本文将记录我在多个不同的机器上，在不同的 CPU 型号上，执行相同的我编写的 dotnet 的 Benchmark 的代码，测试不同的 CPU 型号对 C# 系的优化程度。本文非严谨测试，数值只有相对意义

以下是我的测试结果，对应的测试代码放在 github 上，可以在本文末尾找到下载代码的方法

我十分推荐你自己拉取代码，在你自己的设备上跑一下，测试其性能。且在开始之前，期望你已经掌握了基础的性能测试知识，避免出现诡异的结论

本文的测试将围绕着尽可能多的覆盖基础 CPU 指令以及基础逻辑行为。基础的 CPU 指令的性能测试已经有许多前辈测试过了，我这里重点测试的是各个 C# 系的上层业务行为下，所调用的多个 CPU 指令的最终性能影响。额外的也覆盖 CPU 缓存，逻辑分支命中，方法参数堆栈传递等的性能。本文的测试重点不在于 C# 系的相同功能的多个不同实现之间的性能对比，重点在于相同的代码在不同的 CPU 型号、内存、系统上的性能差异，正如此需求所述，本文非严谨测试，测试结果的数值只有相对意义

数组创建

英特尔 13th Gen Intel Core i7-13700K

以下是在我开发机上跑的，我开了几百个进程，有比较多干扰，但是问题不大，因为 i7-13700K 依然性能遥遥领先。等后续找个空闲的机器，再跑一次比较准确的性能测试


BenchmarkDotNet v0.13.12, Windows 11 (10.0.22631.3447/23H2/2023Update/SunValley3)
13th Gen Intel Core i7-13700K, 1 CPU, 24 logical and 16 physical cores
.NET SDK 8.0.204
  [Host]     : .NET 8.0.4 (8.0.424.16909), X64 RyuJIT AVX2
  Job-AXOZTJ : .NET 8.0.4 (8.0.424.16909), X64 RyuJIT AVX2

RunStrategy=Throughput

| Method | ArraySize | Mean | Error | StdDev | Median | Ratio | RatioSD | |————————- |———- |—————–:|—————:|—————:|—————–:|——:|——–:| | NewArray | 10 | 3.873 ns | 0.1146 ns | 0.2417 ns | 3.777 ns | 1.00 | 0.00 | | GCZeroInitialized | 10 | 12.234 ns | 0.2815 ns | 0.4382 ns | 12.168 ns | 3.15 | 0.21 | | GCZeroUninitialized | 10 | 4.470 ns | 0.1491 ns | 0.4056 ns | 4.354 ns | 1.14 | 0.13 | | NewArrayWithRandomVisit | 10 | 12.012 ns | 0.2679 ns | 0.2506 ns | 11.941 ns | 3.09 | 0.18 | | NewArrayWithOrdinalVisit | 10 | 9.839 ns | 0.3379 ns | 0.9803 ns | 9.635 ns | 2.58 | 0.26 | | | | | | | | | | | NewArray | 100 | 11.875 ns | 0.1932 ns | 0.2444 ns | 11.813 ns | 1.00 | 0.00 | | GCZeroInitialized | 100 | 21.980 ns | 0.4524 ns | 0.8931 ns | 21.820 ns | 1.88 | 0.08 | | GCZeroUninitialized | 100 | 12.126 ns | 0.2769 ns | 0.5201 ns | 11.953 ns | 1.04 | 0.05 | | NewArrayWithRandomVisit | 100 | 47.344 ns | 0.9635 ns | 2.1351 ns | 46.572 ns | 4.03 | 0.24 | | NewArrayWithOrdinalVisit | 100 | 75.207 ns | 1.4285 ns | 1.3363 ns | 75.364 ns | 6.33 | 0.15 | | | | | | | | | | | NewArray | 1000 | 110.197 ns | 2.1602 ns | 2.0206 ns | 109.619 ns | 1.00 | 0.00 | | GCZeroInitialized | 1000 | 116.560 ns | 2.0796 ns | 1.8435 ns | 116.604 ns | 1.06 | 0.03 | | GCZeroUninitialized | 1000 | 33.476 ns | 0.5921 ns | 0.5538 ns | 33.643 ns | 0.30 | 0.01 | | NewArrayWithRandomVisit | 1000 | 208.835 ns | 4.1962 ns | 8.8512 ns | 205.699 ns | 1.92 | 0.09 | | NewArrayWithOrdinalVisit | 1000 | 620.850 ns | 11.5406 ns | 10.7951 ns | 619.304 ns | 5.64 | 0.15 | | | | | | | | | | | NewArray | 10000 | 996.853 ns | 21.9389 ns | 61.8790 ns | 970.393 ns | 1.00 | 0.00 | | GCZeroInitialized | 10000 | 996.704 ns | 20.8764 ns | 58.5397 ns | 974.900 ns | 1.00 | 0.08 | | GCZeroUninitialized | 10000 | 63.200 ns | 1.0544 ns | 0.9863 ns | 63.315 ns | 0.06 | 0.00 | | NewArrayWithRandomVisit | 10000 | 1,242.151 ns | 24.2642 ns | 38.4856 ns | 1,233.944 ns | 1.21 | 0.07 | | NewArrayWithOrdinalVisit | 10000 | 6,068.245 ns | 90.8508 ns | 84.9819 ns | 6,076.727 ns | 5.79 | 0.34 | | | | | | | | | | | NewArray | 100000 | 7,381.046 ns | 137.9635 ns | 147.6194 ns | 7,372.520 ns | 1.00 | 0.00 | | GCZeroInitialized | 100000 | 7,214.089 ns | 85.2068 ns | 71.1515 ns | 7,209.220 ns | 0.97 | 0.02 | | GCZeroUninitialized | 100000 | 7,347.661 ns | 146.3643 ns | 174.2363 ns | 7,306.838 ns | 1.00 | 0.03 | | NewArrayWithRandomVisit | 100000 | 8,456.669 ns | 164.5726 ns | 219.6997 ns | 8,517.366 ns | 1.14 | 0.05 | | NewArrayWithOrdinalVisit | 100000 | 129,749.709 ns | 2,408.4302 ns | 2,773.5518 ns | 128,963.159 ns | 17.57 | 0.55 | | | | | | | | | | | NewArray | 1000000 | 59,752.036 ns | 1,194.7579 ns | 1,929.3113 ns | 59,414.325 ns | 1.00 | 0.00 | | GCZeroInitialized | 1000000 | 60,008.303 ns | 1,188.0164 ns | 1,778.1671 ns | 59,378.000 ns | 1.01 | 0.04 | | GCZeroUninitialized | 1000000 | 58,868.279 ns | 1,023.4279 ns | 957.3151 ns | 58,724.731 ns | 0.97 | 0.04 | | NewArrayWithRandomVisit | 1000000 | 56,399.609 ns | 1,068.5479 ns | 999.5204 ns | 56,296.948 ns | 0.93 | 0.03 | | NewArrayWithOrdinalVisit | 1000000 | 1,314,841.960 ns | 26,155.6618 ns | 27,986.2651 ns | 1,313,674.414 ns | 21.92 | 1.00 |

