.NET数学计算性能 [英] .NET math calculation performances

问题描述

我问了一个有关将Excel的BetaInv函数移植到.NET的问题: SQL中的BetaInv函数服务器

I asked a question about having the Excel's BetaInv function ported to .NET: BetaInv function in SQL Server

现在，我设法用纯依赖性更少的C#代码编写了该函数，并且在逗号后最多6或7位数字的情况下，我的确获得了与MS Excel中相同的结果，结果对我们来说很好，问题在于此类代码已嵌入在SQL CLR函数中，它从存储过程中被调用了数千次，并使整个过程的执行速度降低了大约50％，从30秒到一分钟(如果我不使用该函数，则一分钟).

now I managed to write that function in pure dependency less C# code and I do get the same results than in MS Excel up to 6 or 7 digits after comma, results are fine for us, the problem is that such code is embedded in a SQL CLR Function and gets called thousands of time from a stored procedure and makes the execution of the whole procedure about 50% slower, from 30 seconds up to a minute if I use that function or not.

这是其中的一些代码，我并没有要求进行深入的分析，但是是否有人在进行此计算时看到任何主要的性能问题?例如使用其他数据类型，而不是使用double或其他类型...?

here some code of it, I am not asking a deep analysis but is there anybody who sees any major performance issue in the way I am doing this calculations? like for example usage of other data types instead of doubles or whatsoever... ?

private static double betacf(double a, double b, double x)
        {
            int m, m2;
            double aa, c, d, del, h, qab, qam, qap;

            qab = a + b;
            qap = a + 1.0;
            qam = a - 1.0;

            c = 1.0; // First step of Lentz’s method.

            d = 1.0 - qab * x / qap;

            if (System.Math.Abs(d) < FPMIN)
            {
                d = FPMIN;
            }

            d = 1.0 / d;
            h = d;

            for (m = 1; m <= MAXIT; ++m)
            {
                m2 = 2 * m;
                aa = m * (b - m) * x / ((qam + m2) * (a + m2));
                d = 1.0 + aa * d; //One step (the even one) of the recurrence.

                if (System.Math.Abs(d) < FPMIN)
                {
                    d = FPMIN;
                }

                c = 1.0 + aa / c;

                if (System.Math.Abs(c) < FPMIN)
                {
                    c = FPMIN;
                }

                d = 1.0 / d;
                h *= d * c;

                aa = -(a + m) * (qab + m) * x / ((a + m2) * (qap + m2));
                d = 1.0 + aa * d; // Next step of the recurrence (the odd one).

                if (System.Math.Abs(d) < FPMIN)
                {
                    d = FPMIN;
                }

                c = 1.0 + aa / c;

                if (System.Math.Abs(c) < FPMIN)
                {
                    c = FPMIN;
                }

                d = 1.0 / d;
                del = d * c;
                h *= del;

                if (System.Math.Abs(del - 1.0) < EPS)
                {
                    // Are we done?
                    break;
                }
            }

            if (m > MAXIT)
            {
                return 0;
            }
            else
            {
                return h;
            }
        }

        private static double gammln(double xx)
        {
            double x, y, tmp, ser;

            double[] cof = new double[] { 76.180091729471457, -86.505320329416776, 24.014098240830911, -1.231739572450155, 0.001208650973866179, -0.000005395239384953 };

            y = xx;
            x = xx;
            tmp = x + 5.5;
            tmp -= (x + 0.5) * System.Math.Log(tmp);

            ser = 1.0000000001900149;

            for (int j = 0; j <= 5; ++j)
            {
                y += 1;
                ser += cof[j] / y;
            }

            return -tmp + System.Math.Log(2.5066282746310007 * ser / x);
        }

推荐答案

对我来说唯一突出的一点是，通常是对性能的影响，是内存分配.我不知道 gammln 的调用频率，但是您可能希望将 double [] cof = new double [] {} 移至静态的一次性分配.

The only thing that stands out for me, and is usually a performance hit, is memory allocation. I don't know how often gammln is called but you might want to move the double[] cof = new double[] {} to a static one time only allocation.

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