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How to handle Complex input in MEX gateway function in CUDA?

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Hi,
I'm trying to write a MEX gateway function to add two complex vectors. I use thrust to do the processing. However, when i want to compile the code in Matlab, i get this error :
Error using mex
C:/Users/moein.m/Documents/C++/ImageReconstruction_VisualStudioCode/Project7_TUI_CUDA3/TUI_CUDA/TUI_CUDA/test2_GPUArray_Complex.cu(55):
error: identifier "mxGetComplexDoubles" is undefined
1 error detected in the compilation of
"C:/Users/moein.m/Documents/C++/ImageReconstruction_VisualStudioCode/Project7_TUI_CUDA3/TUI_CUDA/TUI_CUDA/test2_GPUArray_Complex.cu".
nvcc warning : The 'compute_35', 'compute_37', 'compute_50', 'sm_35', 'sm_37' and 'sm_50' architectures
are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress
warning).
test2_GPUArray_Complex.cu
Error in mexcuda (line 168)
[varargout{1:nargout}] = mex(mexArguments{:});
Error in test2_GPUArray_matlabRunner (line 4)
mexcuda('-v', 'test2_GPUArray_Complex.cu' , 'NVCCFLAGS=-gencode=arch=compute_50,code=sm_50 -Xptxas
-dlcm=cg');
Here is my code:
#include <cuda_runtime.h>
#include "device_launch_parameters.h"
#include <stdio.h>
#include "cuda.h"
#include <iostream>
#include <mex.h>
#include "gpu/mxGPUArray.h"
//#include <cuComplex.h>
//#include <cublas_v2.h>
#include <thrust/complex.h>
#include "matrix.h"
#define gpuErrchk(ans) { gpuAssert((ans), __FILE__, __LINE__); }
inline void gpuAssert(cudaError_t code, const char* file, int line, bool abort = true)
{
if (code != cudaSuccess)
{
fprintf(stderr, "GPUassert: %s %s %d\n", cudaGetErrorString(code), file, line);
if (abort) exit(code);
}
}
typedef thrust::complex<float> fcomp;
__global__ void add(fcomp * Device_DataRes, fcomp * Device_Data1, fcomp * Device_Data2, int N) {
int TID = threadIdx.y * blockDim.x + threadIdx.x;
int BlockOFFset = blockDim.x * blockDim.y * blockIdx.x;
int GID_RowBased = BlockOFFset + TID;
if (GID_RowBased < N) {
Device_DataRes[GID_RowBased] = Device_Data1[GID_RowBased] + Device_Data2[GID_RowBased];
}
}
void mexFunction(int nlhs, mxArray* plhs[],
int nrhs, const mxArray* prhs[]) {
int N = 1000;
int ArrayByteSize = sizeof(fcomp) * N;
fcomp* Data1;
fcomp* Device_Data1;
fcomp* Data2;
fcomp* Device_Data2;
fcomp* DataRes;
fcomp* Device_DataRes;
Data1 = static_cast<fcomp*>(mxGetComplexDoubles(prhs[0]));
//Data1 = (fcomp*)(mxGetComplexDoubles(prhs[0]));
gpuErrchk(cudaMalloc((void**)&Device_Data1, ArrayByteSize));
gpuErrchk(cudaMemcpy(Device_Data1, Data1, ArrayByteSize, cudaMemcpyHostToDevice));
Data2 = static_cast<fcomp*>(mxGetComplexDoubles(prhs[1]));
gpuErrchk(cudaMalloc((void**)&Device_Data2, ArrayByteSize));
gpuErrchk(cudaMemcpy(Device_Data2, Data2, ArrayByteSize, cudaMemcpyHostToDevice));
plhs[0] = mxCreateNumericMatrix(N, 1, mxSINGLE_CLASS, mxCOMPLEX);
DataRes = static_cast<fcomp*> (mxGetData(plhs[0]));
gpuErrchk(cudaMalloc((void**)&Device_DataRes, ArrayByteSize));
dim3 block(1024);
int GridX = (N / block.x + 1);
dim3 grid(GridX);//SystemSetup.NumberOfTransmitter
add << <grid, block >> > (Device_DataRes, Device_Data1, Device_Data2, N);
gpuErrchk(cudaMemcpy(DataRes, Device_DataRes, ArrayByteSize, cudaMemcpyDeviceToHost));
cudaFree(Device_Data1);
cudaFree(Device_Data2);
cudaFree(Device_DataRes);
}
Could you please let me know what is wrong here? It seems that "mxGetComplexDoubles" is making the problem.
Regards,
Moein.

Accepted Answer

Edric Ellis
Edric Ellis on 18 Jun 2021
Firstly, as per the doc page for interleaved complex data, you need to add the command-line flag -R2018a to use mxGetComplexDoubles. But actually I think given that you are casting to thrust::complex<float>, you actually should use mxGetComplexSingles.
You can't directly cast between the mxComplexSingle* returned by mxGetComplexSingles and thrust::complex<float>, but it should still work to perform the memcpy operations. In other words, you need to do this:
mxComplexSingle * Data1 = mxGetComplexSingles(prhs[0]);
thrust::complex<float> * Device_Data1;
gpuErrchk(cudaMalloc((void**)&Device_Data1, ArrayByteSize));
gpuErrchk(cudaMemcpy(Device_Data1, Data1, ArrayByteSize, cudaMemcpyHostToDevice));
  3 Comments
Edric Ellis
Edric Ellis on 21 Jun 2021
It doesn't look like atomicAdd is what you want here - that's for scalar values I think. In this case, I'd simply return the gpuArray data back to MATLAB and use the builtin overload of + for gpuArray.

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