System.Numerics.Vector 为 .NET Core 和 .NET Framework 带来了 SIMD 支持。它适用于 .NET Framework 4.6+ 和 .NET Core。
// Baseline
public void SimpleSumArray()
{
for (int i = 0; i < left.Length; i++)
results[i] = left[i] + right[i];
}
// Using Vector<T> for SIMD support
public void SimpleSumVectors()
{
int ceiling = left.Length / floatSlots * floatSlots;
for (int i = 0; i < ceiling; i += floatSlots)
{
Vector<float> v1 = new Vector<float>(left, i);
Vector<float> v2 = new Vector<float>(right, i);
(v1 + v2).CopyTo(results, i);
}
for (int i = ceiling; i < left.Length; i++)
{
results[i] = left[i] + right[i];
}
}
不幸的是,向量的初始化可能是限制步骤。为了解决这个问题,一些来源建议使用 MemoryMarshal 将源数组转换为向量数组 [1][2]。例如:
// Improving Vector<T> Initialization Performance
public void SimpleSumVectorsNoCopy()
{
int numVectors = left.Length / floatSlots;
int ceiling = numVectors * floatSlots;
// leftMemory is simply a ReadOnlyMemory<float> referring to the "left" array
ReadOnlySpan<Vector<float>> leftVecArray = MemoryMarshal.Cast<float, Vector<float>>(leftMemory.Span);
ReadOnlySpan<Vector<float>> rightVecArray = MemoryMarshal.Cast<float, Vector<float>>(rightMemory.Span);
Span<Vector<float>> resultsVecArray = MemoryMarshal.Cast<float, Vector<float>>(resultsMemory.Span);
for (int i = 0; i < numVectors; i++)
resultsVecArray[i] = leftVecArray[i] + rightVecArray[i];
}
在 .NET Core 上运行时,这会显着提高性能:
| Method | Mean | Error | StdDev |
|----------------------- |----------:|----------:|----------:|
| SimpleSumArray | 165.90 us | 0.1393 us | 0.1303 us |
| SimpleSumVectors | 53.69 us | 0.0473 us | 0.0443 us |
| SimpleSumVectorsNoCopy | 31.65 us | 0.1242 us | 0.1162 us |
不幸的是,在.NET Framework上,这种初始化向量的方式会产生相反的效果。它实际上会导致更差的性能:
| Method | Mean | Error | StdDev |
|----------------------- |----------:|---------:|---------:|
| SimpleSumArray | 152.92 us | 0.128 us | 0.114 us |
| SimpleSumVectors | 52.35 us | 0.041 us | 0.038 us |
| SimpleSumVectorsNoCopy | 77.50 us | 0.089 us | 0.084 us |
有没有办法在 .NET Framework 上优化 Vector 的初始化并获得与 .NET Core 相似的性能?已使用此示例应用程序 [1] 进行了测量。