Java: Data Science Made Easy

Using Aparapi for matrix multiplication

We will use the matrices as used in the Implementing basic matrix operations section. We start with the declaration of the MatrixMultiplicationKernel class, which contains the vector declarations, a constructor, the run method, and a displayResults method. The vectors for matrices A and B have been flattened to one-dimensional arrays by allocating the matrices in row-column order:

class MatrixMultiplicationKernel extends Kernel {     float[] vectorA = {         0.1950f, 0.0311f, 0.3588f,          0.2203f, 0.1716f, 0.5931f,          0.2105f, 0.3242f};     float[] vectorB = {         0.0502f, 0.9823f, 0.9472f,          0.5732f, 0.2694f, 0.916f};     float[] vectorC;     int n;     int m;     int p;     @Override     public void run() {         ...     }  public MatrixMultiplicationKernel(int ...