如何解决Arrays.stream（）。map（）。sum（）的不稳定性能？
JIT将首先编译热循环，该热循环将迭代并操作（映射/减少）数组元素。由于数组包含2个20个元素，因此发生的时间很早。
稍后，JIT编译管道，最有可能在已编译的基准测试方法中内联，并且由于内联限制而无法将其全部编译为一种方法。恰好发生在热循环中达到那些内联限制的情况，并且没有内联对map或sum的调用，因此热循环被无意间“取消了优化”。
-XX:+UnlockDiagnosticVMOptions -XX:+PrintCompilation运行基准测试时使用这些选项，尽早看到以下输出：
-XX:+PrintInlining
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)
那就是编译的热循环。（这%意味着已替换堆栈或OSR）
稍后再进行流管道的编译（我怀疑基准方法有10,000次迭代，但我尚未验证）：
@ 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$5ReducingSink::get (8 bytes) inline (hot)
@ 4 java.lang.Integer::valueOf (32 bytes) inline (hot)
@ 28 java.lang.Integer::<init> (10 bytes) inline (hot)
@ 1 java.lang.Number::<init> (5 bytes) inline (hot)
@ 1 java.lang.Object::<init> (1 bytes) inline (hot)
@ 12 java.lang.Integer::intValue (5 bytes) accessor
请注意热循环中的方法发生的“内联太深”。
甚至在以后生成的JMH测量循环上进行编译：
26857 685 3 test.generated.Measure_multiply::multiply_avgt_jmhLoop (55 bytes)
@ 7 java.lang.System::nanoTime (0 bytes) intrinsic
@ 16 test.Measure::multiply (23 bytes)
@ 4 java.util.Arrays::stream (8 bytes)
@ 4 java.util.Arrays::stream (11 bytes)
@ 3 java.util.Arrays::spliterator (10 bytes)
@ 6 java.util.Spliterators::spliterator (25 bytes) callee is too large
@ 7 java.util.stream.StreamSupport::intStream (14 bytes)
@ 6 java.util.stream.StreamOpFlag::fromcharacteristics (37 bytes) callee is too large
@ 10 java.util.stream.IntPipeline$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)
请注意，没有尝试内联整个流管道，它在到达热循环之前就已停止运行，请参阅“被调用方太大”，从而重新优化热循环。
例如，可以增加内联限制来避免这种行为-XX:MaxInlineLevel=12。
解决方法
我遇到了一个非常原始的数组上非常简单的map / reduce操作的性能配置文件实例。这是我的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();
}
}
以下是典型输出的摘要：
# 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：首先将性能降低十倍，然后将其恢复。相应的时间是测试运行的2.5和22.5秒。这些事件的时间对阵列大小BTW非常敏感。
有什么可能解释这种行为？JIT编译器可能已经在第一次迭代中完成了工作。没有要说的GC操作（由VisualVM确认）…对于任何种类的解释，我都完全不知所措。
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)
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