You're a bit confused about whether you're passing in a *pointer* to an array or the actual array data. The C struct says pointer to, your packing code says inlined array.
I'd suggest checking out numpy record arrays.
Andreas
On Sonntag 10 Mai 2009, Per B. Sederberg wrote:
> Hi Folks:
>
> I'm working on simulating a simple neural network model on the GPU.
> In my case, I should see benefits from performing many simulations of
> the simple model at once across threads instead of parallelizing
> individual simulations because the neural network is so small.
>
> I'd like to pass a struct with arrays containing parameters and
> initialization information for the neural network and also a place to
> put results. This is only to keep the code clean (otherwise I'll be
> passing in handfuls of parameters to the kernel.) I have had full
> success passing in separate parameters, but have failed to pass the
> struct, getting launch failed errors at various stages of the process
> (sometimes when allocating memory and sometimes with trying to read it
> off the device.)
>
> I've included a simplified example below. I realize the class to
> handle talking to the C struct is a bit crazy, but if it worked I
> could clean it up into a more general class.
>
> Is there any clue as to what is wrong or is there a better way to
> accomplish what I'm trying to do? I'm pretty new to pycuda and cuda,
> so I won't be offended at all if you give me drastically different
> suggestions of what to do or if you point out a ridiculous error that
> I'm making ;)
>
> Thanks,
> Per
>
> PS-> I'm using a git clone of pycuda from about a week ago and version
> 2.1 of CUDA libs on a GTX285.
>
> struct_test.py (also attached, but in case no attachments are allowed):
> ------------------
>
> import pycuda.driver as cuda
> import pycuda.autoinit
> from pycuda.compiler import SourceModule
>
> import numpy as np
>
> mod = SourceModule(
> """
>
> struct results
> {
> unsigned int n;
> float *A;
> float *B;
> };
>
> __global__ void struct_test(results *res)
> {
> unsigned int i;
> for (i=0; i<res->n; i++)
> {
> res->A[i] = res->B[i] + 1;
> }
> }
>
> """)
>
>
> cu_struct = mod.get_function("struct_test")
>
> class Results(object):
> def __init__(self, n=10):
> self._cptr = None
> self.n = np.uint32(n)
> self.A = np.zeros(self.n,dtype=np.float32)
> self.B = np.ones(self.n,dtype=np.float32)
> def send_to_gpu(self):
> if self._cptr is None:
> self._cptr = cuda.mem_alloc(self.nbytes())
> cuda.memcpy_htod(self._cptr, self.pack())
> def get_from_gpu(self):
> if not self._cptr is None:
> tempstr = np.array([' ']*self.nbytes())
> cuda.memcpy_dtoh(tempstr,self._cptr)
> ind = np.array([0,self.n.nbytes])
> self.n = np.fromstring(tempstr[ind[0]:ind[1]],
>
> dtype=self.n.dtype).reshape(self.n.shape) ind[0] += self.n.nbytes
> ind[1] += self.A.nbytes
> self.A = np.fromstring(tempstr[ind[0]:ind[1]],
>
> dtype=self.A.dtype).reshape(self.A.dtype) ind[0] += self.A.nbytes
> ind[1] += self.B.nbytes
> self.B = np.fromstring(tempstr[ind[0]:ind[1]],
>
> dtype=self.B.dtype).reshape(self.B.dtype) def pack(self):
> return self.n.tostring() + self.A.tostring() + self.B.tostring()
> def nbytes(self):
> return self.n.nbytes + self.A.nbytes + self.B.nbytes
>
> res = Results(10)
> res.send_to_gpu()
> cu_struct(res._cptr, block=(1,1,1))
> res.get_from_gpu()
>
> print res.A
> print res.B
> print res.n
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