cuda-api-wrappers

d then are obvious in retrospect. I think this is true of much of math Yup. And the some of the rest of math becomes obvious when some re-derives it using alternative and more convenient/powerful techniques. > I think the reason so few succeed at this is that it's just too tempting to mix the layers. As an author of a library that also focuses on being a "layer" of sorts (https://github.com/eyalroz/cuda-api-wrappers/), I struggle with this temptation a lot! Especially when, like the author says, the boundaries of the layers are not as clear as one might imagine. > I strongly believe that iteration time has a huge impact on programmer satisfaction I'm pretty certain that the vast majority developers perform 10x more incremental builds than full builds. So, not just satisfaction - it's just most of what we do. It's also those builds which we wait-out rather than possible go look for some distraction: https://xkcd.com/303/ OTOH, the article doesn't mention interaction with build artifact caching schemes, which lessen the difference between building from scratch and building incrementally. > Peter Collingbourne found Ninja and did the work to plug it into the much more popular CMake ... If anyone is responsible for making Ninja succeed out there in the real world, Peter is due the credit. It is so gratifying when a person you didn't know makes your software project that much more impactful! Makes you really feel optimistic again about humanity and socialism and stuff.

, num_elements) print(f"[Vector addition of {num_elements} elements]") print(f"CUDA kernel launch with {grid_size} blocks of {block_size} threads each") vector_add((grid_size,), (block_size,), args) incorrect = cp.abs(a + b - c) > 1e-5 if cp.any(incorrect): print("Result verification failed at element", cp.argmax(incorrect)) print("Test PASSED") print("SUCCESS") It could be made even shorter with a cp.ElementwiseKernel https://docs.cupy.dev/en/stable/user_guide/kernel.html#basic... Although I have to concede that the automatic grid size computation in cuda-api-wrappers is nice. A few marketing tips for your README: * Put a code example directly at the top. You want to present the selling points of your library to the reader as fast as possible. For reference, look at the CuPy README https://github.com/cupy/cupy?tab=readme-ov-file#cupy--numpy-... which immediately shows reader what it is good for. Your README starts with lots of text, but nobody reads text anymore these days. A link to examples is almost at the end, and then the examples are deeply nested. * The first links in the README should link to your own library, for example to documentation or examples. You do not want to lead the reader away from your GitHub page. * Add syntax highlighting with "cpp" after triple backticks: ```cpp <code here> ```

CUDA, OpenCL and maybe HIP/ROCm. Unfortunately, I don't have the free time to do this on my own, so if anyone is interested in collaborating on something like that, please drop me a line. ----- You can find the OpenCL-that-is-also-CUDA mechanism at: https://github.com/eyalroz/gpu-kernel-runner/blob/main/kerne... and https://github.com/eyalroz/gpu-kernel-runner/blob/main/kerne... (the files are provided alongside a tool for testing, profiling and debugging individual kernels outside of their respective applications.)