Compiling with ptx option in Matlab


I am trying to speed up my Matlab codes using GPU.

For a beginnig I tried a simple vectorAdd function which simply adds two vectors.

The code is as below.

#include "cuda.h"

#include "mex.h"

#define BLOCK_SIZE 256

/* Kernel to add elements of two arrays on the GPU */

__global__ void vecAdd(float* A, float* B, float* C)


	__shared__ float As[BLOCK_SIZE];

	__shared__ float Bs[BLOCK_SIZE];


	int tx = threadIdx.x;

	int bx = blockIdx.x;


	int index = tx + BLOCK_SIZE*bx;


	As[tx] = A[index];

	Bs[tx] = B[index];


	C[index] = As[tx] + Bs[tx];


/* Gateway function */

void mexFunction(int nlhs, mxArray *plhs[],int nrhs, const mxArray *prhs[])


	int M, N;

	float *data1, *data2, *result;

	float *data1_gpu, *data2_gpu, *result_gpu;

	if (nrhs != 2 || nlhs != 1)

		mexErrMsgTxt("The number of input and output arguments must 2 & 1 resp.");

	if ( mxGetM(prhs[0]) != mxGetM(prhs[1]) || mxGetN(prhs[0]) != mxGetN(prhs[1]))

		mexErrMsgTxt("Sizes of input vectors must be the same");

	if ( mxGetM(prhs[0]) != 1 && mxGetN(prhs[0]) != 1)

		mexErrMsgTxt("Inputs should be vectors (i.e. 1xN or Nx1 matrices)");

	if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS || mxGetClassID(prhs[1]) != mxSINGLE_CLASS)

		mexErrMsgTxt("Input vectors should be of type mxSINGLE_CLASS (i.e. single)");


	/* Find the dimensions of the data */

	M = mxGetM(prhs[0]);

	N = mxGetN(prhs[0]);

	if (M*N % BLOCK_SIZE != 0)

		mexErrMsgTxt("Length of the input vectors should be an integer multiple of 256");

	// Create an mxArray for the output data

	plhs[0] = mxCreateNumericArray(mxGetNumberOfDimensions(prhs[0]), mxGetDimensions(prhs[0]),mxSINGLE_CLASS, mxREAL);

	// Create an input and output data array on the GPU

	cudaMalloc( (void **) &data1_gpu,sizeof(float)*M*N);

	cudaMalloc( (void **) &data2_gpu,sizeof(float)*M*N);

	cudaMalloc( (void **) &result_gpu,sizeof(float)*M*N);

	/* Retrieve the input data */

	data1 = (float*)mxGetPr(prhs[0]);

	data2 = (float*)mxGetPr(prhs[1]);

	result = (float*)mxGetPr(plhs[0]);

	// The input array is single precision, it can be sent directly to the card

	cudaMemcpy( data1_gpu, data1, sizeof(float)*M*N,cudaMemcpyHostToDevice);

	cudaMemcpy( data2_gpu, data2, sizeof(float)*M*N,cudaMemcpyHostToDevice);


	// Compute execution configuration using BLOCK_SIZE threads per block

	dim3 dimBlock(BLOCK_SIZE);

	dim3 dimGrid((M*N)/dimBlock.x);

	if ( (N*M) % BLOCK_SIZE !=0 ) 


	// Call function on GPU

	vecAdd<<<dimGrid,dimBlock>>>(data1_gpu, data2_gpu, result_gpu);

	// Copy result back to host

	cudaMemcpy( result, result_gpu, sizeof(float)*M*N, cudaMemcpyDeviceToHost);	

	// Clean-up memory on device





Since they are faster I used shared memory. But when I compare the speed of Matlab and my code (using tic toc and a big vector which is 1024e+4 long) Matlab always beats me.

I would really appreciate your valuable comments on this code, where may I be wrong?

And the main question is: I would like to investigate the assembly code. However -ptx (or -keep) options are not supported by nvmex script

And I hope to find the reason for slowness in assembly code.

PS: I am using Matlab R2008a

First of all, using tic and toc is going to time the entire runtime, including memory copies to and from the GPU, and as the kernel does essentially no mathematical operations but a read and write to global memory, there will be no speed up at all, since the memory latency will dominate your execution time.

Second, in 2007a at least mxGetPr returns a double *, independent of the data type you put in, so I don’t see how you can assume its a float *.

Thirdly, you can use -keep with nvmex, you just have to insert it at the right location in the nvmexopts.bat file

ie: set “COMPFLAGS=-c -arch sm_11 -keep …”

Like I said above, the reason for the “slowness” is that the GPU’s multiprocessors are not doing basically any work, it is mostly the memory copies to and from the cpu, and memory copies to and from the gpu that you are timing.