CUDA C programming with 2 video cards
===

I am very new to CUDA programming and was reading the 'CUDA C Programming Guide' provided by nvidia.
 ([http://developer.download.nvidia.com/compute/DevZone/docs/html/C/doc/CUDA_C_Programming_Guide.pdf][1])

In the page 25, it has the following C code that does the matrix multiplication. Can you please tell me how can I make that code run on two devices? (if I have two nvida CUDA capable cards installed in my computer). Can you please show me with an example. 

    // Matrices are stored in row-major order: 
    // M(row, col) = *(M.elements + row * M.stride + col) 
    typedef struct { 
    	int width; 
    	int height; 
    	int stride; 
    	float* elements; 
    } Matrix; 
    
    // Get a matrix element 
    __device__ float GetElement(const Matrix A, int row, int col) 
    { 
    	return A.elements[row * A.stride + col]; 
    } 
    
    // Set a matrix element 
    __device__ void SetElement(Matrix A, int row, int col, float value) 
    { 
    	A.elements[row * A.stride + col] = value; 
    } 
    
    // Get the BLOCK_SIZExBLOCK_SIZE sub-matrix Asub of A that is 
    // located col sub-matrices to the right and row sub-matrices down 
    // from the upper-left corner of A 
    __device__ Matrix GetSubMatrix(Matrix A, int row, int col) 
    { 
    	Matrix Asub; 
    	Asub.width = BLOCK_SIZE; 
    	Asub.height = BLOCK_SIZE; 
    	Asub.stride = A.stride; 
    	Asub.elements = &A.elements[A.stride * BLOCK_SIZE * row + BLOCK_SIZE * col]; 
    	return Asub;
    	} 
    
    // Thread block size 
    #define BLOCK_SIZE 16 
    
    // Forward declaration of the matrix multiplication kernel 
    __global__ void MatMulKernel(const Matrix, const Matrix, Matrix); 
    
    // Matrix multiplication - Host code
    // Matrix dimensions are assumed to be multiples of BLOCK_SIZE 
    void MatMul(const Matrix A, const Matrix B, Matrix C) 
    { 
    	// Load A and B to device memory 
    	Matrix d_A; 
    	d_A.width = d_A.stride = A.width; d_A.height = A.height; 
    	size_t size = A.width * A.height * sizeof(float); 
    	cudaMalloc(&d_A.elements, size); 
    	cudaMemcpy(d_A.elements, A.elements, size, cudaMemcpyHostToDevice); 
    	Matrix d_B; 
    	d_B.width = d_B.stride = B.width; d_B.height = B.height; 
    	size = B.width * B.height * sizeof(float); 
    	cudaMalloc(&d_B.elements, size); 
    	cudaMemcpy(d_B.elements, B.elements, size, cudaMemcpyHostToDevice); 
    	
    	// Allocate C in device memory 
    	Matrix d_C; 
    	d_C.width = d_C.stride = C.width; d_C.height = C.height; 
    	size = C.width * C.height * sizeof(float); 
    	cudaMalloc(&d_C.elements, size); 
    	
    	// Invoke kernel 
    	dim3 dimBlock(BLOCK_SIZE, BLOCK_SIZE); 
    	dim3 dimGrid(B.width / dimBlock.x, A.height / dimBlock.y); 
    	MatMulKernel<<<dimGrid, dimBlock>>>(d_A, d_B, d_C); 
    	
    	// Read C from device memory 
    	cudaMemcpy(C.elements, d_C.elements, size, cudaMemcpyDeviceToHost); 
    	
    	// Free device memory 
    	cudaFree(d_A.elements); 
    	cudaFree(d_B.elements); 
    	cudaFree(d_C.elements); 
    } 
    
    // Matrix multiplication kernel called by MatMul() 
    __global__ void MatMulKernel(Matrix A, Matrix B, Matrix C) 
    { 
    	// Block row and column 
    	int blockRow = blockIdx.y; 
    	int blockCol = blockIdx.x; 
    	
    	// Each thread block computes one sub-matrix Csub of C 
    	Matrix Csub = GetSubMatrix(C, blockRow, blockCol);
    
    	// Each thread computes one element of Csub 
    	// by accumulating results into Cvalue 
    	float Cvalue = 0; 
    	
    	// Thread row and column within Csub 
    	int row = threadIdx.y; 
    	int col = threadIdx.x; 
    	
    	// Loop over all the sub-matrices of A and B that are 
    	// required to compute Csub 
    	// Multiply each pair of sub-matrices together 
    	// and accumulate the results 
    	for (int m = 0; m < (A.width / BLOCK_SIZE); ++m) 
    	{ 
    		// Get sub-matrix Asub of A 
    		Matrix Asub = GetSubMatrix(A, blockRow, m); 
    		// Get sub-matrix Bsub of B 
    		Matrix Bsub = GetSubMatrix(B, m, blockCol); 
    		
    		// Shared memory used to store Asub and Bsub respectively 
    		__shared__ float As[BLOCK_SIZE][BLOCK_SIZE]; 
    		__shared__ float Bs[BLOCK_SIZE][BLOCK_SIZE]; 
    		
    		// Load Asub and Bsub from device memory to shared memory 
    		// Each thread loads one element of each sub-matrix 
    		As[row][col] = GetElement(Asub, row, col); 
    		Bs[row][col] = GetElement(Bsub, row, col); 
    		
    		// Synchronize to make sure the sub-matrices are loaded 
    		// before starting the computation 
    		__syncthreads(); 
    		
    		// Multiply Asub and Bsub together 
    		for (int e = 0; e < BLOCK_SIZE; ++e) 
    			Cvalue += As[row][e] * Bs[e][col]; 
    		
    		// Synchronize to make sure that the preceding 
    		// computation is done before loading two new 
    		// sub-matrices of A and B in the next iteration 
    		__syncthreads(); 
    	} 
    	
    	// Write Csub to device memory 
    	// Each thread writes one element 
    	SetElement(Csub, row, col, Cvalue); 
    }

  [1]: http://developer.download.nvidia.com/compute/DevZone/docs/html/C/doc/CUDA_C_Programming_Guide.pdf
