Scala 中隐藏的性能成本?-Java问题

Hidden performance cost in Scala?(Scala 中隐藏的性能成本?)

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问题描述

我遇到了这个老问题和用 scala 2.10.3 做了以下实验.

I came across this old question and did the following experiment with scala 2.10.3.

我重写了 Scala 版本以使用显式尾递归:

I rewrote the Scala version to use explicit tail recursion:

import scala.annotation.tailrec

object ScalaMain {
  private val t = 20

  private def run() {
    var i = 10
    while(!isEvenlyDivisible(2, i, t))
      i += 2
    println(i)
  }

  @tailrec private def isEvenlyDivisible(i: Int, a: Int, b: Int): Boolean = {
    if (i > b) true
    else (a % i == 0) && isEvenlyDivisible(i+1, a, b)
  }

  def main(args: Array[String]) {
    val t1 = System.currentTimeMillis()
    var i = 0
    while (i < 20) {
      run()
      i += 1
    }
    val t2 = System.currentTimeMillis()
    println("time: " + (t2 - t1))
  }
}

并将其与以下 Java 版本进行比较.为了与 Scala 进行公平比较，我有意识地将函数设为非静态函数:

and compared it to the following Java version. I consciously made the functions non-static for a fair comparison with Scala:

public class JavaMain {
    private final int t = 20;

    private void run() {
        int i = 10;
        while (!isEvenlyDivisible(2, i, t))
            i += 2;
        System.out.println(i);
    }

    private boolean isEvenlyDivisible(int i, int a, int b) {
        if (i > b) return true;
        else return (a % i == 0) && isEvenlyDivisible(i+1, a, b);
    }

    public static void main(String[] args) {
        JavaMain o = new JavaMain();
        long t1 = System.currentTimeMillis();
        for (int i = 0; i < 20; ++i)
          o.run();
        long t2 = System.currentTimeMillis();
        System.out.println("time: " + (t2 - t1));
    }
}

这是我电脑上的结果:

> java JavaMain
....
time: 9651
> scala ScalaMain
....
time: 20592

这是(Java HotSpot(TM) 64 位服务器 VM，Java 1.7.0_51)上的 scala 2.10.3.

This is scala 2.10.3 on (Java HotSpot(TM) 64-Bit Server VM, Java 1.7.0_51).

我的问题是 scala 版本的隐藏成本是多少?

My question is what is the hidden cost with the scala version?

非常感谢.

推荐答案

好吧，OP 的基准测试并不是最理想的.需要减轻大量影响，包括预热、消除死代码、分叉等.幸运的是，JMH 已经处理了很多事情，并且绑定了 Java 和 Scala.请按照 JMH 页面上的流程获取 benchmark 项目，然后您可以在那里移植下面的 benchmark.

Well, OP's benchmarking is not the ideal one. Tons of effects need to be mitigated, including warmup, dead code elimination, forking, etc. Luckily, JMH already takes care of many things, and has bindings for both Java and Scala. Please follow the procedures on JMH page to get the benchmark project, then you can transplant the benchmarks below there.

这是示例 Java 基准测试:

This is the sample Java benchmark:

@BenchmarkMode(Mode.AverageTime)
@OutputTimeUnit(TimeUnit.MICROSECONDS)
@State(Scope.Benchmark)
@Fork(3)
@Warmup(iterations = 5)
@Measurement(iterations = 5)
public class JavaBench {

    @Param({"1", "5", "10", "15", "20"})
    int t;

    private int run() {
        int i = 10;
        while(!isEvenlyDivisible(2, i, t))
            i += 2;
        return i;
    }

    private boolean isEvenlyDivisible(int i, int a, int b) {
        if (i > b)
            return true;
        else
            return (a % i == 0) && isEvenlyDivisible(i + 1, a, b);
    }

    @GenerateMicroBenchmark
    public int test() {
        return run();
    }

}

...这是示例 Scala 基准测试:

...and this is the sample Scala benchmark:

@BenchmarkMode(Array(Mode.AverageTime))
@OutputTimeUnit(TimeUnit.MICROSECONDS)
@State(Scope.Benchmark)
@Fork(3)
@Warmup(iterations = 5)
@Measurement(iterations = 5)
class ScalaBench {

  @Param(Array("1", "5", "10", "15", "20"))
  var t: Int = _

  private def run(): Int = {
    var i = 10
    while(!isEvenlyDivisible(2, i, t))
      i += 2
    i
  }