兆芯 ZHAOXIN KaiXian KX-U6780A


BenchmarkDotNet v0.13.12, UnionTech OS Desktop 20 E
ZHAOXIN KaiXian KX-U6780A2.7GHz (Max: 2.70GHz), 1 CPU, 8 logical and 8 physical cores
.NET SDK 8.0.204
  [Host]     : .NET 8.0.4 (8.0.424.16909), X64 RyuJIT AVX
  Job-YPUGMN : .NET 8.0.4 (8.0.424.16909), X64 RyuJIT AVX

RunStrategy=Throughput
Method ArraySize Mean Error StdDev Median Ratio RatioSD
NewArray 10 40.20 ns 0.977 ns 1.491 ns 39.98 ns 1.00 0.00
GCZeroInitialized 10 141.12 ns 2.996 ns 6.051 ns 139.67 ns 3.54 0.18
GCZeroUninitialized 10 48.72 ns 0.849 ns 0.663 ns 48.91 ns 1.19 0.05
NewArrayWithRandomVisit 10 195.75 ns 1.082 ns 0.845 ns 195.65 ns 4.77 0.16
NewArrayWithOrdinalVisit 10 72.42 ns 1.513 ns 2.400 ns 72.45 ns 1.80 0.08
               
NewArray 100 135.07 ns 2.892 ns 6.100 ns 135.41 ns 1.00 0.00
GCZeroInitialized 100 228.42 ns 4.662 ns 10.135 ns 228.83 ns 1.70 0.11
GCZeroUninitialized 100 137.26 ns 2.939 ns 5.519 ns 136.70 ns 1.02 0.06
NewArrayWithRandomVisit 100 572.02 ns 11.660 ns 19.157 ns 568.34 ns 4.26 0.27
NewArrayWithOrdinalVisit 100 467.29 ns 9.357 ns 13.117 ns 464.49 ns 3.47 0.21
               
NewArray 1000 1,037.70 ns 20.377 ns 54.742 ns 1,031.50 ns 1.00 0.00
GCZeroInitialized 1000 1,127.93 ns 22.581 ns 59.091 ns 1,125.79 ns 1.09 0.07
GCZeroUninitialized 1000 653.93 ns 6.239 ns 4.871 ns 652.04 ns 0.60 0.02
NewArrayWithRandomVisit 1000 2,375.21 ns 47.088 ns 100.349 ns 2,352.11 ns 2.27 0.13
NewArrayWithOrdinalVisit 1000 4,474.90 ns 87.887 ns 107.933 ns 4,453.16 ns 4.19 0.28
               
NewArray 10000 9,586.62 ns 189.501 ns 369.608 ns 9,657.74 ns 1.00 0.00
GCZeroInitialized 10000 9,767.26 ns 194.643 ns 462.590 ns 9,811.53 ns 1.02 0.07
GCZeroUninitialized 10000 4,093.63 ns 80.993 ns 143.965 ns 4,026.86 ns 0.43 0.02
NewArrayWithRandomVisit 10000 13,908.10 ns 202.573 ns 169.158 ns 13,928.15 ns 1.47 0.06
NewArrayWithOrdinalVisit 10000 43,057.16 ns 854.132 ns 1,495.943 ns 42,914.21 ns 4.50 0.25
               
NewArray 100000 63,542.13 ns 576.256 ns 510.836 ns 63,519.28 ns 1.00 0.00
GCZeroInitialized 100000 66,357.64 ns 1,312.089 ns 2,118.779 ns 66,043.66 ns 1.03 0.03
GCZeroUninitialized 100000 63,638.29 ns 1,241.493 ns 1,477.909 ns 63,270.73 ns 1.01 0.03
NewArrayWithRandomVisit 100000 76,609.50 ns 1,501.442 ns 1,729.063 ns 75,958.21 ns 1.21 0.03
NewArrayWithOrdinalVisit 100000 665,286.65 ns 9,295.620 ns 7,762.264 ns 662,915.19 ns 10.47 0.16
               
