terracuda

Good question. For your first point, Terracuda doesn't do anything particularly intelligent with movement between host/device memories--it's mostly there just to hide that away from you. However, empirically, if your baseline is Lua or Python, almost anything you write in CUDA is going to be more efficient so long as you have enough data to parallelize over. As for the second point, that is still mostly left to the programmer. GPU code doesn't necessarily preclude complex data structures, however. For one of my benchmarks, we used a software renderer that generates a quadtree on the fly and accesses that during the shading phase (https://github.com/willcrichton/terracuda/blob/master/render...).

Project author here. Your first claim is true, and you can verify it here: https://github.com/willcrichton/terracuda/blob/master/matrix... However, I would dispute that it makes the benchmark meaningless. I agree that no one will write the hand-optimized matrix multiply routine in Terra, but I think that misunderstands the use case of the language. Terracuda is intended to make everyday GPGPU computations super easy, so you can speed up simple scripting code that would otherwise be order of magnitudes slower. Look at the other examples in the repo: if you wanted to write a simple hash function, Mandelbrot generator, or renderer, Terracuda makes it simple to accelerate Lua code. There will always be room for super optimized kernels for common functions like matrix multiplication, but I believe there's a strong need for a language that hand