NumbaPro CUDA Python speeds up a Monte Carlo pricer 14x

I'm be surprised if there was that large of a speedup going from a well-written numpy implementation to C/C++ (assuming a non-parallel implementation in both)...

In my experience, it's usually more like 2x-5x for a straight translation of the algorithm from numpy to C. (Though I have to confess that I often write rather naive C...)

Numpy performs quite well if you avoid a few common pitfalls. It certainly can be beat, but it's often faster than people think it is.

NumbaPro generates CUDA code that runs on the GPU. I don't see how using C++ to do it would necessarily make it faster as the code executing on the CPU end of things is not the bottleneck.

If you look at the profiler screenshot at the bottom of the page, the time spent in the cuRAND kernel (cyan) is ~2x longer than in the numbapro kernel (purple). Even if the numbapro kernel is written in CUDA-C and suppose it will be a lot faster, you still won't hit 100x speedup. In addition, all kernels are double-precision, the 100x speedup is more common for single-precision kernels.

From my experience with CUDA/OpenCL, its easy to get a working algorithm that provides some speedup, but getting it to fully utilize the GPU takes some manual optimization.

Things like optimizing memory accesses to prevent bank conflicts, using per thread-group shared memory effectively to reduce global memory accesses are a bit tricky to get right, and I'm not sure how well auto-generated code would be able to do this.

>There's a few ways to do it, but what's usually done is to have a large bitmap filled with random values that's accessed by a function of threadID and current pixel (or whatever other info you have handy).

Do you have any good links that elaborate on this kind of technique? I had an idea that sounds a lot like this once, but being fairly ignorant about the topic I had no idea what to call it or how to implement it.

Thanks.