标签：performance micro-optimization c net expression-trees
为什么在Expression.Call上编译的lambda构建比应该执行相同操作的委托慢一些？以及如何避免呢？
解释BenchmarkDotNet结果.
我们正在比较CallBuildedReal和CallLambda；其他两个CallBuilded和CallLambdaConst是CallLambda的“子表单”,并显示相等的数字.但是与CallBuildedReal的区别是很重要的.
//[Config(typeof(Config))]
[RankColumn, MinColumn, MaxColumn, StdDevColumn, MedianColumn]
[ClrJob , CoreJob]
[HtmlExporter, MarkdownExporter]
[MemoryDiagnoser /*, InliningDiagnoser*/]
public class BenchmarkCallSimple
{
static Func<StringBuilder, int, int, bool> callLambda;
static Func<StringBuilder, int, int, bool> callLambdaConst;
static Func<StringBuilder, int, int, bool> callBuilded;
static Func<StringBuilder, int, int, bool> callBuildedReal;
private static bool Append<T>(StringBuilder sb, T i1, T i2, Func<T, T, T> operation)
{
sb.Append(operation(i1, i2));
return true;
}
private static Func<StringBuilder, T, T, bool> BuildCallMethod<T>(Func<T, T, T> operation)
{
return (sb, i1, i2)=> { sb.Append(operation(i1, i2)); return true; };
}
private static int AddMethod(int a, int b)
{
return a + b;
}
static BenchmarkCallSimple()
{
var x = Expression.Parameter(typeof(int));
var y = Expression.Parameter(typeof(int));
var additionExpr = Expression.Add(x, y);
callLambdaConst = BuildCallMethod<int>(AddMethod);
callLambda = BuildCallMethod<int>((a, b) => a + b);
var operationDelegate = Expression.Lambda<Func<int, int, int>>(additionExpr, x, y).Compile();
callBuilded = BuildCallMethod(operationDelegate);
var operationExpressionConst = Expression.Constant(operationDelegate, operationDelegate.GetType());
var sb1 = Expression.Parameter(typeof(StringBuilder), "sb");
var i1 = Expression.Parameter(typeof(int), "i1");
var i2 = Expression.Parameter(typeof(int), "i2");
var appendMethodInfo = typeof(BenchmarkCallSimple).GetTypeInfo().GetDeclaredMethod(nameof(BenchmarkCallSimple.Append));
var appendMethodInfoGeneric = appendMethodInfo.MakeGenericMethod(typeof(int));
var appendCallExpression = Expression.Call(appendMethodInfoGeneric,
new Expression[] { sb1, i1, i2, operationExpressionConst }
);
var appendLambda = Expression.Lambda(appendCallExpression, new[] { sb1, i1, i2 });
callBuildedReal = (Func<StringBuilder, int, int, bool>)(appendLambda.Compile());
}
[Benchmark]
public string CallBuildedReal()
{
StringBuilder sb = new StringBuilder();
var b = callBuildedReal(sb, 1, 2);
return sb.ToString();
}
[Benchmark]
public string CallBuilded()
{
StringBuilder sb = new StringBuilder();
var b = callBuilded(sb, 1, 2);
return sb.ToString();
}
[Benchmark]
public string CallLambda()
{
StringBuilder sb = new StringBuilder();
var b = callLambda(sb, 1, 2);
return sb.ToString();
}
[Benchmark]
public string CallLambdaConst()
{
StringBuilder sb = new StringBuilder();
var b = callLambdaConst(sb, 1, 2);
return sb.ToString();
}
}
结果：
BenchmarkDotNet=v0.10.5, OS=Windows 10.0.14393
Processor=Intel Core i5-2500K CPU 3.30GHz (Sandy Bridge), ProcessorCount=4
Frequency=3233539 Hz, Resolution=309.2587 ns, Timer=TSC
[Host] : Clr 4.0.30319.42000, 64bit RyuJIT-v4.6.1648.0
Clr : Clr 4.0.30319.42000, 64bit RyuJIT-v4.6.1648.0
Core : .NET Core 4.6.25009.03, 64bit RyuJIT
