java – Arrays.stream()的错误性能map()。sum()

我研究了一个非常简单的map / reduce操作的原始数组上非常不稳定的性能配置文件的例子。这里是我的jmh基准代码:

@OutputTimeUnit(TimeUnit.NANOSECONDS)
@BenchmarkMode(Mode.AverageTime)
@OperationsPerInvocation(Measure.ARRAY_SIZE)
@Warmup(iterations = 300, time = 200, timeUnit=MILLISECONDS)
@Measurement(iterations = 1, time = 1000, timeUnit=MILLISECONDS)
@State(Scope.Thread)
@Threads(1)
@Fork(1)
public class Measure
{
  static final int ARRAY_SIZE = 1<<20;
  final int[] ds = new int[ARRAY_SIZE];

  private IntUnaryOperator mapper;

  @Setup public void setup() {
    setAll(ds, i->(int)(Math.random()*(1<<7)));
    final int multiplier = (int)(Math.random()*10);
    mapper = d -> multiplier*d;
  }

  @Benchmark public double multiply() {
    return Arrays.stream(ds).map(mapper).sum();
  }
}

下面是典型输出的片段:

# VM invoker: /Library/Java/JavaVirtualMachines/jdk1.8.0_20.jdk/Contents/Home/jre/bin/java
# VM options: <none>
# Warmup: 300 iterations, 200 ms each
# Measurement: 1 iterations, 1000 ms each
# Threads: 1 thread, will synchronize iterations
# Benchmark mode: Average time, time/op
# Benchmark: org.sample.Measure.multiply

# Run progress: 0,00% complete, ETA 00:01:01
# Fork: 1 of 1
# Warmup Iteration   1: 0,779 ns/op
# Warmup Iteration   2: 0,684 ns/op
# Warmup Iteration   3: 0,608 ns/op
# Warmup Iteration   4: 0,619 ns/op
# Warmup Iteration   5: 0,642 ns/op
# Warmup Iteration   6: 0,638 ns/op
# Warmup Iteration   7: 0,660 ns/op
# Warmup Iteration   8: 0,611 ns/op
# Warmup Iteration   9: 0,636 ns/op
# Warmup Iteration  10: 0,692 ns/op
# Warmup Iteration  11: 0,632 ns/op
# Warmup Iteration  12: 0,612 ns/op
# Warmup Iteration  13: 1,280 ns/op
# Warmup Iteration  14: 7,261 ns/op
# Warmup Iteration  15: 7,379 ns/op
# Warmup Iteration  16: 7,376 ns/op
# Warmup Iteration  17: 7,379 ns/op
# Warmup Iteration  18: 7,195 ns/op
# Warmup Iteration  19: 7,351 ns/op
# Warmup Iteration  20: 7,761 ns/op
....
....
....
# Warmup Iteration 100: 7,300 ns/op
# Warmup Iteration 101: 7,384 ns/op
# Warmup Iteration 102: 7,132 ns/op
# Warmup Iteration 103: 7,278 ns/op
# Warmup Iteration 104: 7,331 ns/op
# Warmup Iteration 105: 7,335 ns/op
# Warmup Iteration 106: 7,450 ns/op
# Warmup Iteration 107: 7,346 ns/op
# Warmup Iteration 108: 7,826 ns/op
# Warmup Iteration 109: 7,221 ns/op
# Warmup Iteration 110: 8,017 ns/op
# Warmup Iteration 111: 7,611 ns/op
# Warmup Iteration 112: 7,376 ns/op
# Warmup Iteration 113: 0,707 ns/op
# Warmup Iteration 114: 0,828 ns/op
# Warmup Iteration 115: 0,608 ns/op
# Warmup Iteration 116: 0,634 ns/op
# Warmup Iteration 117: 0,633 ns/op
# Warmup Iteration 118: 0,660 ns/op
# Warmup Iteration 119: 0,635 ns/op
# Warmup Iteration 120: 0,566 ns/op

关键时刻发生在迭代13和113:首先,性能降低十倍,然后恢复。相应的时间为2.5和22.5秒进入测试运行。这些事件的时序对阵列大小(BTW)非常敏感。

什么可以解释这种行为? JIT编译器可能在第一次迭代中完成了它的工作;没有GC行动说话(由VisualVM确认)…我对于任何解释都完全失去了。

我的Java版本(OS X):

