Is it possible to use cuRAND with feval (Parallel computing toolbox)?
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Hi,
I am trying to call feval instruction (Parallel Computing toolbox) with a kernel which uses the cuRAND library (<http://developer.nvidia.com/curand)>, and I need to pass to feval an argument of type curandState (needed to initialize random generators in cuRAND).
I have something similar to:
K=parallel.gpu.CUDAKernel('kernel.ptx','kernel.cu');
[arg_out]=feval(K,arg_in, state);
"state" must be a curandState variable.
I tried cheating MATLAB with:
[arg_out]=feval(K,arg_in, 1);
But I got the following error message:
_Error using iParseToken (line 259) Unsupported type in argument specification "curandState * state".
Error in C:\Program Files\MATLAB\R2011b\toolbox\distcomp\gpu\+parallel\+internal\+gpu\handleKernelArgs.p>iParseCPrototype (line 181)
Error in C:\Program Files\MATLAB\R2011b\toolbox\distcomp\gpu\+parallel\+internal\+gpu\handleKernelArgs.p>handleKernelArgs (line 70)_
I have not found any information in google. Could anyone please help me?
Thank you in advance.
María.
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採用された回答
Edric Ellis
2012 年 1 月 31 日
Is there a particular reason why you wish to use CURAND rather than the builtin random number generators in R2011b? You can use
parallel.gpu.GPUArray.rand(...)
This uses the 'combRecursive' random number generator which is also available in MATLAB, and (I believe) is a very high quality random number generator. "help parallel.gpu.rng" should tell you more.
If you did wish to use CURAND, you would need to pack the curandState structure elements into a GPUArray probably of type 'unsigned char', and then pass that back into a separate kernel to do the actual random number generation.
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その他の回答 (2 件)
Edric Ellis
2012 年 2 月 1 日
For what it's worth, I have some example CUDA code and MATLAB driving code to show how one might use CURAND. First off, here's the CUDA code:
#include <curand_kernel.h>
const size_t stateSize = sizeof( curandState );
__device__ void copyState( void * out, void const * in ) {
unsigned char * outc = static_cast< unsigned char * >( out );
unsigned char const * inc = static_cast< unsigned char const * >( in );
for ( int i = 0; i < stateSize; ++ i ) {
outc[i] = inc[i];
}
}
__global__ void returnStateSize( unsigned int * value ) {
value[0] = stateSize;
}
__global__ void initState( unsigned char * stateArray ) {
int idx = blockDim.x * blockIdx.x + threadIdx.x;
curandState state;
curand_init( 1234, idx, 0, &state );
copyState( stateArray + idx * stateSize, &state );
}
__global__ void generate( double * x, unsigned char * stateArray ) {
int idx = blockDim.x * blockIdx.x + threadIdx.x;
curandState state;
copyState( &state, stateArray + idx * stateSize );
x[idx] = curand_uniform_double( &state );
copyState( stateArray + idx * stateSize, &state );
}
And here's some MATLAB code which uses that:
import parallel.gpu.GPUArray;
% Get the number of bytes per thread of state.
stateSizeK = parallel.gpu.CUDAKernel( 'userand.ptx', 'userand.cu', 'returnStateSize' );
stateSz = double( gather( feval( stateSizeK, zeros( 'uint32' ) ) ) );
% Set up the random state
initK = parallel.gpu.CUDAKernel( 'userand.ptx', 'userand.cu', 'initState' );
initK.ThreadBlockSize = 256;
initK.GridSize = 10;
randState = feval( initK, GPUArray.zeros( stateSz, 256*10, 'uint8' ) );
genK = parallel.gpu.CUDAKernel( 'userand.ptx', 'userand.cu', 'generate' );
genK.ThreadBlockSize = 256;
genK.GridSize = 10;
% Generate some random numbers
[rand1, randState] = feval( genK, GPUArray.zeros(1, 256*10), randState );
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