我们知道 TPL(所以 PLINQ 也是如此)如果他认为任务很简单并在单核上执行，则不会消耗所有核。但即使是复杂的任务，他也会这样做!例如，这里是关于 Java 并行性的文章中的代码:

import org.openjdk.jmh.infra.Blackhole;
import org.openjdk.jmh.annotations.*;
import java.util.concurrent.TimeUnit;
import java.util.stream.IntStream;
import java.math.BigInteger;

@Warmup(iterations=5)
@Measurement(iterations=10)
@BenchmarkMode(Mode.AverageTime)
@OutputTimeUnit(TimeUnit.MICROSECONDS)
@State(Scope.Benchmark)
@Fork(2)
public class Factorial {
    private static final BigInteger ONE = BigInteger.valueOf(1);

    @Param({"10", "100", "1000", "10000", "50000"})
    private int n;

    public static BigInteger naive(int n) {
        BigInteger r = ONE;
        for (int i = 2; i <= n; ++i)
            r = r.multiply(BigInteger.valueOf(i));
        return r;
    }

    public static BigInteger streamed(int n) {
        if(n < 2) return ONE;
        return IntStream.rangeClosed(2, n).mapToObj(BigInteger::valueOf).reduce(BigInteger::multiply).get();
    }

    public static BigInteger streamedParallel(int n) {
        if(n < 2) return ONE;
        return IntStream.rangeClosed(2, n).parallel().mapToObj(BigInteger::valueOf).reduce(BigInteger::multiply).get();
    }

    public static BigInteger fourBlocks(int n) {
        if(n < 2) return ONE;
        BigInteger r1 = ONE, r2 = ONE, r3 = ONE, r4 = ONE;
        int i;
        for (i = n; i > 4; i -= 4)
        {
            r1 = r1.multiply(BigInteger.valueOf(i));
            r2 = r2.multiply(BigInteger.valueOf(i - 1));
            r3 = r3.multiply(BigInteger.valueOf(i - 2));
            r4 = r4.multiply(BigInteger.valueOf(i - 3));
        }
        int mult = i == 4 ? 24 : i == 3 ? 6 : i == 2 ? 2 : 1;
        return r1.multiply(r2).multiply(r3.multiply(r4)).multiply(BigInteger.valueOf(mult));
    }

    public static BigInteger streamedShift(int n) {
        if(n < 2) return ONE;
        int p = 0, c = 0;
        while ((n >> p) > 1) {
            p++;
            c += n >> p;
        }
        return IntStream.rangeClosed(2, n).map(i -> i >> Integer.numberOfTrailingZeros(i))
                .mapToObj(BigInteger::valueOf).reduce(BigInteger::multiply).get().shiftLeft(c);
    }

    public static BigInteger streamedParallelShift(int n) {
        if(n < 2) return ONE;
        int p = 0, c = 0;
        while ((n >> p) > 1) {
            p++;
            c += n >> p;
        }
        return IntStream.rangeClosed(2, n).parallel().map(i -> i >> Integer.numberOfTrailingZeros(i))
                .mapToObj(BigInteger::valueOf).reduce(BigInteger::multiply).get().shiftLeft(c);
    }

    @Benchmark    
    public void testNaive(Blackhole bh) {
        bh.consume(naive(n));
    }

    @Benchmark    
    public void testStreamed(Blackhole bh) {
        bh.consume(streamed(n));
    }

    @Benchmark    
    public void testStreamedParallel(Blackhole bh) {
        bh.consume(streamedParallel(n));
    }

    @Benchmark    
    public void testFourBlocks(Blackhole bh) {
        bh.consume(fourBlocks(n));
    }

    @Benchmark    
    public void testStreamedShift(Blackhole bh) {
        bh.consume(streamedShift(n));
    }

    @Benchmark    
    public void testStreamedParallelShift(Blackhole bh) {
        bh.consume(streamedParallelShift(n));
    }
}

和结果:

