c#-矢量弱SIMD性能

我正在优化算法,正在考虑将Vector应用于double进行乘法和累加运算.最接近的实现显然是Vector.dot(v1,v2); …但是,为什么我的代码这么慢？

namespace ConsoleApp1 {
    class Program {
        public static double SIMDMultAccumulate(double[] inp1, double[] inp2) {

            var simdLength = Vector<double>.Count;
            var returnDouble = 0d;

            // Find the max and min for each of Vector<ushort>.Count sub-arrays 
            var i = 0;
            for (; i <= inp1.Length - simdLength; i += simdLength) {
                var va = new Vector<double>(inp1, i);
                var vb = new Vector<double>(inp2, i);
                returnDouble += Vector.Dot(va, vb);
            }

            // Process any remaining elements
            for (; i < inp1.Length; ++i) {
                var va = new Vector<double>(inp1, i);
                var vb = new Vector<double>(inp2, i);
                returnDouble += Vector.Dot(va, vb);
            }

            return returnDouble;
        }


        public static double NonSIMDMultAccumulate(double[] inp1, double[] inp2) {
            var returnDouble = 0d;

            for (int i = 0; i < inp1.Length; i++) {
                returnDouble += inp1[i] * inp2[i];
            }

            return returnDouble;
        }

        static void Main(string[] args) {
            Console.WriteLine("Is hardware accelerated: " + Vector.IsHardwareAccelerated);

            const int size = 24;
            var inp1 = new double[size];
            var inp2 = new double[size];

            var random = new Random();
            for (var i = 0; i < inp1.Length; i++) {
                inp1[i] = random.NextDouble();
                inp2[i] = random.NextDouble();
            }

            var sumSafe = 0d;
            var sumFast = 0d;

            var sw = Stopwatch.StartNew();
            for (var i = 0; i < 10; i++) {
                sumSafe =  NonSIMDMultAccumulate(inp1, inp2);
            }
            Console.WriteLine("{0} Ticks", sw.Elapsed.Ticks);

            sw.Restart();
            for (var i = 0; i < 10; i++) {
                sumFast = SIMDMultAccumulate(inp1, inp2);
            }
            Console.WriteLine("{0} Ticks", sw.Elapsed.Ticks);

//            Assert.AreEqual(sumSafe, sumFast, 0.00000001);
        }
    }

}

与非SIMD版本相比,SIMD版本需要大约70％的刻度.我正在运行Haswell架构和恕我直言. FMA3应该被实施！ (发布版本,首选x64).

有任何想法吗？
多谢你们！

解决方法:

使用BechmarkDotNet,假定输入数组的长度(ITEMS = 10000)是Vector.Count的倍数,使用SIMD Vector可以获得几乎两倍的性能：

    [Benchmark(Baseline = true)]
    public double DotDouble()
    {
        double returnVal = 0.0;
        for(int i = 0; i < ITEMS; i++)
        {
            returnVal += doubleArray[i] * doubleArray2[i];
        }
        return returnVal;
    }

    [Benchmark]
    public double DotDoubleVectorNaive()
    {
        double returnVal = 0.0;
        for(int i = 0; i < ITEMS; i += doubleSlots)
        {
           returnVal += Vector.Dot(new Vector<double>(doubleArray, i), new Vector<double>(doubleArray2, i));
        }
        return returnVal;  
    }

    [Benchmark]
    public double DotDoubleVectorBetter()
    {
        Vector<double> sumVect = Vector<double>.Zero;
        for (int i = 0; i < ITEMS; i += doubleSlots)
        {
            sumVect += new Vector<double>(doubleArray, i) * new Vector<double>(doubleArray2, i);
        }
        return Vector.Dot(sumVect, Vector<double>.One);
    }

    BenchmarkDotNet=v0.10.14, OS=Windows 10.0.17134
    Intel Core i7-4500U CPU 1.80GHz (Haswell), 1 CPU, 4 logical and 2 physical cores
    Frequency=1753758 Hz, Resolution=570.2041 ns, Timer=TSC
    .NET Core SDK=2.1.300
      [Host]     : .NET Core 2.1.0 (CoreCLR 4.6.26515.07, CoreFX 4.6.26515.06), 64bit RyuJIT
      DefaultJob : .NET Core 2.1.0 (CoreCLR 4.6.26515.07, CoreFX 4.6.26515.06), 64bit RyuJIT


                Method |      Mean |     Error |    StdDev | Scaled |
---------------------- |----------:|----------:|----------:|-------:|
             DotDouble | 10.341 us | 0.0902 us | 0.0844 us |   1.00 |
  DotDoubleVectorNaive |  5.907 us | 0.0206 us | 0.0183 us |   0.57 |
 DotDoubleVectorBetter |  4.825 us | 0.0197 us | 0.0184 us |   0.47 |

为了完整起见,RiuJIT将在Haswell上将Vector.Dot产品编译为：

vmulpd  ymm0,ymm0,ymm1            
vhaddpd ymm0,ymm0,ymm0    
vextractf128 xmm2,ymm0,1                
vaddpd  xmm0,xmm0,xmm2              
vaddsd  xmm6,xmm6,xmm0

编辑以根据注释添加点积在循环外的大小写,并为点积添加ASm.

标签：simd,c,system-numerics
来源： https://codeday.me/bug/20191108/2010051.html