提升精神因果报应真正的发电机性能

Question

当我在检查提升精神业力产生者的表现时，当我对实数使用策略时，我有点惊讶于性能下降。在柯尔鲁上直播

代码取自提振精神，并添加了几个测试函数。Coliru示例替换了所使用的计时器。注意，Coliru会中止长时间运行的程序，因此它可能不会结束所有测试。

可以看到，策略使用会降低性能2-3 (coliru上的x10)次数。这是预期的行为吗？

我的数字：

冲刺: 0.367 碘流: 0.818 格式: 1.036 业力: 0.087 (字符串)：0.152 因果报应(字符串)与政策: 0.396 业力(规则)：0.12 业力(直接)：0.083 卡玛(直接)字符串: 0.089 带政策的karma (直接)字符串: 0.278

用x64 VC14建造的

Answer 1

如果你比较苹果和梨，那就不是回归了。在这种情况下，两次。

第一对苹果梨

这里fixed是苹果，scientific是梨。

不仅结果输出明显不同，而且到达结果需要不同的步骤。

重要的是，scientific涉及到输入值的log10，以便在小数点之前以基数10位确定数字的大小：

​

​

默认情况下，real_policies称之为“廉价”判决：

    static int floatfield(T n)
    {
        if (traits::test_zero(n))
            return fmtflags::fixed;

        T abs_n = traits::get_absolute_value(n);
        return (abs_n >= 1e5 || abs_n < 1e-3) 
          ? fmtflags::scientific : fmtflags::fixed;
    }

因此，如果您选择一种将切换到科学方式的格式，您可以看到差异消失：123456.123456而不是12345.12345.：

clock resolution: mean is 16.9199 ns (40960002 iterations)

benchmarking format_performance_direct_string
collecting 100 samples, 1 iterations each, in estimated 4.7784 ms
mean: 238.81 ns, lb 187.22 ns, ub 493.46 ns, ci 0.95
std dev: 507.559 ns, lb 5.36317 ns, ub 1111.94 ns, ci 0.95
found 11 outliers among 100 samples (11%)
variance is severely inflated by outliers

benchmarking format_performance_direct_string_with_policy
collecting 100 samples, 96 iterations each, in estimated 1699.2 μs
mean: 173.927 ns, lb 172.764 ns, ub 176.939 ns, ci 0.95
std dev: 8.33706 ns, lb 0.256875 ns, ub 16.9312 ns, ci 0.95
found 2 outliers among 100 samples (2%)
variance is moderately inflated by outliers

benchmarking format_performance_string
collecting 100 samples, 84 iterations each, in estimated 1705.2 μs
mean: 312.646 ns, lb 311.027 ns, ub 314.819 ns, ci 0.95
std dev: 9.42479 ns, lb 7.32668 ns, ub 15.2546 ns, ci 0.95
found 1 outliers among 100 samples (1%)
variance is moderately inflated by outliers

benchmarking format_performance_string_with_policy
collecting 100 samples, 31 iterations each, in estimated 1736 μs
mean: 193.572 ns, lb 192.257 ns, ub 200.032 ns, ci 0.95
std dev: 12.8586 ns, lb 0.322008 ns, ub 30.6708 ns, ci 0.95
found 4 outliers among 100 samples (4%)
variance is severely inflated by outliers

如您所见，自定义策略是(可预测的)更快的

As 交互式链接

​

​

第二对苹果/梨

当您将精度固定在15位数字时，就会出现这种情况。

通过使用两种策略的单独的头对头基准，这两种策略实际上是精确的：http://paste.ubuntu.com/13087371/，您可以看到，这不仅失去了将格式固定到上面看到的scientific的好处：

clock resolution: mean is 18.6041 ns (40960002 iterations)

benchmarking format_performance_direct_string_with_policy
collecting 100 samples, 1 iterations each, in estimated 1892.9 μs
mean: 228.83 ns, lb 179.9 ns, ub 471.84 ns, ci 0.95
std dev: 483.67 ns, lb 2.29965 ns, ub 1153.98 ns, ci 0.95
found 14 outliers among 100 samples (14%)
variance is severely inflated by outliers

benchmarking format_performance_direct_string_with_policy15
collecting 100 samples, 45 iterations each, in estimated 1858.5 μs
mean: 418.697 ns, lb 410.976 ns, ub 438.865 ns, ci 0.95
std dev: 58.0984 ns, lb 24.1313 ns, ub 115.549 ns, ci 0.95
found 6 outliers among 100 samples (6%)
variance is severely inflated by outliers

benchmarking format_performance_string_with_policy
collecting 100 samples, 87 iterations each, in estimated 1870.5 μs
mean: 262.057 ns, lb 254.73 ns, ub 269.354 ns, ci 0.95
std dev: 37.2502 ns, lb 31.1261 ns, ub 50.5813 ns, ci 0.95
found 17 outliers among 100 samples (17%)
variance is severely inflated by outliers

benchmarking format_performance_string_with_policy15
collecting 100 samples, 42 iterations each, in estimated 1898.4 μs
mean: 458.505 ns, lb 453.626 ns, ub 481.044 ns, ci 0.95
std dev: 45.5401 ns, lb 4.30147 ns, ub 108.045 ns, ci 0.95
found 4 outliers among 100 samples (4%)
variance is severely inflated by outliers

或在图中：交互式链接

​

​