$ java -version
java version "1.8.0_20"
Java(TM) SE Runtime Environment (build 1.8.0_20-b26)
Java HotSpot(TM) 64-Bit Server VM (build 25.20-b23, mixed mode)
JIT将首先编译迭代的热循环,并对数组元素进行操作(map / reduce)。这发生得很早,因为数组包含220个元素。

后来在JIT上编译管道,很可能在编译的基准测试方法中内联,并且由于内联限制无法将其编译为一个方法。它只是发生那些内联限制在热循环中达到,并且调用map或sum不是内联的,所以热循环是无意的“去优化”。

在运行基准时使用选项-XX:UnlockDiagnosticVMOptions -XX:PrintCompilation -XX:PrintInlining,早期应该看到如下输出:

   1202  487 %     4       java.util.Spliterators$IntArraySpliterator::forEachRemaining @ 49 (68 bytes)
                              @ 53   java.util.stream.IntPipeline$3$1::accept (23 bytes)   inline (hot)
                               \-> TypeProfile (1186714/1186714 counts) = java/util/stream/IntPipeline$3$1
                                @ 12   test.Measure$$Lambda$2/1745776415::applyAsInt (9 bytes)   inline (hot)
                                 \-> TypeProfile (1048107/1048107 counts) = test/Measure$$Lambda$2
                                  @ 5   test.Measure::lambda$setup$1 (4 bytes)   inline (hot)
                                @ 17   java.util.stream.ReduceOps$5ReducingSink::accept (19 bytes)   inline (hot)
                                 \-> TypeProfile (1048107/1048107 counts) = java/util/stream/ReduceOps$5ReducingSink
                                  @ 10   java.util.stream.IntPipeline$$Lambda$3/1779653790::applyAsInt (6 bytes)   inline (hot)
                                   \-> TypeProfile (1048064/1048064 counts) = java/util/stream/IntPipeline$$Lambda$3
                                    @ 2   java.lang.Integer::sum (4 bytes)   inline (hot)

这是热循环得到编译。 (%表示它在堆栈已替换,或OSR’ed)

后来进一步编译流管道发生(在怀疑约10,000次迭代的基准测试方法,但我还没有验证):

