PyCuda-如何在内核中使用以Python编写的函数？

Question

I want to parallelize my Python code and I'm trying to use PyCuda. What I saw so far is that you have to write a "Kernel" in C into your Python code. This Kernel is what is going to be parallelized. Am I right? Example (doubling an array of random numbers, from https://documen.tician.de/pycuda/tutorial.html ):

import pycuda.driver as cuda
import pycuda.autoinit
from pycuda.compiler import SourceModule
import numpy
a = numpy.random.randn(4, 4)
a = a.astype(numpy.float32)
a_gpu = cuda.mem_alloc(a.nbytes)
cuda.memcpy_htod(a_gpu, a)
# Kernel:
mod = SourceModule("""
  __global__ void doublify(float *a)
  {
    int idx = threadIdx.x + threadIdx.y*4;
    a[idx] *= 2;
  }
  """)
func = mod.get_function("doublify")
func(a_gpu, block=(4, 4, 1))
a_doubled = numpy.empty_like(a)
cuda.memcpy_dtoh(a_doubled, a_gpu)

print(a_doubled)
print(a)

The point is that my Python code has classes and other things all suitable with Python and unsuitable with C (ie untranslatable to C).

Let me clarify: my has 256 independent for-loops that I want to parallelize. These loops contain Python code that can't be translated to C.

How can I parallelize an actual Python code with PyCuda without translating my code to C?

Answer 1

You can't.

PyCUDA doesn't support device side python, all device code must be written in the CUDA C dialect.

Numba includes a direct Python compiler which can allow an extremely limited subset of Python language features to be compiled and run directly on the GPU. This does not include access to any Python libraries such as numpy, scipy, etc.