问题
I have a question on working with Python CUDA libraries from Continuum's Accelerate and numba packages. Is using the decorator @jit with target = gpu the same as @cuda.jit?
回答1:
No, they are not the same, although the eventual compilation path into PTX into assembler is. The @jit decorator is the general compiler path, which can be optionally steered onto a CUDA device. The @cuda.jit decorator is effectively the low level Python CUDA kernel dialect which Continuum Analytics have developed. So you get support for CUDA built-in variables like threadIdx and memory space specifiers like __shared__ in @cuda.jit.
If you want to write a CUDA kernel in Python and compile and run it, use @cuda.jit. Otherwise, if you want to accelerate an existing piece of Python use @jit with a CUDA target.
来源：https://stackoverflow.com/questions/35890045/difference-between-cuda-jit-and-jittarget-gpu