NewArray 1000000 461,130.99 ns 9,000.698 ns 10,004.252 ns 461,306.23 ns 1.00 0.00
GCZeroInitialized 1000000 459,810.29 ns 8,893.401 ns 10,586.961 ns 455,791.25 ns 1.00 0.03
GCZeroUninitialized 1000000 456,245.03 ns 8,819.606 ns 12,363.856 ns 452,252.89 ns 0.99 0.04
NewArrayWithRandomVisit 1000000 497,132.01 ns 9,841.562 ns 12,796.810 ns 490,990.22 ns 1.08 0.03
NewArrayWithOrdinalVisit 1000000 6,742,537.03 ns 48,986.470 ns 38,245.414 ns 6,732,321.64 ns 14.51 0.31

飞腾腾锐 Phytium D2000


BenchmarkDotNet v0.13.12, Kylin V10 SP1
Phytium,D2000/8 E8C, 8 logical cores
.NET SDK 8.0.204
  [Host]     : .NET 8.0.4 (8.0.424.16909), Arm64 RyuJIT AdvSIMD
  Job-NHRLJG : .NET 8.0.4 (8.0.424.16909), Arm64 RyuJIT AdvSIMD

RunStrategy=Throughput

| Method | ArraySize | Mean | Error | StdDev | Ratio | RatioSD | |————————- |———– |——————–:|——————:|——————-:|——:|——–:| | NewArray | 10 | 22.18 ns | 0.149 ns | 0.132 ns | 1.00 | 0.00 | | GCZeroInitialized | 10 | 92.43 ns | 0.564 ns | 0.440 ns | 4.17 | 0.02 | | GCZeroUninitialized | 10 | 25.68 ns | 0.248 ns | 0.243 ns | 1.16 | 0.01 | | NewArrayWithRandomVisit | 10 | 108.25 ns | 0.299 ns | 0.250 ns | 4.88 | 0.03 | | NewArrayWithOrdinalVisit | 10 | 34.55 ns | 0.126 ns | 0.112 ns | 1.56 | 0.01 | | | | | | | | | | NewArray | 100 | 76.35 ns | 0.941 ns | 0.880 ns | 1.00 | 0.00 | | GCZeroInitialized | 100 | 163.69 ns | 0.952 ns | 0.743 ns | 2.14 | 0.03 | | GCZeroUninitialized | 100 | 80.21 ns | 0.528 ns | 0.468 ns | 1.05 | 0.02 | | NewArrayWithRandomVisit | 100 | 421.53 ns | 1.679 ns | 1.402 ns | 5.52 | 0.06 | | NewArrayWithOrdinalVisit | 100 | 300.66 ns | 1.274 ns | 1.130 ns | 3.94 | 0.05 | | | | | | | | | | NewArray | 1000 | 640.11 ns | 4.059 ns | 3.598 ns | 1.00 | 0.00 | | GCZeroInitialized | 1000 | 672.06 ns | 3.242 ns | 3.032 ns | 1.05 | 0.01 | | GCZeroUninitialized | 1000 | 483.70 ns | 2.202 ns | 1.952 ns | 0.76 | 0.01 | | NewArrayWithRandomVisit | 1000 | 1,765.24 ns | 6.469 ns | 5.402 ns | 2.76 | 0.02 | | NewArrayWithOrdinalVisit | 1000 | 2,850.39 ns | 12.971 ns | 12.133 ns | 4.45 | 0.03 | | | | | | | | | | NewArray | 10000 | 5,219.58 ns | 36.810 ns | 32.631 ns | 1.00 | 0.00 | | GCZeroInitialized | 10000 | 5,280.52 ns | 27.550 ns | 24.422 ns | 1.01 | 0.01 | | GCZeroUninitialized | 10000 | 2,640.52 ns | 44.642 ns | 34.853 ns | 0.51 | 0.01 | | NewArrayWithRandomVisit | 10000 | 8,992.89 ns | 20.367 ns | 19.052 ns | 1.72 | 0.01 | | NewArrayWithOrdinalVisit | 10000 | 26,983.43 ns | 355.773 ns | 297.086 ns | 5.17 | 0.05 | | | | | | | | | | NewArray | 100000 | 45,506.61 ns | 431.868 ns | 403.970 ns | 1.00 | 0.00 | | GCZeroInitialized | 100000 | 45,543.14 ns | 432.449 ns | 404.513 ns | 1.00 | 0.01 | | GCZeroUninitialized | 100000 | 44,461.84 ns | 331.168 ns | 309.775 ns | 0.98 | 0.01 | | NewArrayWithRandomVisit | 100000 | 57,232.01 ns | 318.770 ns | 298.178 ns | 1.26 | 0.01 | | NewArrayWithOrdinalVisit | 100000 | 445,380.51 ns | 2,904.888 ns | 2,425.713 ns | 9.78 | 0.10 | | | | | | | | | | NewArray | 1000000 | 318,862.16 ns | 1,899.267 ns | 1,683.651 ns | 1.00 | 0.00 | | GCZeroInitialized | 1000000 | 319,510.71 ns | 4,669.274 ns | 3,645.462 ns | 1.00 | 0.01 | | GCZeroUninitialized | 1000000 | 314,884.17 ns | 5,637.859 ns | 4,401.669 ns | 0.99 | 0.02 | | NewArrayWithRandomVisit | 1000000 | 357,843.40 ns | 3,063.527 ns | 2,865.625 ns | 1.12 | 0.01 | | NewArrayWithOrdinalVisit | 1000000 | 4,547,465.54 ns | 15,355.309 ns | 12,822.379 ns | 14.28 | 0.05 | | | | | | | | | | NewArray | 1000000000 | 1,541,406,672.88 ns | 35,733,853.844 ns | 102,527,125.216 ns | 1.000 | 0.00 | | GCZeroInitialized | 1000000000 | 1,548,370,215.42 ns | 38,407,327.571 ns | 110,197,822.498 ns | 1.009 | 0.10 | | GCZeroUninitialized | 1000000000 | 1,486,735.21 ns | 28,605.254 ns | 26,757.372 ns | 0.001 | 0.00 | | NewArrayWithRandomVisit | 1000000000 | 1,590,271,119.60 ns | 33,473,585.461 ns | 96,041,991.522 ns | 1.036 | 0.09 | | NewArrayWithOrdinalVisit | 1000000000 | 3,861,833,983.54 ns | 2,367,487.064 ns | 1,976,958.923 ns | 2.546 | 0.16 |

