我使用LongStream
的rangeClosed
来测试数字总和的性能。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
和rangedSum
之间的区别在于只使用了内部函数sum((。为什么性能差异如此之大?
在验证了sum()
函数最终使用reduce(0, Long::sum)
之后,它不与在rangedReduceSum
方法中使用reduce(0, Long::sum)
相同吗?
我做了与OP相同的任务,并且我可以复制完全相同的结果:第二个任务慢了大约3倍。但当我把预热改为只有一次迭代时,事情开始变得有趣起来:
# 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次迭代后都会显著减慢。对于第二个任务,它在第五次迭代后减慢了3次。如果我们将预热计算为迭代,那么在10次迭代之后,开始缓慢是有意义的。看起来像是Benchmark库中的一个bug,它不能很好地使用GC。但正如警告所说,这种情况下的基准结果只是供参考。