Are you a Rust developer who wants to learn CUDA programming?

Try a GPU programming challenge in Rust. Write a kernel, run it on a real GPU, and use profiling to see what your code is doing.

I added Rust support to GPUFlight Performance Lab so you can do exactly that. Start with a provided problem, make a change, and compare the results.

Learn GPU programming in Rust by solving hands-on challenges in GPU occupancy, memory coalescing, shared-memory bank conflicts, tiled matrix multiplication, atomic contention, warp-level reductions, stencil optimization, online softmax, and so on.

Rust selected in GPUFlight Performance Lab. This vector-add kernel uses 256 threads per block.

Comparing it with CUDA C++ has been a useful way to see how each compiler handles the same work.

One thing I keep coming back to is the compiler output. The source code tells me what I asked for.…