以上的飞腾腾锐 Phytium D2000 最后的测试数据预计是不正常的

数组拷贝

测试维度

参与测试的内容如下：

  • CopyByFor ： 使用 for 循环进行拷贝数组
  • Memcpy ： 使用标准 C 提供的 memcpy 函数进行拷贝，在 linux 下使用 libc.so.6 导出函数，在 windows 下使用 msvcrt.dll 导出函数。这处于非常裸露的方式，更具体请参阅下文的数据说明内容
  • CopyBlockUnaligned ： 使用 dotnet 自带的 Unsafe.CopyBlockUnaligned 方法进行数组拷贝

英特尔 13th Gen Intel Core i7-13700K

数组较小

小于 1000 的数组时，存在较大 P/Invoke 干扰，于是决定最小设置为 1000 的值


BenchmarkDotNet v0.13.12, Windows 11 (10.0.22631.3447/23H2/2023Update/SunValley3)
13th Gen Intel Core i7-13700K, 1 CPU, 24 logical and 16 physical cores
.NET SDK 8.0.204
  [Host]     : .NET 8.0.4 (8.0.424.16909), X64 RyuJIT AVX2
  Job-GCHWHL : .NET 8.0.4 (8.0.424.16909), X64 RyuJIT AVX2

RunStrategy=Throughput
Method source dest Mean Error StdDev Ratio
CopyByFor Int32[10000] Int32[10000] 1,958.98 ns 8.391 ns 7.007 ns 1.000
Memcpy Int32[10000] Int32[10000] 609.35 ns 3.266 ns 3.055 ns 0.311
CopyBlockUnaligned Int32[10000] Int32[10000] 577.84 ns 1.391 ns 1.301 ns 0.295
CopyByFor Int32[1000] Int32[1000] 202.09 ns 0.376 ns 0.352 ns 0.103
Memcpy Int32[1000] Int32[1000] 32.21 ns 0.323 ns 0.302 ns 0.016
CopyBlockUnaligned Int32[1000] Int32[1000] 19.19 ns 0.067 ns 0.059 ns 0.010

根据上述测试数据可以看到，即使在较小数据量情况下，依然 memcpy 和 Unsafe.CopyBlockUnaligned 比 for 速度快

数组较大


BenchmarkDotNet v0.13.12, Windows 11 (10.0.22631.3447/23H2/2023Update/SunValley3)
13th Gen Intel Core i7-13700K, 1 CPU, 24 logical and 16 physical cores
.NET SDK 8.0.204
  [Host]     : .NET 8.0.4 (8.0.424.16909), X64 RyuJIT AVX2
  Job-DBDADP : .NET 8.0.4 (8.0.424.16909), X64 RyuJIT AVX2

RunStrategy=Throughput

| Method | source | dest | Mean | Error | StdDev | Median | Ratio | RatioSD | |——————- |—————– |—————– |—————–:|—————:|—————–:|—————–:|——:|——–:| | CopyByFor | Int32[100000000] | Int32[100000000] | 41,348,684.32 ns | 751,207.515 ns | 1,028,261.326 ns | 41,102,646.15 ns | 1.000 | 0.00 | | Memcpy | Int32[100000000] | Int32[100000000] | 27,086,427.67 ns | 738,121.867 ns | 2,057,588.736 ns | 26,318,143.75 ns | 0.675 | 0.05 | | CopyBlockUnaligned | Int32[100000000] | Int32[100000000] | 24,020,801.37 ns | 467,035.642 ns | 458,691.448 ns | 23,894,810.94 ns | 0.579 | 0.02 | | CopyByFor | Int32[10000000] | Int32[10000000] | 3,800,486.40 ns | 69,523.151 ns | 162,508.123 ns | 3,748,857.23 ns | 0.092 | 0.01 | | Memcpy | Int32[10000000] | Int32[10000000] | 2,313,413.90 ns | 75,362.059 ns | 208,827.911 ns | 2,248,826.17 ns | 0.058 | 0.01 | | CopyBlockUnaligned | Int32[10000000] | Int32[10000000] | 2,005,075.29 ns | 55,131.653 ns | 149,989.727 ns | 1,925,467.19 ns | 0.049 | 0.00 | | CopyByFor | Int32[1000000] | Int32[1000000] | 201,416.81 ns | 1,630.278 ns | 1,524.963 ns | 200,902.27 ns | 0.005 | 0.00 | | Memcpy | Int32[1000000] | Int32[1000000] | 104,570.31 ns | 3,304.068 ns | 9,319.184 ns | 100,412.65 ns | 0.003 | 0.00 | | CopyBlockUnaligned | Int32[1000000] | Int32[1000000] | 99,385.15 ns | 1,824.888 ns | 1,617.716 ns | 99,135.09 ns | 0.002 | 0.00 | | CopyByFor | Int32[10000] | Int32[10000] | 1,958.87 ns | 4.267 ns | 3.783 ns | 1,959.42 ns | 0.000 | 0.00 | | Memcpy | Int32[10000] | Int32[10000] | 624.06 ns | 4.451 ns | 4.164 ns | 622.60 ns | 0.000 | 0.00 | | CopyBlockUnaligned | Int32[10000] | Int32[10000] | 581.32 ns | 2.044 ns | 1.912 ns | 581.53 ns | 0.000 | 0.00 | | CopyByFor | Int32[1000] | Int32[1000] | 201.05 ns | 0.678 ns | 0.635 ns | 201.05 ns | 0.000 | 0.00 | | Memcpy | Int32[1000] | Int32[1000] | 32.12 ns | 0.638 ns | 0.683 ns | 32.10 ns | 0.000 | 0.00 | | CopyBlockUnaligned | Int32[1000] | Int32[1000] | 21.02 ns | 0.090 ns | 0.085 ns | 21.04 ns | 0.000 | 0.00 |

