正如其他人所说，Stopwatch 应该是解决此问题的正确工具。不过可以对其进行一些改进，具体请参见此线程：Benchmarking small code samples in C#, can this implementation be improved?。
我看到了Thomas Maierhofer here的一些有用提示
他的代码基本上是这样的：
//prevent the JIT Compiler from optimizing Fkt calls away
long seed = Environment.TickCount;
//use the second Core/Processor for the test
Process.GetCurrentProcess().ProcessorAffinity = new IntPtr(2);
//prevent "Normal" Processes from interrupting Threads
Process.GetCurrentProcess().PriorityClass = ProcessPriorityClass.High;
//prevent "Normal" Threads from interrupting this thread
Thread.CurrentThread.Priority = ThreadPriority.Highest;
//warm up
method();
var stopwatch = new Stopwatch()
for (int i = 0; i < repetitions; i++)
{
stopwatch.Reset();
stopwatch.Start();
for (int j = 0; j < iterations; j++)
method();
stopwatch.Stop();
print stopwatch.Elapsed.TotalMilliseconds;
}
另一种方法是依靠 Process.TotalProcessTime 来衡量 CPU 保持忙碌的时间运行非常代码/进程，as shown here 这可以反映更真实的情况，因为没有其他进程影响测量。它做了类似的事情：
var start = Process.GetCurrentProcess().TotalProcessorTime;
method();
var stop = Process.GetCurrentProcess().TotalProcessorTime;
print (end - begin).TotalMilliseconds;
samething can be found here. 的一个赤裸裸的详细实现
我编写了一个辅助类来以易于使用的方式执行这两个操作：
public class Clock
{
interface IStopwatch
{
bool IsRunning { get; }
TimeSpan Elapsed { get; }
void Start();
void Stop();
void Reset();
}
class TimeWatch : IStopwatch
{
Stopwatch stopwatch = new Stopwatch();
public TimeSpan Elapsed
{
get { return stopwatch.Elapsed; }
}
public bool IsRunning
{
get { return stopwatch.IsRunning; }
}
public TimeWatch()
{
if (!Stopwatch.IsHighResolution)
throw new NotSupportedException("Your hardware doesn't support high resolution counter");
//prevent the JIT Compiler from optimizing Fkt calls away
long seed = Environment.TickCount;
//use the second Core/Processor for the test
Process.GetCurrentProcess().ProcessorAffinity = new IntPtr(2);
//prevent "Normal" Processes from interrupting Threads
Process.GetCurrentProcess().PriorityClass = ProcessPriorityClass.High;
//prevent "Normal" Threads from interrupting this thread
Thread.CurrentThread.Priority = ThreadPriority.Highest;
}
public void Start()
{
stopwatch.Start();
}
public void Stop()
{
stopwatch.Stop();
}
public void Reset()
{
stopwatch.Reset();
}
}
class CpuWatch : IStopwatch
{
TimeSpan startTime;
TimeSpan endTime;
bool isRunning;
public TimeSpan Elapsed
{
get
{
if (IsRunning)
throw new NotImplementedException("Getting elapsed span while watch is running is not implemented");
return endTime - startTime;
}
}
public bool IsRunning
{
get { return isRunning; }
}
public void Start()
{
startTime = Process.GetCurrentProcess().TotalProcessorTime;
isRunning = true;
}
public void Stop()
{
endTime = Process.GetCurrentProcess().TotalProcessorTime;
isRunning = false;
}
public void Reset()
{
startTime = TimeSpan.Zero;
endTime = TimeSpan.Zero;
}
}
public static void BenchmarkTime(Action action, int iterations = 10000)
{
Benchmark<TimeWatch>(action, iterations);
}
static void Benchmark<T>(Action action, int iterations) where T : IStopwatch, new()
{
//clean Garbage
GC.Collect();
//wait for the finalizer queue to empty
GC.WaitForPendingFinalizers();
//clean Garbage
GC.Collect();
//warm up
action();
var stopwatch = new T();
var timings = new double[5];
for (int i = 0; i < timings.Length; i++)
{
stopwatch.Reset();
stopwatch.Start();
for (int j = 0; j < iterations; j++)
action();
stopwatch.Stop();
timings[i] = stopwatch.Elapsed.TotalMilliseconds;
print timings[i];
}
print "normalized mean: " + timings.NormalizedMean().ToString();
}
public static void BenchmarkCpu(Action action, int iterations = 10000)
{
Benchmark<CpuWatch>(action, iterations);
}
}
打电话就行了
Clock.BenchmarkTime(() =>
{
//code
}, 10000000);
或
Clock.BenchmarkCpu(() =>
{
//code
}, 10000000);
Clock 的最后一部分是棘手的部分。如果你想显示最终的时间，你可以选择你想要的时间。我写了一个扩展方法NormalizedMean，它为您提供读取时间的平均值丢弃噪声。我的意思是我计算每个时间与实际平均值的偏差，然后我丢弃那些值离平均偏差（称为绝对偏差；注意它不是经常听到的标准偏差）更远（只有较慢的），最后返回剩余值的平均值。这意味着，例如，如果定时值是{ 1, 2, 3, 2, 100 }（以毫秒或其他单位为单位），它会丢弃100，并返回{ 1, 2, 3, 2 } 的平均值，即2。或者如果计时是{ 240, 220, 200, 220, 220, 270 }，它会丢弃270，并返回{ 240, 220, 200, 220, 220 } 的平均值，即220。
public static double NormalizedMean(this ICollection<double> values)
{
if (values.Count == 0)
return double.NaN;
var deviations = values.Deviations().ToArray();
var meanDeviation = deviations.Sum(t => Math.Abs(t.Item2)) / values.Count;
return deviations.Where(t => t.Item2 > 0 || Math.Abs(t.Item2) <= meanDeviation).Average(t => t.Item1);
}
public static IEnumerable<Tuple<double, double>> Deviations(this ICollection<double> values)
{
if (values.Count == 0)
yield break;
var avg = values.Average();
foreach (var d in values)
yield return Tuple.Create(d, avg - d);
}