                          @ 16   java.util.stream.IntPipeline::sum (11 bytes)   inline (hot)
                           \-> TypeProfile (5120/5120 counts) = java/util/stream/IntPipeline$3
                            @ 2   java.lang.invoke.LambdaForm$MH/1279902262::linkToTargetMethod (8 bytes)   force inline by annotation
                              @ 4   java.lang.invoke.LambdaForm$MH/1847865997::identity (18 bytes)   force inline by annotation
                                @ 14   java.lang.invoke.LambdaForm$DMH/2024969684::invokeStatic_L_L (14 bytes)   force inline by annotation
                                  @ 1   java.lang.invoke.DirectMethodHandle::internalMemberName (8 bytes)   force inline by annotation
                                  @ 10   sun.invoke.util.ValueConversions::identity (2 bytes)   inline (hot)
                            @ 7   java.util.stream.IntPipeline::reduce (16 bytes)   inline (hot)
                              @ 3   java.util.stream.ReduceOps::makeInt (18 bytes)   inline (hot)
                                @ 1   java.util.Objects::requireNonNull (14 bytes)   inline (hot)
                                @ 14   java.util.stream.ReduceOps$5::<init> (16 bytes)   inline (hot)
                                  @ 12   java.util.stream.ReduceOps$ReduceOp::<init> (10 bytes)   inline (hot)
                                    @ 1   java.lang.Object::<init> (1 bytes)   inline (hot)
                              @ 6   java.util.stream.AbstractPipeline::evaluate (94 bytes)   inline (hot)
                                @ 50   java.util.stream.AbstractPipeline::isParallel (8 bytes)   inline (hot)
                                @ 80   java.util.stream.TerminalOp::getOpFlags (2 bytes)   inline (hot)
                                 \-> TypeProfile (5122/5122 counts) = java/util/stream/ReduceOps$5
                                @ 85   java.util.stream.AbstractPipeline::sourceSpliterator (163 bytes)   inline (hot)
                                  @ 79   java.util.stream.AbstractPipeline::isParallel (8 bytes)   inline (hot)
                                @ 88   java.util.stream.ReduceOps$ReduceOp::evaluateSequential (18 bytes)   inline (hot)
                                  @ 2   java.util.stream.ReduceOps$5::makeSink (5 bytes)   inline (hot)
                                    @ 1   java.util.stream.ReduceOps$5::makeSink (16 bytes)   inline (hot)
                                      @ 12   java.util.stream.ReduceOps$5ReducingSink::<init> (15 bytes)   inline (hot)
                                        @ 11   java.lang.Object::<init> (1 bytes)   inline (hot)
                                  @ 6   java.util.stream.AbstractPipeline::wrapAndCopyInto (18 bytes)   inline (hot)
                                    @ 3   java.util.Objects::requireNonNull (14 bytes)   inline (hot)
                                    @ 9   java.util.stream.AbstractPipeline::wrapSink (37 bytes)   inline (hot)
                                      @ 1   java.util.Objects::requireNonNull (14 bytes)   inline (hot)
                                      @ 23   java.util.stream.IntPipeline$3::opWrapSink (10 bytes)   inline (hot)
                                       \-> TypeProfile (4868/4868 counts) = java/util/stream/IntPipeline$3
                                        @ 6   java.util.stream.IntPipeline$3$1::<init> (11 bytes)   inline (hot)
                                          @ 7   java.util.stream.Sink$ChainedInt::<init> (16 bytes)   inline (hot)
                                            @ 1   java.lang.Object::<init> (1 bytes)   inline (hot)
                                            @ 6   java.util.Objects::requireNonNull (14 bytes)   inline (hot)
                                    @ 13   java.util.stream.AbstractPipeline::copyInto (53 bytes)   inline (hot)
                                      @ 1   java.util.Objects::requireNonNull (14 bytes)   inline (hot)
                                      @ 9   java.util.stream.AbstractPipeline::getStreamAndOpFlags (5 bytes)   accessor
                                      @ 12   java.util.stream.StreamOpFlag::isKnown (19 bytes)   inline (hot)
                                      @ 20   java.util.Spliterator::getExactSizeIfKnown (25 bytes)   inline (hot)
                                       \-> TypeProfile (4870/4870 counts) = java/util/Spliterators$IntArraySpliterator
                                        @ 1   java.util.Spliterators$IntArraySpliterator::characteristics (5 bytes)   accessor
                                        @ 19   java.util.Spliterators$IntArraySpliterator::estimateSize (11 bytes)   inline (hot)
                                      @ 25   java.util.stream.Sink$ChainedInt::begin (11 bytes)   inline (hot)
                                       \-> TypeProfile (4870/4870 counts) = java/util/stream/IntPipeline$3$1
                                        @ 5   java.util.stream.ReduceOps$5ReducingSink::begin (9 bytes)   inline (hot)
                                         \-> TypeProfile (4871/4871 counts) = java/util/stream/ReduceOps$5ReducingSink
                                      @ 32   java.util.Spliterator$OfInt::forEachRemaining (53 bytes)   inline (hot)
                                        @ 12   java.util.Spliterators$IntArraySpliterator::forEachRemaining (68 bytes)   inline (hot)
                                          @ 53   java.util.stream.IntPipeline$3$1::accept (23 bytes)   inline (hot)
                                            @ 12   test.Measure$$Lambda$2/1745776415::applyAsInt (9 bytes)   inline (hot)
                                             \-> TypeProfile (1048107/1048107 counts) = test/Measure$$Lambda$2
                                              @ 5   test.Measure::lambda$setup$1 (4 bytes)   inlining too deep
                                            @ 17   java.util.stream.ReduceOps$5ReducingSink::accept (19 bytes)   inline (hot)
                                             \-> TypeProfile (1048107/1048107 counts) = java/util/stream/ReduceOps$5ReducingSink
                                              @ 10   java.util.stream.IntPipeline$$Lambda$3/1779653790::applyAsInt (6 bytes)   inlining too deep
                                               \-> TypeProfile (1048064/1048064 counts) = java/util/stream/IntPipeline$$Lambda$3
                                          @ 53   java.util.stream.IntPipeline$3$1::accept (23 bytes)   inline (hot)
                                            @ 12   test.Measure$$Lambda$2/1745776415::applyAsInt (9 bytes)   inline (hot)
                                             \-> TypeProfile (1048107/1048107 counts) = test/Measure$$Lambda$2
                                              @ 5   test.Measure::lambda$setup$1 (4 bytes)   inlining too deep
                                            @ 17   java.util.stream.ReduceOps$5ReducingSink::accept (19 bytes)   inline (hot)
                                             \-> TypeProfile (1048107/1048107 counts) = java/util/stream/ReduceOps$5ReducingSink
                                              @ 10   java.util.stream.IntPipeline$$Lambda$3/1779653790::applyAsInt (6 bytes)   inlining too deep
                                               \-> TypeProfile (1048064/1048064 counts) = java/util/stream/IntPipeline$$Lambda$3
                                      @ 38   java.util.stream.Sink$ChainedInt::end (10 bytes)   inline (hot)
                                        @ 4   java.util.stream.Sink::end (1 bytes)   inline (hot)
                                         \-> TypeProfile (5120/5120 counts) = java/util/stream/ReduceOps$5ReducingSink
                                  @ 12   java.util.stream.ReduceOps$5ReducingSink::get (5 bytes)   inline (hot)
                                    @ 1   java.util.stream.ReduceOps$5ReducingSink::get (8 bytes)   inline (hot)
                                      @ 4   java.lang.Integer::valueOf (32 bytes)   inline (hot)
                                        @ 28   java.lang.Integer::<init> (10 bytes)   inline (hot)
                                          @ 1   java.lang.Number::<init> (5 bytes)   inline (hot)
                                            @ 1   java.lang.Object::<init> (1 bytes)   inline (hot)
                              @ 12   java.lang.Integer::intValue (5 bytes)   accessor