兆芯 ZHAOXIN KaiXian KX-U6780A

数组较小


BenchmarkDotNet v0.13.12, UnionTech OS Desktop 20 E
ZHAOXIN KaiXian KX-U6780A2.7GHz (Max: 2.70GHz), 1 CPU, 8 logical and 8 physical cores
.NET SDK 8.0.204
  [Host]     : .NET 8.0.4 (8.0.424.16909), X64 RyuJIT AVX
  Job-SBDPDU : .NET 8.0.4 (8.0.424.16909), X64 RyuJIT AVX

RunStrategy=Throughput

| Method | source | dest | Mean | Error | StdDev | Median | Ratio | RatioSD | |——————- |————- |————- |———-:|———-:|———-:|———-:|——:|——–:| | CopyByFor | Int32[10000] | Int32[10000] | 14.814 us | 0.1734 us | 0.1537 us | 14.785 us | 1.00 | 0.00 | | Memcpy | Int32[10000] | Int32[10000] | 15.329 us | 0.2950 us | 0.5167 us | 15.313 us | 1.04 | 0.04 | | CopyBlockUnaligned | Int32[10000] | Int32[10000] | 13.125 us | 0.5590 us | 1.6482 us | 13.188 us | 0.94 | 0.09 | | CopyByFor | Int32[1000] | Int32[1000] | 1.127 us | 0.0226 us | 0.0211 us | 1.127 us | 0.08 | 0.00 | | Memcpy | Int32[1000] | Int32[1000] | 2.152 us | 0.0571 us | 0.1675 us | 2.197 us | 0.13 | 0.02 | | CopyBlockUnaligned | Int32[1000] | Int32[1000] | 2.297 us | 0.0453 us | 0.0863 us | 2.279 us | 0.16 | 0.01 |

数组较大


BenchmarkDotNet v0.13.12, UnionTech OS Desktop 20 E
ZHAOXIN KaiXian KX-U6780A2.7GHz (Max: 2.70GHz), 1 CPU, 8 logical and 8 physical cores
.NET SDK 8.0.204
  [Host]     : .NET 8.0.4 (8.0.424.16909), X64 RyuJIT AVX
  Job-KKBWNV : .NET 8.0.4 (8.0.424.16909), X64 RyuJIT AVX

RunStrategy=Throughput

| Method | source | dest | Mean | Error | StdDev | Median | Ratio | RatioSD | |——————- |—————– |—————– |—————:|————–:|—————:|—————:|——:|——–:| | CopyByFor | Int32[100000000] | Int32[100000000] | 334,741.708 μs | 7,661.2780 μs | 22,469.2022 μs | 332,150.996 μs | 1.000 | 0.00 | | Memcpy | Int32[100000000] | Int32[100000000] | 164,233.004 μs | 3,256.7894 μs | 8,406.8134 μs | 161,660.880 μs | 0.493 | 0.04 | | CopyBlockUnaligned | Int32[100000000] | Int32[100000000] | 164,128.312 μs | 3,671.9104 μs | 10,826.7108 μs | 162,440.250 μs | 0.492 | 0.05 | | CopyByFor | Int32[10000000] | Int32[10000000] | 33,404.753 μs | 663.0404 μs | 1,687.6494 μs | 32,963.932 μs | 0.100 | 0.01 | | Memcpy | Int32[10000000] | Int32[10000000] | 23,405.518 μs | 1,142.2886 μs | 3,350.1346 μs | 24,879.320 μs | 0.070 | 0.01 | | CopyBlockUnaligned | Int32[10000000] | Int32[10000000] | 24,981.451 μs | 498.7301 μs | 899.3133 μs | 24,921.681 μs | 0.075 | 0.00 | | CopyByFor | Int32[1000000] | Int32[1000000] | 5,036.027 μs | 100.2153 μs | 195.4623 μs | 5,014.961 μs | 0.015 | 0.00 | | Memcpy | Int32[1000000] | Int32[1000000] | 2,585.947 μs | 51.0945 μs | 106.6533 μs | 2,601.145 μs | 0.008 | 0.00 | | CopyBlockUnaligned | Int32[1000000] | Int32[1000000] | 2,529.769 μs | 50.4126 μs | 98.3259 μs | 2,516.467 μs | 0.008 | 0.00 | | CopyByFor | Int32[10000] | Int32[10000] | 13.663 μs | 0.2509 μs | 0.2224 μs | 13.680 μs | 0.000 | 0.00 | | Memcpy | Int32[10000] | Int32[10000] | 10.112 μs | 0.1976 μs | 0.2957 μs | 10.131 μs | 0.000 | 0.00 | | CopyBlockUnaligned | Int32[10000] | Int32[10000] | 10.010 μs | 0.1742 μs | 0.1630 μs | 9.964 μs | 0.000 | 0.00 | | CopyByFor | Int32[1000] | Int32[1000] | 1.088 μs | 0.0058 μs | 0.0045 μs | 1.089 μs | 0.000 | 0.00 | | Memcpy | Int32[1000] | Int32[1000] | 1.358 μs | 0.0266 μs | 0.0364 μs | 1.355 μs | 0.000 | 0.00 | | CopyBlockUnaligned | Int32[1000] | Int32[1000] | 1.349 μs | 0.0267 μs | 0.0461 μs | 1.334 μs | 0.000 | 0.00 |

