In the tile example, each element is accessed twice from the global memory and four times from the tile_static memory. In the non-AMP and non-tile examples, each element of A and B is accessed four times from the global memory to calculate the product. For each algorithm, experimentation is required to find the optimal algorithm and tile size. Because calculating the products requires repeated access to the values in the submatrices, there is an overall performance gain. However, once the data is in tile_static memory, access to the data is much faster. As each thread executes, the tile_static variables are created for each tile appropriately and the call to tile_barrier::wait controls the program flow.Īs you examine the algorithm closely, notice that each submatrix is loaded into a tile_static memory twice. There is no indexing specifically for the tiles and the threads can execute in any order. Multiply locA and locB and add them to the results that are already in product. Copy the elements of tile of b into locB. Multiply locA and locB and put the results in product.Ĭopy the elements of tile of a into locA. It stops all of the threads in the tile until both locA and locB are filled. The call to tile_barrier::wait is essential. Therefore, you use global indices to access a, b, and product. Notice that product is tiled, not a and b.
MATRIX MULTIPLICATION CODE
The code uses these conceptual steps:Ĭopy the elements of tile of a into locA. This example is significantly different than the example without tiling. The results are available from both the product and productMatrix variables. Copy the contents of product back to the productMatrix variable. After both iterations of the loop, copy the sum to the product variable by using the global location. Now go on to the next iteration of the loop. moved ahead, the values in locA and locB would change. All threads must wait until the sums are calculated. both iterations of the loop, in effect adding the two products The threads in the tile all wait here until locA and locB are filled. For the first tile and the second loop, it copies b into locA and g into locB. For the first tile and the first loop, it copies a into locA and e into locB. Given a 4x4 matrix and a 2x2 tile size, this loop executes twice for each thread. and the entire array_view (rowGlobal, colGlobal). Get the location of the thread relative to the tile (row, col) Call parallel_for_each by using 2x2 tiles. Open MatrixMultiply.cpp and use the following code to replace the existing code. The product is calculated by multiplying the rows of A by the columns of B element by element. The product of multiplying A by B is the following 3-by-3 matrix. In this section, consider the multiplication of two matrices, A and B, which are defined as follows:Ī is a 3-by-2 matrix and B is a 2-by-3 matrix. In the Add New Item dialog box, select C++ File (.cpp), enter MatrixMultiply.cpp in the Name box, and then choose the Add button. In Solution Explorer, open the shortcut menu for Source Files, and then choose Add > New Item.
Select Empty Project, enter MatrixMultiply in the Name box, and then choose the OK button. Under Installed in the templates pane, select Visual C++. On the menu bar in Visual Studio, choose File > New > Project. * and store sum of product of elements in sum.To create a project in Visual Studio 2017 or 2015 * Multiply row of first matrix to column of second matrix
Printf("\nEnter elements in matrix B of size %dx%d: \n", SIZE, SIZE) * Input elements in second matrix from user */ Printf("Enter elements in matrix A of size %dx%d: \n", SIZE, SIZE) * Input elements in first matrix from user */ Multiplication of two matrices is defined as. Two matrices can be multiplied only and only if number of columns in the first matrix is same as number of rows in second matrix. Must know - Program to perform scalar matrix multiplication Matrix Multiplication