title: CUDA C programming with 2 video cards
body: <p>I am very new to CUDA programming and was reading the 'CUDA C Programming Guide' provided by nvidia.
   (<a href="http://developer.download.nvidia.com/compute/DevZone/docs/html/C/doc/CUDA_C_Programming_Guide.pdf">http://developer.download.nvidia.com/compute/DevZone/docs/html/C/doc/CUDA_C_Programming_Guide.pdf</a>)</p>
  
  <p>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. </p>
  
  <pre><code>// 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 = &amp;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(&amp;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(&amp;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(&amp;d_C.elements, size); 
  
      // Invoke kernel 
      dim3 dimBlock(BLOCK_SIZE, BLOCK_SIZE); 
      dim3 dimGrid(B.width / dimBlock.x, A.height / dimBlock.y); 
      MatMulKernel&lt;&lt;&lt;dimGrid, dimBlock&gt;&gt;&gt;(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 &lt; (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 &lt; 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); 
  }
  </code></pre>