and fun. You can get quite far on the optimizing compute part. But getting compatibility for differnt chips and memory access is hard. When you start, chose specific problem, specific chip, specific instruction set." (majke)

The Painful Debugging Process

Several commenters emphasized the difficulty and time-consuming nature of debugging CUDA code, often involving tools like compute-sanitizer and Nsight.

  • "I think learning CUDA for me is an endeavor of pain and going through 'compute-sanitizer' and Nsight because you will find that most of your time will go into debugging why things is running slower than you think." (elashri)
  • "This is so true it hurts." (kevmo314, responding to the above quote)

Practical Approach & Incremental Learning

The advice to start small, focus on correctness before optimization, and gradually increase complexity came up multiple times. Porting existing CPU code to CUDA was also suggested.

  • "Take things slowly. Take a simple project that you know how to do without CUDA then port it to CUDA ane benchmark against CPU and try to optimize different aspect of it... The one advice that can be helpful is not to think about optimization at the beginning. Start with correct, then optimize. A working slow kernel beats a fast kernel that corrupts memory." (elashri)
  • "Write the functionality you want to have in increasing complexity. Write loops first, then parallelize these loops over the grid. Use global memory first, then put things into shared memory and registers. Use plain matrix multiplication first, then use mma (TensorCore) primitives to speed things up." (korbip)
  • "Then supplement with resources A/R. Ideally, find some tasks in your programs that are parallelize. (Learning what these are is important too!), and switch them to Cuda. If you don't have any, make a toy case, e.g. an n-body simulation." (the__alchemist)

Hardware and Architecture Considerations

Many users recommend not only learning the CUDA language, but also spending effort on understanding the nuances of GPU hardware and parallel computing paradigms.

  • "While a GPU and the Nvlink interfaces are Nvidia specific, working in a massively-parallel distributed computing environment is a general branch of knowledge that is translatable across HPC architectures." (lokimedes)
  • "Once you have understood the principle working mode and architecture of a GPU...Iterate over the CUDA C Programming Guide. It covers all (most) of the functionality that you want to learn - but can't be just read an memorized. When you apply it you learn it." (korbip)

CUDA vs. Alternative Approaches/Libraries

Several users expressed frustration with the CUDA ecosystem, particularly the lack of vendor-agnostic solutions, and highlighted the availability of higher-level libraries. Some prefer CUDA for its practicality, while others prefer to use alternatives, but the general frustration with alternatives remains.

  • "I'd rather learn to use a library that works on any brand of GPU. If that is not an option, I'll wait!" (amelius)
  • "This is continuously a point of frustration! Vulkan compute is... suboptimal. I use Cuda because it feels like the only practical option. I want Vulkan or something else to compete seriously, but until that happens, I will use Cuda." (the__alchemist)
  • "Then learn PyTorch." (latchkey)
  • "Also all GPU vendors, including Intel and AMD, also rather push their own compute APIs, even if based on top of Khronos ones." (pjmlp)

CUDA and the AI/ML Hype

Some commenters cautioned against learning CUDA solely because of the AI/ML hype, emphasizing that CUDA skills are distinct from ML model building and more relevant to specialized areas like game development, HPC, and hardware engineering.

  • "Assuming you are asking this because of the deep learning/ChatGPT hype, the first question you should ask yourself is, do you really need to? The skills needed for CUDA are completely unrelated to building machine learning models... CUDA belongs to the domain of game developers, graphics people, high performance computing and computer engineers (hardware). From the point of view of machine learning development and research, it's nothing more than an implementation detail." (Onavo)
  • "But, if you're trying to get hired programming CUDA, what that really means is they want you implementing AI stuff (unless it's game dev). AI programming is a much wider and deeper subject than CUDA itself, so be ready to spend a bunch of time studying and hacking to come up to speed in that." (throwaway81523)

Fragmentation and Hardware Compatibility Issues

The issue of hardware fragmentation, with different NVIDIA GPUs supporting different instruction sets and features, was raised as a significant challenge for writing portable and performant CUDA code.

  • "Additionally there is a problem with Nvidia segmenting the market - some opcodes are present in old gpu's (CUDA arch is not forwards compatible). Some opcodes are reserved to "AI" chips (like H100). So, to get code that is fast on both H100 and RTX5090 is super hard. Add to that a fact that each card has different SM count and memory capacity and bandwidth... and you end up with an impossible compatibility matrix." (majke)

Recommended Resources and Learning Paths

Several users shared specific resources, including books, online courses, and code examples, to aid in learning CUDA and parallel programming.