Parallel.For()通过并行化代码可以大大提高性能，但它也有开销（线程之间的同步，在每次迭代时调用委托）。而且由于在您的代码中，每次迭代都非常短（基本上只有几条 CPU 指令），因此这种开销可能会变得很突出。
因此，我认为 usingParallel.For()不是适合您的解决方案。相反，如果您手动并行化代码（在这种情况下非常简单），您可能会看到性能提高。
为了验证这一点，我进行了一些测量：我在MultiplicateArray()200 000 000 个项目的数组上运行了不同的实现（我使用的代码如下）。在我的机器上，串行版本始终需要 0.21 秒，Parallel.For()通常需要 0.45 秒左右，但有时会飙升到 8-9 秒！
首先，我将尝试改进常见情况，稍后我会谈到这些尖峰。我们想用N个 CPU 处理这个数组，所以我们把它分成N个大小相等的部分，分别处理每个部分。结果？0.35 秒。这仍然比串行版本差。但是for遍历数组中的每个项目是最优化的结构之一。我们不能做点什么来帮助编译器吗？提取计算循环的边界可能会有所帮助。事实证明确实如此：0.18 秒。这比串行版本好，但不是很多。而且，有趣的是，在我的 4 核机器（无超线程）上将并行度从 4 更改为 2 并没有改变结果：仍然是 0.18 秒。这让我得出结论，CPU 不是这里的瓶颈，内存带宽才是。
现在，回到峰值：我的自定义并行化没有它们，但是Parallel.For()有，为什么？Parallel.For()确实使用范围分区，这意味着每个线程都处理自己的数组部分。但是，如果一个线程提前完成，它将尝试帮助处理尚未完成的另一个线程的范围。如果发生这种情况，您将获得大量虚假共享，这可能会大大降低代码速度。我自己对强制错误共享的测试似乎表明这确实是问题所在。强制的并行度Parallel.For()似乎对尖峰有所帮助。
当然，所有这些测量都是特定于我计算机上的硬件的，对你来说会有所不同，所以你应该自己测量。
我使用的代码：
static void Main()
{
double[] array = new double[200 * 1000 * 1000];
for (int i = 0; i < array.Length; i++)
array[i] = 1;
for (int i = 0; i < 5; i++)
{
Stopwatch sw = Stopwatch.StartNew();
Serial(array, 2);
Console.WriteLine("Serial: {0:f2} s", sw.Elapsed.TotalSeconds);
sw = Stopwatch.StartNew();
ParallelFor(array, 2);
Console.WriteLine("Parallel.For: {0:f2} s", sw.Elapsed.TotalSeconds);
sw = Stopwatch.StartNew();
ParallelForDegreeOfParallelism(array, 2);
Console.WriteLine("Parallel.For (degree of parallelism): {0:f2} s", sw.Elapsed.TotalSeconds);
sw = Stopwatch.StartNew();
CustomParallel(array, 2);
Console.WriteLine("Custom parallel: {0:f2} s", sw.Elapsed.TotalSeconds);
sw = Stopwatch.StartNew();
CustomParallelExtractedMax(array, 2);
Console.WriteLine("Custom parallel (extracted max): {0:f2} s", sw.Elapsed.TotalSeconds);
sw = Stopwatch.StartNew();
CustomParallelExtractedMaxHalfParallelism(array, 2);
Console.WriteLine("Custom parallel (extracted max, half parallelism): {0:f2} s", sw.Elapsed.TotalSeconds);
sw = Stopwatch.StartNew();
CustomParallelFalseSharing(array, 2);
Console.WriteLine("Custom parallel (false sharing): {0:f2} s", sw.Elapsed.TotalSeconds);
}
}
static void Serial(double[] array, double factor)
{
for (int i = 0; i < array.Length; i++)
{
array[i] = array[i] * factor;
}
}
static void ParallelFor(double[] array, double factor)
{
Parallel.For(
0, array.Length, i => { array[i] = array[i] * factor; });
}
static void ParallelForDegreeOfParallelism(double[] array, double factor)
{
Parallel.For(
0, array.Length, new ParallelOptions { MaxDegreeOfParallelism = Environment.ProcessorCount },
i => { array[i] = array[i] * factor; });
}
static void CustomParallel(double[] array, double factor)
{
var degreeOfParallelism = Environment.ProcessorCount;
var tasks = new Task[degreeOfParallelism];
for (int taskNumber = 0; taskNumber < degreeOfParallelism; taskNumber++)
{
// capturing taskNumber in lambda wouldn't work correctly
int taskNumberCopy = taskNumber;
tasks[taskNumber] = Task.Factory.StartNew(
() =>
{
for (int i = array.Length * taskNumberCopy / degreeOfParallelism;
i < array.Length * (taskNumberCopy + 1) / degreeOfParallelism;