注意在热循环中的方法发生的“内联太深”。

甚至以后生成的JMH测量循环被编译:

  26857  685       3       test.generated.Measure_multiply::multiply_avgt_jmhLoop (55 bytes)
                              @ 7   java.lang.System::nanoTime (0 bytes)   intrinsic
                              @ 16   test.Measure::multiply (23 bytes)
                                @ 4   java.util.Arrays::stream (8 bytes)
                                  @ 4   java.util.Arrays::stream (11 bytes)
                                    @ 3   java.util.Arrays::spliterator (10 bytes)
                                      @ 6   java.util.Spliterators::spliterator (25 bytes)   callee is too large
                                    @ 7   java.util.stream.StreamSupport::intStream (14 bytes)
                                      @ 6   java.util.stream.StreamOpFlag::fromCharacteristics (37 bytes)   callee is too large
                                      @ 10   java.util.stream.IntPipeline$Head::<init> (8 bytes)
                                        @ 4   java.util.stream.IntPipeline::<init> (8 bytes)
                                          @ 4   java.util.stream.AbstractPipeline::<init> (55 bytes)   callee is too large
                                @ 11   java.util.stream.IntPipeline::map (26 bytes)
                                  @ 1   java.util.Objects::requireNonNull (14 bytes)
                                    @ 8   java.lang.NullPointerException::<init> (5 bytes)   don't inline Throwable constructors
                                  @ 22   java.util.stream.IntPipeline$3::<init> (20 bytes)
                                    @ 16   java.util.stream.IntPipeline$StatelessOp::<init> (29 bytes)   callee is too large
                                @ 16   java.util.stream.IntPipeline::sum (11 bytes)
                                  @ 2   java.lang.invoke.LambdaForm$MH/1279902262::linkToTargetMethod (8 bytes)   force inline by annotation
                                    @ 4   java.lang.invoke.LambdaForm$MH/1847865997::identity (18 bytes)   force inline by annotation
                                      @ 14   java.lang.invoke.LambdaForm$DMH/2024969684::invokeStatic_L_L (14 bytes)   force inline by annotation
                                        @ 1   java.lang.invoke.DirectMethodHandle::internalMemberName (8 bytes)   force inline by annotation
                                        @ 10   sun.invoke.util.ValueConversions::identity (2 bytes)
                                  @ 7   java.util.stream.IntPipeline::reduce (16 bytes)
                                    @ 3   java.util.stream.ReduceOps::makeInt (18 bytes)
                                      @ 1   java.util.Objects::requireNonNull (14 bytes)
                                      @ 14   java.util.stream.ReduceOps$5::<init> (16 bytes)
                                        @ 12   java.util.stream.ReduceOps$ReduceOp::<init> (10 bytes)
                                          @ 1   java.lang.Object::<init> (1 bytes)
                                    @ 6   java.util.stream.AbstractPipeline::evaluate (94 bytes)   callee is too large
                                    @ 12   java.lang.Integer::intValue (5 bytes)

注意,没有尝试内联整个流管道,它在到达热循环之前停止,见“被调用程序太大”,从而重新优化热循环。

可以增加内联限制以避免此类行为,例如-XX:MaxInlineLevel = 12。

http://stackoverflow.com/questions/25847397/erratic-performance-of-arrays-stream-map-sum

本站文章除注明转载外,均为本站原创或编译
转载请明显位置注明出处:java – Arrays.stream()的错误性能map()。sum()