是否可以在 Visual Studio 编写的 MexGateway 代码中将变量预先分配给 CPU/GPU 内存? [英] Is it possible to pre-allocate a variable to CPU/GPU memory in the MexGateway code written in Visual Studio?

问题描述

我正在尝试编写一个 MexGateway 代码，将 matlab 中的两个变量传递给编译后的 MexFile，将变量复制到 cuda 内核，进行处理并将结果返回给 Matlab.我需要在 matlab 的 for 循环中使用这个 MexFile.

I'm trying to write a MexGateway code to pass two variables in matlab to the compiled MexFile, copy the variables to a cuda kernel, do the processing and bring back the results to Matlab. I need to use this MexFile in a for loop in matlab.

问题在于:这两个输入对于我的应用程序来说非常大，并且在每个循环中只有其中一个(在以下代码中称为 Device_Data)在更改.所以，我正在寻找一种预先分配稳定输入的方法，这样它就不会在我的 for 循环的每次迭代中从 GPU 中删除.我还需要说，我真的需要在我的 Visual Studio 代码中做到这一点，并在 MexGateway 代码中做到这一点(我不想在 Matlab 中做到这一点).有什么解决办法吗?

The problem is that: The two inputs are huge for my application and ONLY one of them (called Device_Data in the following code) is changing in each loop. So, i'm looking for a way to pre-allocate the stable input so that it does not remove from the GPU at each iteration of my for loop. I also need to say that I really need to do it in my visual studio code and make this happen in the MexGateway code (I do not want to do it in Matlab). is there any solution for this?

这是我的代码(我已经编译过了.它工作正常):

Here is my code (I have already compiled it. It works fine):

#include <cuda_runtime.h>
#include "device_launch_parameters.h"
#include <stdio.h>
#include "cuda.h"
#include <iostream>
#include <mex.h>
#include "MexFunctions.cuh"




__global__ void add (int* Device_Data, int* Device_MediumX, int N) {
int TID = threadIdx.y * blockDim.x + threadIdx.x;
if (TID < N) {
    for (int i = 0; i < N; i++) {
        Device_Data[i] = Device_Data[i] + Device_MediumX[i];
    }
}
}
void mexFunction(int nlhs, mxArray* plhs[],
int nrhs, const mxArray* prhs[]) {

int N = 128;
int* MediumX;
int* Data;
int* Data_New;

MediumX = (int*)mxGetPr(prhs[0]);
Data = (int*)mxGetPr(prhs[1]);

plhs[0] = mxCreateNumericMatrix(N,1, mxINT32_CLASS, mxREAL);
Data_New = (int*)mxGetData(plhs[0]);


int ArrayByteSize = sizeof(int) * N;
int* Device_MediumX; // device pointer to the X coordinates of the medium
gpuErrchk(cudaMalloc((int**)&Device_MediumX, ArrayByteSize));
gpuErrchk(cudaMemcpy(Device_MediumX, MediumX, ArrayByteSize, cudaMemcpyHostToDevice));

int* Device_Data; // device pointer to the X coordinates of the medium
gpuErrchk(cudaMalloc((int**)&Device_Data, ArrayByteSize));
gpuErrchk(cudaMemcpy(Device_Data, Data, ArrayByteSize, cudaMemcpyHostToDevice));

dim3 block(N, 1);
dim3 grid(1);//SystemSetup.NumberOfTransmitter
add << <grid, block >> > (Device_Data, Device_MediumX, N);

(cudaMemcpy(Data_New, Device_Data, ArrayByteSize, cudaMemcpyDeviceToHost));


cudaDeviceReset();

}

推荐答案

可以，只要你有 MATLAB 的分布式计算工具箱/并行计算工具箱.

Yes it is possible, as long as you have the Distributed Computing Toolbox/Parallel computing toolbox of MATLAB.

工具箱允许在普通 MATLAB 代码中有一个名为 gpuArrays 的东西，但它也有一个 C 接口，您可以在其中获取和设置这些 MATLAB 数组的 GPU 地址.

The toolbox allows to have a thing called gpuArrays in normal MATLAB code, but it also has a C interface where you can get and set these MATLAB arrays GPU addresses.

您可以在此处找到文档:

You can find the documentation here:

https:///uk.mathworks.com/help/parallel-computing/gpu-cuda-and-mex-programming.html?s_tid=CRUX_lftnav

例如，对于 mex 文件的第一个输入:

For example, for the first input to a mex file:

mxGPUArray const *dataHandler= mxGPUCreateFromMxArray(prhs[0]); // Can be CPU or GPU, will copy to GPU if its not already there
float  *  d_data = static_cast<float  *>( (float *)mxGPUGetDataReadOnly(dataHandler)); // get the pointer itself (assuming float data)

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