  @tailrec private def isEvenlyDivisible(i: Int, a: Int, b: Int): Boolean = {
    if (i > b) true
    else (a % i == 0) && isEvenlyDivisible(i + 1, a, b)
  }

  @GenerateMicroBenchmark
  def test(): Int = {
    run()
  }

}

如果您在 JDK 8 GA、Linux x86_64 上运行这些，您将获得:

If you run these on JDK 8 GA, Linux x86_64, then you'll get:

Benchmark             (t)   Mode   Samples         Mean   Mean error    Units
o.s.ScalaBench.test     1   avgt        15        0.005        0.000    us/op
o.s.ScalaBench.test     5   avgt        15        0.489        0.001    us/op
o.s.ScalaBench.test    10   avgt        15       23.672        0.087    us/op
o.s.ScalaBench.test    15   avgt        15     3406.492        9.239    us/op
o.s.ScalaBench.test    20   avgt        15  2483221.694     5973.236    us/op

Benchmark            (t)   Mode   Samples         Mean   Mean error    Units
o.s.JavaBench.test     1   avgt        15        0.002        0.000    us/op
o.s.JavaBench.test     5   avgt        15        0.254        0.007    us/op
o.s.JavaBench.test    10   avgt        15       12.578        0.098    us/op
o.s.JavaBench.test    15   avgt        15     1628.694       11.282    us/op
o.s.JavaBench.test    20   avgt        15  1066113.157    11274.385    us/op

请注意，我们会同时使用 t 来查看效果是否对于 t 的特定值是局部的.不是，效果系统，Java版快一倍.

Notice we juggle t to see if the effect is local for the particular value of t. It is not, the effect is systematic, and Java version being twice as fast.

PrintAssembly 将对此有所了解.这是 Scala 基准测试中最热门的块:

PrintAssembly will shed some light on this. This one is the hottest block in Scala benchmark:

0x00007fe759199d42: test   %r8d,%r8d
0x00007fe759199d45: je     0x00007fe759199d76  ;*irem
                                               ; - org.sample.ScalaBench::isEvenlyDivisible@11 (line 52)
                                               ; - org.sample.ScalaBench::run@10 (line 45)
0x00007fe759199d47: mov    %ecx,%eax
0x00007fe759199d49: cmp    $0x80000000,%eax
0x00007fe759199d4e: jne    0x00007fe759199d58
0x00007fe759199d50: xor    %edx,%edx
0x00007fe759199d52: cmp    $0xffffffffffffffff,%r8d
0x00007fe759199d56: je     0x00007fe759199d5c
0x00007fe759199d58: cltd   
0x00007fe759199d59: idiv   %r8d

...这是Java中的类似块:

...and this is similar block in Java:

0x00007f4a811848cf: movslq %ebp,%r10
0x00007f4a811848d2: mov    %ebp,%r9d
0x00007f4a811848d5: sar    $0x1f,%r9d
0x00007f4a811848d9: imul   $0x55555556,%r10,%r10
0x00007f4a811848e0: sar    $0x20,%r10
0x00007f4a811848e4: mov    %r10d,%r11d
0x00007f4a811848e7: sub    %r9d,%r11d         ;*irem
                                              ; - org.sample.JavaBench::isEvenlyDivisible@9 (line 63)
                                              ; - org.sample.JavaBench::isEvenlyDivisible@19 (line 63)
                                              ; - org.sample.JavaBench::run@10 (line 54)

请注意，在 Java 版本中，编译器如何使用将整数余数计算转换为乘法和右移的技巧(参见 Hacker's Delight，第 10 章，第 19 节).当编译器检测到我们根据常量计算余数时，这是可能的，这表明 Java 版本达到了那个甜蜜的优化，但 Scala 版本没有.您可以深入研究字节码反汇编以找出 scalac 中的哪些怪癖发生了干预，但本练习的重点是代码生成中令人惊讶的微小差异被基准放大了很多.

Notice how in Java version the compiler employed the trick for translating integer remainder calculation into the multiplication and shifting right (see Hacker's Delight, Ch. 10, Sect. 19). This is possible when compiler detects we compute the remainder against the constant, which suggests Java version hit that sweet optimization, but Scala version did not. You can dig into the bytecode disassembly to figure out what quirk in scalac have intervened, but the point of this exercise is that surprising minute differences in code generation are magnified by benchmarks a lot.

附:@tailrec...

UPDATE:更彻底的效果解释:http:///shipilev.net/blog/2014/java-scala-divided-we-fail/

UPDATE: A more thorough explanation of the effect: http://shipilev.net/blog/2014/java-scala-divided-we-fail/

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