The library cudapp is the basis of SimplePyCuda, a simple wrapper for CUDA functions in Python. The idea is to workaround the issues regarding Context in old CUDA versions, that persist on PyCuda.
No need to pre-compile the project.
Install from pip (https://pypi.org/project/simplepycuda/):
pip install simplepycudaIn Python:
import ctypes
import numpy
from simplepycuda import SimplePyCuda, SimpleSourceModule, grid, block
def classicExample(cuda):
a = numpy.random.randn(4,4)
a = a.astype(numpy.float32)
print a
a_gpu = cuda.mem_alloc(a.nbytes)
cuda.memcpy_htod(a_gpu, a)
mod = SimpleSourceModule("""
#include<stdio.h>
__global__ void doublify ( float* a )
{
int idx = threadIdx.x + threadIdx.y*4;
a[idx] *= 2;
//printf("oi=%d\\n",idx);
}
""")
func = mod.get_function("doublify")
# TODO: this next line will be made automatically in get_function method... just need a few more time :)
func.argtypes = [ctypes.c_void_p, grid, block, ctypes.c_ulong, ctypes.c_ulong]
func(a_gpu, grid(1,1), block(4,4,1), 0, 0)
cuda.memcpy_dtoh(a, a_gpu)
cuda.deviceSynchronize()
print a
cuda.free(a_gpu) # this is not necessary in PyCUDA
print "Finished"
def main():
cuda = SimplePyCuda()
classicExample(cuda)
return 0MIT License - Igor Machado Coelho and Rodolfo Pereira Araujo (2017)