数据说明

通过数据对比 Intel 和 兆芯 以上测试数据，可以看到在 Int32[10000] 的测试数据集里面，轻松就可以看到 Intel 比 兆芯 快了 10 倍，如下图所示

在如下图的对比 Intel 和 兆芯 的对较大的数组进行拷贝的性能，可以看到 Intel 平台也的确能够比 兆芯 快出 10 倍的性能

具体的性能比较如下

方法 数组长度 Intel 兆芯 Intel比兆芯 兆芯比Intel
CopyByFor Int32[100000000] 41,348,684.32 334,741,708.00 0.1235241481 8.095583052
Memcpy Int32[100000000] 27,086,427.67 164,233,004.00 0.1649268235 6.063295094
CopyBlockUnaligned Int32[100000000] 24,020,801.37 164,128,312.00 0.1463537953 6.832757553
CopyByFor Int32[10000000] 3,800,486.40 33,404,753.00 0.1137708278 8.789599405
Memcpy Int32[10000000] 2,313,413.90 23,405,518.00 0.0988405341 10.11730672
CopyBlockUnaligned Int32[10000000] 2,005,075.29 24,981,451.00 0.0802625632 12.45910871
CopyByFor Int32[1000000] 201,416.81 5,036,027.00 0.0399951807 25.00301241
Memcpy Int32[1000000] 104,570.31 2,585,947.00 0.0404379169 24.72926589
CopyBlockUnaligned Int32[1000000] 99,385.15 2,529,769.00 0.0392862550 25.45419512
CopyByFor Int32[10000] 1,958.87 13,663.00 0.1433704165 6.974939634
Memcpy Int32[10000] 624.06 10,112.00 0.0617147943 16.20357017
CopyBlockUnaligned Int32[10000] 581.32 10,010.00 0.0580739261 17.21943164
CopyByFor Int32[1000] 201.05 1,088.00 0.1847886029 5.411589157
Memcpy Int32[1000] 32.12 1,358.00 0.0236524300 42.27895392
CopyBlockUnaligned Int32[1000] 21.02 1,349.00 0.0155819125 64.17697431

更具体的对 兆芯 的分析：在对较小的数组进行拷贝，使用 for 进行拷贝的速度比标准 C 的 memcpy 函数快，使用 for 循环进行拷贝与 dotnet 的 Unsafe.CopyBlockUnaligned 差不多。而在 Intel 平台下，无论是 标准 C 的 memcpy 还是 dotnet 的 Unsafe.CopyBlockUnaligned 都比 for 快几倍。这就意味着无论是 memcpy 还是 CopyBlockUnaligned 里面的指令优化，在 兆芯 下都是负优化

在更大的数据两情况下，可以看到 Intel 平台的 memcpy 和 CopyBlockUnaligned 对 for 循环的优化比率不断下跌，其数据情况如下

数组长度 CopyByFor Memcpy CopyBlockUnaligned CopyByFor与Memcpy比率 CopyByFor与CopyBlockUnaligned比率
1000 201.05 32.12 21.02 6.259339975 9.564700285
10000 1,958.87 624.06 581.32 3.138912925 3.369693112
1000000 201,416.81 104,570.31 99,385.15 1.926137639 2.026628827
10000000 3,800,486.40 2,313,413.90 2,005,075.29 1.642804342 1.895433263
100000000 41,348,684.32 27,086,427.67 24,020,801.37 1.52654624 1.721369894

我的猜测是随着数组长度增加，将逐渐超过了 Intel 的 CPU 的缓存，导致了比率的下降。但无论如何，使用 memcpy 和 CopyBlockUnaligned 在 Intel 下都有优化

这就是为什么在数组较大时，如在 100000000 长度时，相同的 Memcpy 方法下兆芯比Intel的耗时比例为 6.06 倍。相较于在 1000 长度时，兆芯比Intel的耗时比例为 42.27 倍小了非常多。如此可以看到其实也不能全怪兆芯，只是因为 Intel 的优化比较强，导致看起来差异比较大

在数组长度比较大的时候，在 兆芯 上也是 memcpy 会比 for 循环拷贝更快。且 memcpy 和 CopyBlockUnaligned 的性能也是基本持平的。也就是说在数据量比较大的时候，使用 dotnet 自带的 Unsafe.CopyBlockUnaligned 方法还是很有意义的，既速度快又相对安全。在数据量比较小的时候，使用 CopyBlockUnaligned 依然不会有较大的性能损失

飞腾腾锐 Phytium D2000

数组较大

BenchmarkDotNet v0.13.12, Kylin V10 SP1 Phytium,D2000/8 E8C, 8 logical cores .NET SDK 8.0.204 [Host] : .NET 8.0.4 (8.0.424.16909), Arm64 RyuJIT AdvSIMD Job-QEJWOH : .NET 8.0.4 (8.0.424.16909), Arm64 RyuJIT AdvSIMD

