Edric M Ellis <eellis@mathworks.com> wrote in message
> Do you have MATLAB Distributed Computing Server installed on the
> 'server' machine? (http://www.mathworks.com/products/distriben/) If so,
> you could use MATLABPOOL and SPMD to do this - if you are able to open a
> matlabpool using the GPU server machine as a worker, the body of the
> SPMD block can execute CUDA code using for example the CUDAKernel
> interface (assuming that's what you're interested in, and that gpuArray
> capabilities are not sufficient).
>
> I must say though that you will almost certainly find life simpler if
> you can develop the kernel locally on your desktop machine. The CUDA
> toolchain can be downloaded without charge from NVIDIA
> (https://developer.nvidia.com/cuda-downloads) and can run even on a
> machine with no GPU (I must admit I haven't tried with the newer
> releases where the toolkit and drivers are bundled together). Obviously,
> if you have a capable GPU on your desktop (even if a poorly performing
> one), you'll be able to develop and debug stuff there.
>
> Cheers,
>
> Edric.
Hello Edric,
Many thanks for your email.
Actually I am able to open a matlabpool using the right GPU and I have already tried to install the CUDA toolkit Dev on my own computer. Nevertheless, the installation does not finish properly and I have the following pop up message "Display Driver failed installation".
I guess that the newest releases include some safety checking regarding the GPU don't they?
I am checking with a previous version (CUDA 4.2) and I will keep you updated. In the same time if you have any suggestions regarding this email, please let me know.
Many thanks,
Alafm![]()
> Do you have MATLAB Distributed Computing Server installed on the
> 'server' machine? (http://www.mathworks.com/products/distriben/) If so,
> you could use MATLABPOOL and SPMD to do this - if you are able to open a
> matlabpool using the GPU server machine as a worker, the body of the
> SPMD block can execute CUDA code using for example the CUDAKernel
> interface (assuming that's what you're interested in, and that gpuArray
> capabilities are not sufficient).
>
> I must say though that you will almost certainly find life simpler if
> you can develop the kernel locally on your desktop machine. The CUDA
> toolchain can be downloaded without charge from NVIDIA
> (https://developer.nvidia.com/cuda-downloads) and can run even on a
> machine with no GPU (I must admit I haven't tried with the newer
> releases where the toolkit and drivers are bundled together). Obviously,
> if you have a capable GPU on your desktop (even if a poorly performing
> one), you'll be able to develop and debug stuff there.
>
> Cheers,
>
> Edric.
Hello Edric,
Many thanks for your email.
Actually I am able to open a matlabpool using the right GPU and I have already tried to install the CUDA toolkit Dev on my own computer. Nevertheless, the installation does not finish properly and I have the following pop up message "Display Driver failed installation".
I guess that the newest releases include some safety checking regarding the GPU don't they?
I am checking with a previous version (CUDA 4.2) and I will keep you updated. In the same time if you have any suggestions regarding this email, please let me know.
Many thanks,
Alafm