Benchmark                              (n)  Mode  Cnt       Score       Error  Units
Factorial.testFourBlocks                10  avgt   20       0.409 ±     0.027  us/op
Factorial.testFourBlocks               100  avgt   20       4.752 ±     0.147  us/op
Factorial.testFourBlocks              1000  avgt   20     113.801 ±     7.159  us/op
Factorial.testFourBlocks             10000  avgt   20   10626.187 ±    54.785  us/op
Factorial.testFourBlocks             50000  avgt   20  281522.808 ± 13619.674  us/op
Factorial.testNaive                     10  avgt   20       0.297 ±     0.002  us/op
Factorial.testNaive                    100  avgt   20       5.060 ±     0.036  us/op
Factorial.testNaive                   1000  avgt   20     277.902 ±     1.311  us/op
Factorial.testNaive                  10000  avgt   20   32471.921 ±  1092.640  us/op
Factorial.testNaive                  50000  avgt   20  970355.227 ± 64386.653  us/op
Factorial.testStreamed                  10  avgt   20       0.326 ±     0.002  us/op
Factorial.testStreamed                 100  avgt   20       5.393 ±     0.190  us/op
Factorial.testStreamed                1000  avgt   20     265.550 ±     1.772  us/op
Factorial.testStreamed               10000  avgt   20   29871.366 ±   234.457  us/op
Factorial.testStreamed               50000  avgt   20  894549.237 ±  5453.425  us/op
Factorial.testStreamedParallel          10  avgt   20       6.114 ±     0.500  us/op
Factorial.testStreamedParallel         100  avgt   20      10.719 ±     0.786  us/op
Factorial.testStreamedParallel        1000  avgt   20      72.225 ±     0.509  us/op
Factorial.testStreamedParallel       10000  avgt   20    2811.977 ±    14.599  us/op
Factorial.testStreamedParallel       50000  avgt   20   49501.716 ±   729.646  us/op
Factorial.testStreamedParallelShift     10  avgt   20       6.684 ±     0.549  us/op
Factorial.testStreamedParallelShift    100  avgt   20      11.176 ±     0.779  us/op
Factorial.testStreamedParallelShift   1000  avgt   20      71.056 ±     3.918  us/op
Factorial.testStreamedParallelShift  10000  avgt   20    2641.108 ±   142.571  us/op
Factorial.testStreamedParallelShift  50000  avgt   20   46480.544 ±   405.648  us/op
Factorial.testStreamedShift             10  avgt   20       0.402 ±     0.006  us/op
Factorial.testStreamedShift            100  avgt   20       5.086 ±     0.039  us/op
Factorial.testStreamedShift           1000  avgt   20     237.279 ±     1.566  us/op
Factorial.testStreamedShift          10000  avgt   20   27572.709 ±   135.489  us/op
Factorial.testStreamedShift          50000  avgt   20  874699.213 ± 53645.087  us/o

您可以看到多线程版本的执行速度比单线程快 19 倍(使用了Core i7-4702MQ)。但是在C#版本中

static BigInteger Streamed(int n)
{
    return n < 2 ? 1 : Enumerable.Range(2, n - 1).Aggregate(BigInteger.One, (acc, elm) => acc*elm);
}

static BigInteger StreamedParallel(int n)
{
    return n < 2 ? 1 : Enumerable.Range(2, n - 1).AsParallel().Aggregate(BigInteger.One, (acc, elm) => acc * elm);
}

与所有其他代码相比，此代码的性能最差，这并不奇怪，因为 TPL 开销没有从多线程中获得性能优势。

所以问题是:为什么 Java 标准多线程库如此明智(任何需要 100us+ 的操作都会被提升，参见引用 http://gee.cs.oswego.edu/dl/html/StreamParallelGuidance.html)，而 C# 不能提升我的 1500ms 的操作机器。

我喜欢 C# 而不是真的很喜欢 Java，这就是它带来伤害的原因，我想了解它的原因...

最佳答案

当像这样使用Aggregate 方法时，PLinq 将按顺序执行聚合，因此在单个线程上执行。当然，乘法可以按任何顺序执行，但 PLinq 无法猜测。例如，如果操作是除法，则更改执行顺序会改变最终结果。

告诉 PLinq 查询可以并行化的一种方法是使用另一个聚合重载，它指示如何合并来自多个线程的结果:

return n < 2 ? 1 : Enumerable.Range(2, n - 1).AsParallel().Aggregate(BigInteger.One, (acc, elm) => acc * elm, (i, j) => i * j, i => i);

对于这个版本，当 n = 100000 时，顺序版本大约需要 9000 毫秒，并行版本需要 4400 毫秒。这几乎快了一倍，这与我的硬件(双核处理器)一致。

您可以阅读这篇文章，了解有关聚合如何与 PLinq 协同​​工作的更多信息:http://blogs.msdn.com/b/pfxteam/archive/2008/01/22/7211660.aspx

关于java - 什么启发式使用 TPL 来确定何时使用多核，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/29669009/