Hi Javier,
It would probably help if you attach the source of the expon_them()
function (since something is definitely happening there).
I'll try to do some psychic debugging though. I find these lines suspicious:
self.weights_lateral = gpuarray.to_gpu(self.weight_matrixLateral())
self.weights_local = gpuarray.to_gpu(self.weight_matrixLocal())
...
self.unitarysX = gpuarray.to_gpu(unitaryX)
self.unitarysY = gpuarray.to_gpu(unitaryY)
In other places you use single precision (np.float32) explicitly, but
here it looks like you are just tossing numpy arrays to GPU, and numpy
arrays have double precision by default. If this is the case, try to
change the lines like:
self.weights_lateral =
gpuarray.to_gpu(self.weight_matrixLateral().astype(np.float32))
... and so on.
Best regards,
Bogdan
On Wed, Oct 13, 2010 at 4:17 AM, Javier <javier.balad...@inria.fr> wrote:
>
> Hello,
>
> I am writing some code using pycuda and have some problems, hope somebody
> could please help me!
>
> I am writing an application that will execute several calls to a function
> that executes a kernel. Every call will share some of the same data, so I am
> planning on sending this data before the first call and keep it inside the
> gpu for the future ones. This way I will not need to copy the same thing
> over and over again to the gpu, saving time. Also this function make a call
> to two different kernels.
>
> I show you how I am trying to do this:
>
> in the init of my class I have:
>
> self.weights_lateral = gpuarray.to_gpu(self.weight_matrixLateral())
> self.weights_local = gpuarray.to_gpu(self.weight_matrixLocal())
> unitaryX, unitaryY = self.unitary_vectors()
> self.unitarysX = gpuarray.to_gpu(unitaryX)
> self.unitarysY = gpuarray.to_gpu(unitaryY)
>
> This are the gpus arrays I want to keep in the gpu memory.
>
> Afterwards I have a function that do this:
>
> #load data into the gpu
> values_gpu =
> gpuarray.to_gpu(np.array(current_values,dtype=np.float32))
>
> #calculate the array of S(v)
> expon_them = mod.get_function("expon_them")
>
> svv =
> gpuarray.to_gpu(np.zeros(self.angle_size*self.image_size*self.image_size,dtype
> = np.float32))
> expon_them(svv, values_gpu,
> np.float32(0.0),np.float32(self.lamb),grid =
> (self.num_blocks,1),block=(self.threads_per_block,1,1))
>
>
> resul =
> gpuarray.to_gpu(np.zeros(self.angle_size*self.image_size*self.image_size,dtype
> = np.float32))
> dv_them = mod.get_function("dv_them")
>
>
> dv_them(resul, values_gpu, self.dx,self.weights_lateral,
> self.weights_local, svv, self.alpha, self.mu, self.beta, self.inpt
> ,self.unitarysX,self.unitarysY,np.float32(0),self.lamb, grid =
> (self.num_blocks,1),block=(self.threads_per_block,1,1))
>
>
> I am having an error when I do resul =
> gpuarray.to_gpu(np.zeros(self.angle_size*self.image_size*self.image_size,dtype
> = np.float32))
> The error is: LaunchError: cuMemcpyHtoD failed: launch failed
>
> I have also been checking if I can acces the gpu arrays that I keep in
> memory and I can not access them.
>
> Something seems to be happening to the context after I execute expon_them
> and I dont know what.
>
> Could somebody please tell me how to fix this?
> --
> View this message in context:
> http://pycuda.2962900.n2.nabble.com/Some-help-with-contexts-please-tp5627674p5627674.html
> Sent from the PyCuda mailing list archive at Nabble.com.
>
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>
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