How can I maximize the performance of element-wise operation on an big array in C#

Question

The operation is to multiply every i-th element of a array (call it A) and i-th element of a matrix of the same size(B), and update the same i-th element of A with the value earned. In a arithmetic formula, A'[i] = A[i]*B[i] (0 < i < n(A)) What's the best way to optimize this operation in a multi-core environment? Here's my current code; ``` var learningRate = 0.001f; var m = 20000; var n = 40000; var W = float[m*n]; var C = float[m*n]; //my current code ...[1] Parallel.ForEach(Enumerable.Range(0, m), i => { for (int j = 0; j <= n - 1; j++) { W[i*n+j] *= C[i*n+j]; } }); //This is somehow far slower than [1], but I don't know why ... [2] Parallel.ForEach(Enumerable.Range(0, n*m), i => { w[i] *= C[i] }); //This is faster than [2], but not as fast as [1] ... [3] for(int i = 0; i < m*n; i++) { w[i] *= C[i] } ``` Tested the following method. But the performance didn't get better at all. http://msdn.microsoft.com/en-us/library/dd560853.aspx ``` public static void Test1() { Random rnd = new…

Accepted Answer

The problem is that while invoking a delegate is relatively fast, it adds up when you invoke it many times and the code inside the delegate is very simple.

What you could try instead is to use a `Partitioner` to specify the range you want to iterate, which allows you to iterate over many items for each delegate invocation (similar to what you're doing in [1]):

```
Parallel.ForEach(Partitioner.Create(0, n * m), partition =>
    {
        for (int i = partition.Item1; i < partition.Item2; i++)
        {
            W[i] *= C[i];
        }
    });
```

How can I maximize the performance of element-wise operation on an big array in C#

Solution

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