硬件内在函数

AVX-512支持：SIMD的终极形态

// 优化的数值计算

// 优化前的代码

public double[] ProcessData(double[] input)
{var result = new double[input.Length];for (int i = 0; i < input.Length; i++){result[i] = Math.Sin(input[i]) * Math.Cos(input[i]);}return result;
}// 优化后的代码：使用硬件内在函数
public unsafe double[] ProcessDataOptimized(double[] input)
{var result = new double[input.Length];fixed (double* pInput = input, pResult = result){int i = 0;if (Avx512F.IsSupported){var size = Vector512<double>.Count;for (; i <= input.Length - size; i += size){var vec = Avx512F.LoadVector512(pInput + i);var sinVec = Avx512F.Sin(vec);var cosVec = Avx512F.Cos(vec);var product = Avx512F.Multiply(sinVec, cosVec);Avx512F.Store(pResult + i, product);}}// 处理剩余元素for (; i < input.Length; i++){pResult[i] = Math.Sin(pInput[i]) * Math.Cos(pInput[i]);}}return result;
}//使用AVX-512进行高性能矩阵运算
using System.Runtime.Intrinsics;
using System.Runtime.Intrinsics.X86;public unsafe void MatrixMultiply(float* left, float* right, float* result, int size)
{for (int i = 0; i < size; i++){for (int j = 0; j < size; j += 16) // 一次处理16个单精度浮点数
        {// 加载16个float到AVX-512寄存器var vecLeft = Avx512F.LoadVector512(left + i * size + j);var vecRight = Avx512F.LoadVector512(right + j * size);// 执行向量乘法var product = Avx512F.Multiply(vecLeft, vecRight);// 存储结果Avx512F.Store(result + i * size + j, product);}}
}

 

public void OptimizedMethod()
{if (Avx512F.IsSupported){// 使用AVX-512优化
        ProcessWithAvx512();}else if (Avx2.IsSupported){// 使用AVX2优化
        ProcessWithAvx2();}else if (Sse42.IsSupported){// 使用SSE4.2优化
        ProcessWithSse42();}else{// 回退到标量实现
        ProcessScalar();}
}