micro benchmark - Page 3

Java: micro-optimizing array manipulation

- by Martin Wiboe

Hello all, I am trying to make a Java port of a simple feed-forward neural network. This obviously involves lots of numeric calculations, so I am trying to optimize my central loop as much as possible. The results should be correct within the limits of the float data type. My current code looks as follows (error handling & initialization removed): /** * Simple implementation of a feedforward neural network. The network supports * including a bias neuron with a constant output of 1.0 and weighted synapses * to hidden and output layers. * * @author Martin Wiboe */ public class FeedForwardNetwork { private final int outputNeurons; // No of neurons in output layer private final int inputNeurons; // No of neurons in input layer private int largestLayerNeurons; // No of neurons in largest layer private final int numberLayers; // No of layers private final int[] neuronCounts; // Neuron count in each layer, 0 is input // layer. private final float[][][] fWeights; // Weights between neurons. // fWeight[fromLayer][fromNeuron][toNeuron] // is the weight from fromNeuron in // fromLayer to toNeuron in layer // fromLayer+1. private float[][] neuronOutput; // Temporary storage of output from previous layer public float[] compute(float[] input) { // Copy input values to input layer output for (int i = 0; i < inputNeurons; i++) { neuronOutput[0][i] = input[i]; } // Loop through layers for (int layer = 1; layer < numberLayers; layer++) { // Loop over neurons in the layer and determine weighted input sum for (int neuron = 0; neuron < neuronCounts[layer]; neuron++) { // Bias neuron is the last neuron in the previous layer int biasNeuron = neuronCounts[layer - 1]; // Get weighted input from bias neuron - output is always 1.0 float activation = 1.0F * fWeights[layer - 1][biasNeuron][neuron]; // Get weighted inputs from rest of neurons in previous layer for (int inputNeuron = 0; inputNeuron < biasNeuron; inputNeuron++) { activation += neuronOutput[layer-1][inputNeuron] * fWeights[layer - 1][inputNeuron][neuron]; } // Store neuron output for next round of computation neuronOutput[layer][neuron] = sigmoid(activation); } } // Return output from network = output from last layer float[] result = new float[outputNeurons]; for (int i = 0; i < outputNeurons; i++) result[i] = neuronOutput[numberLayers - 1][i]; return result; } private final static float sigmoid(final float input) { return (float) (1.0F / (1.0F + Math.exp(-1.0F * input))); } } I am running the JVM with the -server option, and as of now my code is between 25% and 50% slower than similar C code. What can I do to improve this situation? Thank you, Martin Wiboe

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