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How to convert complex float to complex integer in MEX gateway function?

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I'm trying to write a MEX gateway function (in CUDA) to add two complex integer arrays given by Matlab. Currently, the following code works fine for 2 complex float arrays. Could you please let me know how should i change the code to be able to read complex integer from Matlab? it should be about the way i define prhs!!
#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 "matrix.h"
#include <thrust/complex.h>
#include <string.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;
__device__ void atAddComplex(fcomp* a, fcomp b) {
float* x = (float*)a; /* cast x pointer to the real part */
float* y = x + 1; /* cast the y pointer to the following mem. address (imaginary part) */
//use atomicAdd for double variables
atomicAdd(x, b.real());
atomicAdd(y, b.imag());
__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];
//Device_Data1[GID_RowBased] = Device_Data1[GID_RowBased] + Device_Data2[GID_RowBased];
atAddComplex(&Device_Data1[GID_RowBased], Device_Data2[GID_RowBased]);
// atomicAdd(&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* Device_Data1;
fcomp* Device_Data2;
fcomp* DataRes;
fcomp* Device_DataRes;
mxComplexSingle* Data1 = mxGetComplexSingles(prhs[0]);
mxComplexSingle* Data2 = mxGetComplexSingles(prhs[1]);
(cudaMalloc((void**)&Device_Data1, ArrayByteSize));
(cudaMemcpy(Device_Data1, Data1, ArrayByteSize, cud SoaMemcpyHostToDevice));
(cudaMalloc((void**)&Device_Data2, ArrayByteSize));
(cudaMemcpy(Device_Data2, Data2, ArrayByteSize, cudaMemcpyHostToDevice));
plhs[0] = mxCreateNumericMatrix(N, 1, mxSINGLE_CLASS, mxCOMPLEX);
DataRes = static_cast<fcomp*> (mxGetData(plhs[0]));
(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);
(cudaMemcpy(DataRes, Device_Data1, ArrayByteSize, cudaMemcpyDeviceToHost));

Accepted Answer

James Tursa
James Tursa on 22 Jun 2021
Edited: James Tursa on 22 Jun 2021
I would guess you can just use the appropriate data types. E.g.,
mxComplexInt32* Data1 = mxGetComplexInt32s(prhs[0]);
mxComplexInt32* Data2 = mxGetComplexInt32s(prhs[1]);
Or, if you wanted to get at the pointers more directly since you will be casting them anyway, then just
int* Data1 = (int *) mxGetData(prhs[0]);
int* Data2 = (int *) mxGetData(prhs[1]);
This all assumes the R2018a+ interleaved complex data model of MATLAB of course.

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