RunStrategy=Throughput

Method source dest Mean Error StdDev Ratio
CopyByFor Int32[100000000] Int32[100000000] 161,848,301.3 ns 275,376.77 ns 229,952.07 ns 1.000
Memcpy Int32[100000000] Int32[100000000] 139,057,784.0 ns 493,850.72 ns 437,785.80 ns 0.859
CopyBlockUnaligned Int32[100000000] Int32[100000000] 137,746,376.7 ns 740,242.45 ns 618,135.97 ns 0.851
CopyByFor Int32[10000000] Int32[10000000] 15,514,977.7 ns 33,694.59 ns 29,869.38 ns 0.096
Memcpy Int32[10000000] Int32[10000000] 14,492,865.7 ns 40,272.32 ns 35,700.37 ns 0.090
CopyBlockUnaligned Int32[10000000] Int32[10000000] 14,497,063.8 ns 38,595.84 ns 30,133.09 ns 0.090
CopyByFor Int32[1000000] Int32[1000000] 1,240,798.0 ns 15,140.32 ns 14,162.26 ns 0.008
Memcpy Int32[1000000] Int32[1000000] 1,046,522.7 ns 20,519.03 ns 19,193.52 ns 0.006
CopyBlockUnaligned Int32[1000000] Int32[1000000] 1,032,201.1 ns 19,159.44 ns 17,921.75 ns 0.006
CopyByFor Int32[10000] Int32[10000] 8,930.6 ns 9.04 ns 8.02 ns 0.000
Memcpy Int32[10000] Int32[10000] 3,058.1 ns 12.31 ns 11.51 ns 0.000
CopyBlockUnaligned Int32[10000] Int32[10000] 3,199.1 ns 16.51 ns 12.89 ns 0.000
CopyByFor Int32[1000] Int32[1000] 886.8 ns 0.66 ns 0.59 ns 0.000
Memcpy Int32[1000] Int32[1000] 250.3 ns 0.36 ns 0.30 ns 0.000
CopyBlockUnaligned Int32[1000] Int32[1000] 235.8 ns 0.29 ns 0.25 ns 0.000

数据说明和对比

飞腾腾锐 Phytium,D2000/8 E8C, 8 logical cores 的跑分不高，与 Intel 最大差距在数组拷贝上能拉到 10 倍，均值性能差距是 4 倍左右。但在我的测试里面飞腾腾锐的性能比兆芯快，大概均值性能差距是 2 倍左右，如以下对比

方法 数组长度 Intel 兆芯 飞腾腾锐 Intel比兆芯 兆芯比Intel 飞腾比Intel 兆芯比飞腾
CopyByFor Int32[100000000] 41,348,684.32 334,741,708.00 161,848,301.30 0.1235241481 8.0955830519 3.9142309837 2.0682435670
Memcpy Int32[100000000] 27,086,427.67 164,233,004.00 139,057,784.00 0.1649268235 6.0632950938 5.1338547000 1.1810414295
CopyBlockUnaligned Int32[100000000] 24,020,801.37 164,128,312.00 137,746,376.70 0.1463537953 6.8327575534 5.7344621679 1.1915254392
CopyByFor Int32[10000000] 3,800,486.40 33,404,753.00 15,514,977.70 0.1137708278 8.7895994050 4.0823663255 2.1530648413
Memcpy Int32[10000000] 2,313,413.90 23,405,518.00 14,492,865.70 0.0988405341 10.1173067215 6.2647093544 1.6149682530
CopyBlockUnaligned Int32[10000000] 2,005,075.29 24,981,451.00 14,497,063.80 0.0802625632 12.4591087051 7.2301842591 1.7232076333
CopyByFor Int32[1000000] 201,416.81 5,036,027.00 1,240,798.00 0.0399951807 25.0030124099 6.1603497742 4.0587001269
Memcpy Int32[1000000] 104,570.31 2,585,947.00 1,046,522.70 0.0404379169 24.7292658882 10.0078377888 2.4709898791
CopyBlockUnaligned Int32[1000000] 99,385.15 2,529,769.00 1,032,201.10 0.0392862550 25.4541951187 10.3858685125 2.4508489673
CopyByFor Int32[10000] 1,958.87 13,663.00 8,930.60 0.1433704165 6.9749396336 4.5590570074 1.5299084048
Memcpy Int32[10000] 624.06 10,112.00 3,058.10 0.0617147943 16.2035701695 4.9003300965 3.3066282986
CopyBlockUnaligned Int32[10000] 581.32 10,010.00 3,199.10 0.0580739261 17.2194316383 5.5031652102 3.1290050327
CopyByFor Int32[1000] 201.05 1,088.00 886.80 0.1847886029 5.4115891569 4.4108430739 1.2268831755
Memcpy Int32[1000] 32.12 1,358.00 250.30 0.0236524300 42.2789539228 7.7926525529 5.4254894127
CopyBlockUnaligned Int32[1000] 21.02 1,349.00 235.80 0.0155819125 64.1769743102 11.2178877260 5.7209499576

点的几何计算

代码和性能测试的设计

以下代码用于测试密集的计算过程中的各个设备之间的性能差异，其性能测试核心代码如下

    [Benchmark()]
    [ArgumentsSource(nameof(GetArgument))]
    public void Test(Point[] source, double[] result)
    {
        for (int i = 1; i < source.Length - 1; i++)
        {
            var a = source[i - 1];
            var b = source[i];
            var c = source[i + 1];

            var abx = b.X - a.X;
            var aby = b.Y - a.Y;

            var acx = c.X - a.X;
            var acy = c.Y - a.Y;

            var cross = abx * acy - aby * acx;
            var abs = Math.Abs(cross);

            var acl = Math.Sqrt(acx * acx + acy * acy);

            result[i] = abs / acl;
        }
    }

以上性能测试中传入的 Point[] source 为输入数据，而 double[] result 为存放的输出数据，输出数据只是为了让计算结果有的存放，让 JIT 开森而已

此性能测试中对代码逻辑的内存访问预测，即 CPU 缓存命中以及浮点计算要求较高。经过实际测试发现 Intel 在这方面的优化还是十分好的，但兆芯则有很大的优化空间