Method | Job | Runtime | Mean | Error | StdDev | Min | Max | Median | Rank | Gen 0 | Allocated |
---------------- |----- |-------- |---------:|---------:|---------:|---------:|---------:|---------:|-----:|-------:|----------:|
CallBuildedReal | Clr | Clr | 137.8 ns | 2.903 ns | 4.255 ns | 133.6 ns | 149.6 ns | 135.6 ns | 7 | 0.0580 | 192 B |
CallBuilded | Clr | Clr | 122.7 ns | 2.068 ns | 1.934 ns | 118.5 ns | 126.2 ns | 122.6 ns | 6 | 0.0576 | 192 B |
CallLambda | Clr | Clr | 119.8 ns | 1.342 ns | 1.255 ns | 117.9 ns | 121.7 ns | 119.6 ns | 5 | 0.0576 | 192 B |
CallLambdaConst | Clr | Clr | 121.7 ns | 1.347 ns | 1.194 ns | 120.1 ns | 124.5 ns | 121.6 ns | 6 | 0.0571 | 192 B |
CallBuildedReal | Core | Core | 114.8 ns | 2.263 ns | 2.117 ns | 112.7 ns | 118.8 ns | 113.7 ns | 3 | 0.0594 | 191 B |
CallBuilded | Core | Core | 109.0 ns | 1.701 ns | 1.591 ns | 106.5 ns | 112.2 ns | 108.8 ns | 2 | 0.0599 | 191 B |
CallLambda | Core | Core | 107.0 ns | 1.181 ns | 1.105 ns | 105.7 ns | 109.4 ns | 106.8 ns | 1 | 0.0593 | 191 B |
CallLambdaConst | Core | Core | 117.3 ns | 2.706 ns | 3.704 ns | 113.4 ns | 127.8 ns | 116.0 ns | 4 | 0.0592 | 191 B |
基准代码：
注意1：有类似的SO线程“ Performance of expression trees”,其中构建表达式在基准测试中显示出最佳结果.
注意2：当我将获得编译表达式的IL代码时,我应该接近回答,所以我正在尝试学习如何获取编译表达式的IL代码(linqpad ?、 ilasm集成到VS ?、动态汇编？),但是,如果您知道可以通过VS实现的简单插件-会对我有很大帮助.
注意3：这不起作用
var assemblyBuilder = System.AppDomain.CurrentDomain.DefineDynamicAssembly(new AssemblyName("testLambda"),System.Reflection.Emit.AssemblyBuilderAccess.Save);
var modelBuilder = assemblyBuilder.DefineDynamicModule("testLambda_module", "testLambda.dll");
var typeBuilder = modelBuilder.DefineType("testLambda_type");
var method = typeBuilder.DefineMethod("testLambda_method", MethodAttributes.Public | MethodAttributes.Static, typeof(bool),
new[] { typeof(StringBuilder), typeof(int), typeof(int), typeof(bool) });
appendLambda.CompileToMethod(method);
typeBuilder.CreateType();
assemblyBuilder.Save("testLambda.dll");
由于System.TypeInitializationException：“ InvalidOperationException：CompileToMethod无法编译常量’System.Func3 [System.Int32,System.Int32,System.Int32]”,因为它是一个非平凡的值,例如活动对象.可以构造该值的表达式树.”
这意味着appendLambda`包含参数类型为Func的参数,该参数类型不是基本类型,并且CompileToMethod仅使用基本类型是有限制的.
解决方法:
由于reasons,编译后的表达式可能会变慢：
TL; DR;
The question is, why is the compiled delegate way slower than a manually-written delegate? Expression.Compile creates a DynamicMethod and associates it with an anonymous assembly to run it in a sand-boxed environment. This makes it safe for a dynamic method to be emitted and executed by partially trusted code but adds some run-time overhead.
有诸如FastExpressionCompiler之类的工具可以帮助缓解问题(免责声明：我是作者)
更新：查看已编译委托的IL
>可以将编译后的委托IL作为字节数组获取：
var hello = "Hello";
Expression<Func<string>> getGreetingExpr = () => hello + " me";
var getGreeting = getGreetingExpr.Compile();
var methodBody = getGreeting.Method.GetMethodBody();
var ilBytes = methodBody.GetILAsByteArray();
>您需要一种解析/读取数组并将其转换为IL指令和参数的方法.
可惜,但我没有找到允许我这样做的工具或强大的NuGet软件包:-(
这是相关的SO question.
最接近的工具可能是this.
标签：performance,micro-optimization,c,net,expression-trees 来源： https://codeday.me/bug/20191026/1932744.html
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