Java: micro-optimizing array manipulation

Java: micro-optimizing array manipulation

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

Edit #1: After seeing the vast amount of responses, I should probably clarify the numbers in our scenario. During a typical run, the method will be called about 50.000 times with different inputs. A typical network would have numberLayers = 3 layers with 190, 2 and 1 neuron, respectively. The innermost loop will therefore have about 2*191+3=385 iterations (when counting the added bias neuron in layers 0 and 1)

Edit #1: After implementing the various suggestions in this thread, our implementation is practically as fast as the C version (within ~2 %). Thanks for all the help! All of the suggestions have been helpful, but since I can only mark one answer as the correct one, I will give it to @Durandal for both suggesting array optimizations and being the only one to precalculate the for loop header.

Answer by nivekastoreth for Java: micro-optimizing array manipulation

First thing I would look into is seeing if Math.exp is slowing you down. See this post on a Math.exp approximation for a native alternative.

Answer by SyntaxT3rr0r for Java: micro-optimizing array manipulation

For a start, don't do this:

// Copy input values to input layer output  for (int i = 0; i < inputNeurons; i++) {      neuronOutput[0][i] = input[i];  }  

But this:

System.arraycopy( input, 0, neuronOutput[0], 0, inputNeurons );  

Answer by sizzzzlerz for Java: micro-optimizing array manipulation

Purely based upon code inspection, your inner most loop has to compute references to a three-dimensional parameter and its being done a lot. Depending upon your array dimensions could you possibly be having cache issues due to have to jump around memory with each loop iteration. Maybe you could rearrange the dimensions so the inner loop tries to access memory elements that are closer to one another than they are now?

In any case, profile your code before making any changes and see where the real bottleneck is.

Answer by Daniel for Java: micro-optimizing array manipulation

I suggest using a fixed point system rather than a floating point system. On almost all processors using int is faster than float. The simplest way to do this is simply shift everything left by a certain amount (4 or 5 are good starting points) and treat the bottom 4 bits as the decimal.

Your innermost loop is doing floating point maths so this may give you quite a boost.

Answer by Jim Ferrans for Java: micro-optimizing array manipulation

The key to optimization is to first measure where the time is spent. Surround various parts of your algorithm with calls to System.nanoTime():

long start_time = System.nanoTime();  doStuff();  long time_taken = System.nanoTime() - start_time;  

I'd guess that while using System.arraycopy() would help a bit, you'll find your real costs in the inner loop.

Depending on what you find, you might consider replacing the float arithmetic with integer arithmetic.

Answer by Peter Lawrey for Java: micro-optimizing array manipulation

Some tips.

• in your inner most loop, think about how you are traversing your CPU cache and re-arrange your matrix so you are accessing the outer most array sequentially. This will result in you accessing your cache in order rather than jumping all over the place. A cache hit can be two orders of magniture faster than a cache miss. e.g restructure fWeights so it is accessed as

activation += neuronOutput[layer-1][inputNeuron] * fWeights[layer - 1][neuron][inputNeuron];

• don't perform work inside the loop (every time) which can be done outside the loop (once). Don't perform the [layer -1] lookup every time when you can place this in a local variable. Your IDE should be able to refactor this easily.

• multi-dimensional arrays in Java are not as efficient as they are in C. They are actually multiple layers of single dimensional arrays. You can restructure the code so you're only using a single dimensional array.

• don't return a new array when you can pass the result array as an argument. (Saves creating a new object on each call).

• rather than peforming layer-1 all over the place, why not use layer1 as layer-1 and using layer1+1 instead of layer.

Answer by Durandal for Java: micro-optimizing array manipulation

Disregarding the actual math, the array indexing in Java can be a performance hog in itself. Consider that Java has no real multidimensional arrays, but rather implements them as array of arrays. In your innermost loop, you access over multiple indices, some of which are in fact constant in that loop. Part of the array access can be move outside of the loop:

final int[] neuronOutputSlice = neuronOutput[layer - 1];  final int[][] fWeightSlice = fWeights[layer - 1];  for (int inputNeuron = 0; inputNeuron < biasNeuron; inputNeuron++) {      activation += neuronOutputSlice[inputNeuron] * fWeightsSlice[inputNeuron][neuron];  }  

It is possible that the server JIT performs a similar code invariant movement, the only way to find out is change and profile it. On the client JIT this should improve performance no matter what. Another thing you can try is to precalculate the for-loop exit conditions, like this:

for (int neuron = 0; neuron < neuronCounts[layer]; neuron++) { ... }  // transform to precalculated exit condition (move invariant array access outside loop)  for (int neuron = 0, neuronCount = neuronCounts[layer]; neuron < neuronCount; neuron++) { ... }  

Again the JIT may already do this for you, so profile if it helps.

Is there a point to multiplying with 1.0F that eludes me here?:

float activation = 1.0F * fWeights[layer - 1][biasNeuron][neuron];  

Other things that could potentially improve speed at cost of readability: inline sigmoid() function manually (the JIT has a very tight limit for inlining and the function might be larger). It can be slightly faster to run a loop backwards (where it doesnt change the outcome of course), since testing the loop index against zero is a little cheaper than checking against a local variable (the innermost loop is a potentical candidate again, but dont expect the output to be 100% identical in all cases, since adding floats a + b + c is potentially not the same as a + c + b).

Answer by Dominic Cerisano for Java: micro-optimizing array manipulation

Replace the expensive floating point sigmoid transfer function with an integer step transfer function.

The sigmoid transfer function is a model of organic analog synaptic learning, which in turn seems to be a model of a step function.

The historical precedent for this is that Hinton designed the back-prop algorithm directly from the first principles of cognitive science theories about real synapses, which in turn were based on real analog measurements, which turn out to be sigmoid.

But the sigmoid transfer function seems to be an organic model of the digital step function, which of course cannot be directly implemented organically.

Rather than model a model, replace the expensive floating point implementation of the organic sigmoid transfer function with the direct digital implementation of a step function (less than zero = -1, greater than zero = +1).

The brain cannot do this, but backprop can!

This not only linearly and drastically improves performance of a single learning iteration, it also reduces the number of learning iterations required to train the network: supporting evidence that learning is inherently digital.

Also supports the argument that Computer Science is inherently cool.

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