Fil Peters <fil.pet...@yandex.com> writes:
> Hello,
>
> I am just new to pycuda and started testing it. I was wondering if it is
> possible to use the gpuarray functions in a sourcemodule.
>
> For example, I was trying to covert the following code into a pycuda
> sourcemodule:
>
> numpy code:
>
> fac1=np.float32(0.5)
> fac2=np.float32(1.001)
> for i in range(niter):
> c = a
> d = b
> a = (c + d)*fac1
> b = c*fac2
> f += a*b + a*a
> k = np.dot(a,b)
>
>
> Until the line "f += a*b + a*a" it works well:
>
> mod = SourceModule("""
>
> __global__ void vecmul(float *dest, float *in1, float *in2, float *in3,
> float *in4, int niter)
> {
>
> const int i = blockDim.x*blockIdx.x + threadIdx.x;
>
> for(int n = 0; n < niter; n++) {
> in3[i] = in1[i];
> in4[i] = in2[i];
> in1[i] = (in3[i]+in4[i])*0.5 ;
> in2[i] = in3[i]*1.001;
> dest[i] += in1[i] * in2[i] + in1[i]*in1[i];
> }
> }
> """)
>
> 'of course I realize that the dot product is not very useful in this loop,
> but in my final program I will need to reuse this value in the loop).
> So for this specific case the question is how to incorporate the
> function gpuarray.dot() in the code, or if that is not possible how to
> include a reduction kernel in the sourcemodule.No, you can't, sorry. Andreas
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