Hello everyone!
I'm new to PyCUDA and GPU programming however initial experiences have been
very pleasant. I started out by some simple task and it seems blazing faster
than running on a CPU. However, I would like to confirm that it's indeed as
fast as it seems.
My main question is whether after 'func' is called and access of the prompt is
regained, are there still any of the tasks running on the GPU? If so, is there
a way to block from performing the next tasks until it has finished?
I've posted the code below for reference purposes. You can change the value of
N so that it's faster. I set it very close to the limit so that I might witness
a delay on returning control of the command prompt.
Thank you in advance and please keep up the excellent work!
-Orestis
=================================================================
import pycuda.driver as cuda
import pycuda.autoinit
from pycuda.compiler import SourceModule
import pycuda.gpuarray as gpuarray
import sys, numpy, random, string
# create random input data
N = 33500000
buf = ''.join(random.choice(string.ascii_uppercase + string.ascii_lowercase +
string.digits) for x in xrange(N))
mod = SourceModule("""
__global__ void get_words(int N, char *a,unsigned int *b)
{
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if ( idx <N-3)
{
b[idx] = (a[idx] << 24) + (a[idx+3]);
}
}
""")
func = mod.get_function("get_words")
# copy buffer to GPU
bufArray = cuda.mem_alloc(N)
cuda.memcpy_htod(bufArray, buf)
# create results array on GPU
resArray = gpuarray.to_gpu(numpy.zeros((N-3,1),dtype=numpy.int32))
# setup parameters and execute function
threadsPerBlock = 512
blocksPerGrid = (N+threadsPerBlock-1)/threadsPerBlock
func(numpy.int32(len(buf)), bufArray, resArray, grid=(blocksPerGrid ,1),
block=(threadsPerBlock,1,1))
# get back results
a = numpy.zeros(N-3,1),dtype=numpy.int32)
b = resArray.get(a)
a = a.reshape(-1).tolist() _______________________________________________
PyCUDA mailing list
[email protected]
http://lists.tiker.net/listinfo/pycuda
