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c#optimizationneural-networkperformance

Math optimization in C#


I've been profiling an application all day long and, having optimized a couple bits of code, I'm left with this on my todo list. It's the activation function for a neural network, which gets called over a 100 million times. According to dotTrace, it amounts to about 60% of the overall function time.

How would you optimize this?

public static float Sigmoid(double value) {
    return (float) (1.0 / (1.0 + Math.Pow(Math.E, -value)));
}

Solution

  • Try:

    public static float Sigmoid(double value) {
        return 1.0f / (1.0f + (float) Math.Exp(-value));
    }
    

    EDIT: I did a quick benchmark. On my machine, the above code is about 43% faster than your method, and this mathematically-equivalent code is the teeniest bit faster (46% faster than the original):

    public static float Sigmoid(double value) {
        float k = Math.Exp(value);
        return k / (1.0f + k);
    }
    

    EDIT 2: I'm not sure how much overhead C# functions have, but if you #include <math.h> in your source code, you should be able to use this, which uses a float-exp function. It might be a little faster.

    public static float Sigmoid(double value) {
        float k = expf((float) value);
        return k / (1.0f + k);
    }
    

    Also if you're doing millions of calls, the function-calling overhead might be a problem. Try making an inline function and see if that's any help.