英特尔 13th Gen Intel Core i7-13700K

BenchmarkDotNet v0.13.12, Windows 11 (10.0.22631.3880/23H2/2023Update/SunValley3)
13th Gen Intel Core i7-13700K, 1 CPU, 24 logical and 16 physical cores
.NET SDK 9.0.100-preview.5.24307.3
  [Host]     : .NET 8.0.6 (8.0.624.26715), X64 RyuJIT AVX2
  Job-UGRNFG : .NET 8.0.6 (8.0.624.26715), X64 RyuJIT AVX2

RunStrategy=Throughput
Method source result Mean Error StdDev
万点 Point[10000] Double[10000] 19.622 μs 0.0914 μs 0.0810 μs
千点 Point[1000] Double[1000] 1.974 μs 0.0108 μs 0.0101 μs

兆芯 ZHAOXIN KaiXian KX-U6780A

BenchmarkDotNet v0.13.12, UnionTech OS Desktop 20 E
ZHAOXIN KaiXian KX-U6780A2.7GHz (Max: 2.70GHz), 1 CPU, 8 logical and 8 physical cores
.NET SDK 8.0.204
  [Host]     : .NET 8.0.4 (8.0.424.16909), X64 RyuJIT AVX
  Job-BBRJWB : .NET 8.0.4 (8.0.424.16909), X64 RyuJIT AVX

RunStrategy=Throughput
Method source result Mean Error StdDev Median
万点 Point[10000] Double[10000] 475.13 us 8.295 us 15.782 us 467.05 us
千点 Point[1000] Double[1000] 50.89 us 1.230 us 3.626 us 50.81 us

飞腾腾锐 Phytium D2000

BenchmarkDotNet v0.13.12, Kylin V10 SP1 Phytium,D2000/8 E8C, 8 logical cores .NET SDK 8.0.204 [Host] : .NET 8.0.4 (8.0.424.16909), Arm64 RyuJIT AdvSIMD Job-JCFXCW : .NET 8.0.4 (8.0.424.16909), Arm64 RyuJIT AdvSIMD

RunStrategy=Throughput

Method source result Mean Error StdDev
万点 Point[10000] Double[10000] 147.96 us 0.015 us 0.014 us
千点 Point[1000] Double[1000] 14.76 us 0.004 us 0.003 us

数据说明和对比

性能对比如下表，可以看到兆芯比Intel能慢上25倍左右，兆芯比飞腾慢上3倍左右

Method source result Intel 兆芯 飞腾腾锐 Intel比兆芯 兆芯比Intel 飞腾比Intel 兆芯比飞腾
万点 Point[10000] Double[10000] 19.622 475.13 147.96 0.041298171 24.21414739 7.540515748 3.211205731
千点 Point[1000] Double[1000] 1.974 50.89 14.76 0.038789546 25.78014184 7.477203647 3.447831978

通过上图可以看到，在进行基础的密集计算中，似乎兆芯做了负面优化

代码

本文代码放在 github 和 gitee 上，可以使用如下命令行拉取代码

先创建一个空文件夹，接着使用命令行 cd 命令进入此空文件夹，在命令行里面输入以下代码，即可获取到本文的代码

git init
git remote add origin https://gitee.com/lindexi/lindexi_gd.git
git pull origin 1e20b4c8ef64b17604e1ee92f41f7ac25ad08d26

以上使用的是 gitee 的源，如果 gitee 不能访问，请替换为 github 的源。请在命令行继续输入以下代码，将 gitee 源换成 github 源进行拉取代码

git remote remove origin
git remote add origin https://github.com/lindexi/lindexi_gd.git
git pull origin 1e20b4c8ef64b17604e1ee92f41f7ac25ad08d26

获取代码之后，进入 BulowukaileFeanayjairwo 文件夹，即可获取到源代码

特别感谢

特别感谢 https://github.com/mjebrahimi/Performance-Wars-Benchmarking-CSharp 提供的代码

参考文档

C# 标准性能测试

C# 标准性能测试高级用法

dotnet 6 数组拷贝性能对比

跑分系列

ARM Phytium,D2000/8 E8C 8 Core 2300 MHz

D2000高效能桌面CPU 跑分：https://www.cpubenchmark.net/cpu.php?cpu=ARM+Phytium%2CD2000%2F8+E8C+8+Core+2300+MHz&id=4862

和 Intel i7-13700K 对比：https://www.cpubenchmark.net/compare/4862vs5060/ARM-Phytium,D20008-E8C-8-Core-2300-MHz-vs-Intel-i7-13700K

另一个和 Intel i7-13700K 对比：https://openbenchmarking.org/vs/Processor/Phytium+D2000,Intel+Core+i7-13700K

本文会经常更新，请阅读原文： https://blog.lindexi.com/post/dotnet-C-%E5%9C%A8%E4%B8%8D%E5%90%8C%E7%9A%84%E6%9C%BA%E5%99%A8-CPU-%E5%9E%8B%E5%8F%B7%E4%B8%8A%E7%9A%84%E5%9F%BA%E5%87%86%E6%80%A7%E8%83%BD%E6%B5%8B%E8%AF%95.html ，以避免陈旧错误知识的误导，同时有更好的阅读体验。

如果你想持续阅读我的最新博客，请点击 RSS 订阅，推荐使用RSS Stalker订阅博客，或者收藏我的博客导航

知识共享许可协议 本作品采用 知识共享署名-非商业性使用-相同方式共享 4.0 国际许可协议 进行许可。欢迎转载、使用、重新发布，但务必保留文章署名林德熙（包含链接： https://blog.lindexi.com ），不得用于商业目的，基于本文修改后的作品务必以相同的许可发布。如有任何疑问，请 与我联系 。

微软最具价值专家

无盈利，不卖课，做纯粹的技术博客

以下是广告时间

推荐关注 Edi.Wang 的公众号

欢迎进入 Eleven 老师组建的 .NET 社区

以上广告全是友情推广，无盈利

相关文章