i++)
{
array[i] = array[i] * factor;
}
});
}
Task.WaitAll(tasks);
}
static void CustomParallelExtractedMax(double[] array, double factor)
{
var degreeOfParallelism = Environment.ProcessorCount;
var tasks = new Task[degreeOfParallelism];
for (int taskNumber = 0; taskNumber < degreeOfParallelism; taskNumber++)
{
// capturing taskNumber in lambda wouldn't work correctly
int taskNumberCopy = taskNumber;
tasks[taskNumber] = Task.Factory.StartNew(
() =>
{
var max = array.Length * (taskNumberCopy + 1) / degreeOfParallelism;
for (int i = array.Length * taskNumberCopy / degreeOfParallelism;
i < max;
i++)
{
array[i] = array[i] * factor;
}
});
}
Task.WaitAll(tasks);
}
static void CustomParallelExtractedMaxHalfParallelism(double[] array, double factor)
{
var degreeOfParallelism = Environment.ProcessorCount / 2;
var tasks = new Task[degreeOfParallelism];
for (int taskNumber = 0; taskNumber < degreeOfParallelism; taskNumber++)
{
// capturing taskNumber in lambda wouldn't work correctly
int taskNumberCopy = taskNumber;
tasks[taskNumber] = Task.Factory.StartNew(
() =>
{
var max = array.Length * (taskNumberCopy + 1) / degreeOfParallelism;
for (int i = array.Length * taskNumberCopy / degreeOfParallelism;
i < max;
i++)
{
array[i] = array[i] * factor;
}
});
}
Task.WaitAll(tasks);
}
static void CustomParallelFalseSharing(double[] array, double factor)
{
var degreeOfParallelism = Environment.ProcessorCount;
var tasks = new Task[degreeOfParallelism];
int i = -1;
for (int taskNumber = 0; taskNumber < degreeOfParallelism; taskNumber++)
{
tasks[taskNumber] = Task.Factory.StartNew(
() =>
{
int j = Interlocked.Increment(ref i);
while (j < array.Length)
{
array[j] = array[j] * factor;
j = Interlocked.Increment(ref i);
}
});
}
Task.WaitAll(tasks);
}
示例输出：
Serial: 0,20 s
Parallel.For: 0,50 s
Parallel.For (degree of parallelism): 8,90 s
Custom parallel: 0,33 s
Custom parallel (extracted max): 0,18 s
Custom parallel (extracted max, half parallelism): 0,18 s
Custom parallel (false sharing): 7,53 s
Serial: 0,21 s
Parallel.For: 0,52 s
Parallel.For (degree of parallelism): 0,36 s
Custom parallel: 0,31 s
Custom parallel (extracted max): 0,18 s
Custom parallel (extracted max, half parallelism): 0,19 s
Custom parallel (false sharing): 7,59 s
Serial: 0,21 s
Parallel.For: 11,21 s
Parallel.For (degree of parallelism): 0,36 s
Custom parallel: 0,32 s
Custom parallel (extracted max): 0,18 s
Custom parallel (extracted max, half parallelism): 0,18 s
Custom parallel (false sharing): 7,76 s
Serial: 0,21 s
Parallel.For: 0,46 s
Parallel.For (degree of parallelism): 0,35 s
Custom parallel: 0,31 s
Custom parallel (extracted max): 0,18 s
Custom parallel (extracted max, half parallelism): 0,18 s
Custom parallel (false sharing): 7,58 s
Serial: 0,21 s
Parallel.For: 0,45 s
Parallel.For (degree of parallelism): 0,40 s
Custom parallel: 0,38 s
Custom parallel (extracted max): 0,18 s
Custom parallel (extracted max, half parallelism): 0,18 s
Custom parallel (false sharing): 7,58 s