为什么 LongStream reduce 和 sum 性能之间存在差异？

Question

我正在使用 LongStream 的 rangeClosed 来测试数字求和的性能。当我通过JMH测试性能时，结果如下。

@BenchmarkMode(Mode.AverageTime)
@OutputTimeUnit(TimeUnit.MILLISECONDS)
@Fork(value = 1, jvmArgs = {"-Xms4G", "-Xmx4G"})
@State(Scope.Benchmark)
@Warmup(iterations = 10, time = 10)
@Measurement(iterations = 10, time = 10)
public class ParallelStreamBenchmark {
  private static final long N = 10000000L;

  @Benchmark
  public long sequentialSum() {
    return Stream.iterate(1L, i -> i + 1).limit(N).reduce(0L, Long::sum);
  }

  @Benchmark
  public long parallelSum() {
    return Stream.iterate(1L, i -> i + 1).limit(N).parallel().reduce(0L, Long::sum);
  }

  @Benchmark
  public long rangedReduceSum() {
    return LongStream.rangeClosed(1, N).reduce(0, Long::sum);
  }

  @Benchmark
  public long rangedSum() {
    return LongStream.rangeClosed(1, N).sum();
  }

  @Benchmark
  public long parallelRangedReduceSum() {
    return LongStream.rangeClosed(1, N).parallel().reduce(0L, Long::sum);
  }

  @Benchmark
  public long parallelRangedSum() {
    return LongStream.rangeClosed(1, N).parallel().sum();
  }

  @TearDown(Level.Invocation)
  public void tearDown() {
    System.gc();
  }

Benchmark                                        Mode  Cnt   Score   Error  Units
ParallelStreamBenchmark.parallelRangedReduceSum  avgt   10   7.895 ± 0.450  ms/op
ParallelStreamBenchmark.parallelRangedSum        avgt   10   1.124 ± 0.165  ms/op
ParallelStreamBenchmark.rangedReduceSum          avgt   10   6.832 ± 0.165  ms/op
ParallelStreamBenchmark.rangedSum                avgt   10  21.564 ± 0.831  ms/op

rangedReduceSum与rangedSum的区别在于只使用了内部函数sum()。为什么会有这么大的性能差异？

经过验证sum()函数最终使用了reduce(0, Long::sum)，这不就和在rangedReduceSum方法中使用reduce(0, Long::sum)一样吗？

Answer 1

我做了与 OP 相同的任务，我可以重现完全相同的结果：第二个任务慢了 ~3 倍。但是当我将预热更改为仅 1 次迭代时，事情开始变得有趣：

# Benchmark: test.ParallelStreamBenchmark.rangedReduceSum
# Warmup Iteration   1: 3.619 ms/op
Iteration   1: 3.931 ms/op
Iteration   2: 3.927 ms/op
Iteration   3: 3.834 ms/op
Iteration   4: 4.006 ms/op
Iteration   5: 4.605 ms/op
Iteration   6: 6.454 ms/op
Iteration   7: 6.466 ms/op
Iteration   8: 6.328 ms/op
Iteration   9: 6.370 ms/op
Iteration  10: 6.244 ms/op

# Benchmark: test.ParallelStreamBenchmark.rangedSum
# Warmup Iteration   1: 3.971 ms/op
Iteration   1: 4.034 ms/op
Iteration   2: 3.970 ms/op
Iteration   3: 3.957 ms/op
Iteration   4: 4.024 ms/op
Iteration   5: 4.278 ms/op
Iteration   6: 19.302 ms/op
Iteration   7: 19.132 ms/op
Iteration   8: 19.189 ms/op
Iteration   9: 18.842 ms/op
Iteration  10: 18.292 ms/op

Benchmark                                Mode  Cnt   Score    Error  Units
ParallelStreamBenchmark.rangedReduceSum  avgt   10   5.216 ±  1.871  ms/op
ParallelStreamBenchmark.rangedSum        avgt   10  11.502 ± 11.879  ms/op

每项任务在第 5 次迭代后都显着变慢。对于第二个任务，它在第 5 次迭代后减慢了 3 倍。如果我们将预热算作迭代，那么在 10 次迭代之后，开始缓慢是有意义的。看起来像是 Benchmark 库中的错误，它不能很好地与 GC 配合使用。但正如警告所说，这种情况下的基准测试结果仅供参考。

为什么 LongStream reduce 和 sum 性能之间存在差异？

Why is there a difference between LongStream reduce and sum performance?

java

benchmarking

java-8

java-stream