dotnet C# 在不同的机器 CPU 型号上的基准性能测试

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

原文链接: http://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

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