Arrays.stream（）。map（）。sum（）的不稳定性能 [英] Erratic performance of Arrays.stream().map().sum()
问题描述
我偶然发现了一个非常不稳定的性能曲线实例，它在原始数组上进行了非常简单的map / reduce操作。这是我的jmh基准代码：
I have chanced upon an instance of exceedingly erratic performance profile of a very simple map/reduce operation on primitive arrays. Here is my jmh benchmark code:
@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();
}
}
以下是典型输出的片段：
And here are the snippets of the typical output:
# 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：首先表现是退化十倍，然后恢复。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。
The key moments happen at iterations 13 and 113: first the performance is degraded by a factor of ten, then it is restored. The corresponding times are 2.5 and 22.5 seconds into the test run. The timing of these events is very sensitive to the array size, BTW.
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动作可言（由VisualVM确认）......我完全不知道任何解释。
What can possibly explain this sort of behavior? The JIT compiler had probably done its work within the first iteration; there is no GC action to speak of (confirmed by VisualVM)... I am at a complete loss as to any kind of explanation.
我的Java版本（操作系统） X）：
My version of Java (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)
推荐答案
JIT将首先编译迭代并在数组元素上运行（map / reduce）的热循环。由于数组包含2个 20 元素，所以很早就会发生这种情况。
The JIT will first compile the hot loop that is iterating over and operating (map/reduce) on the array elements. This happens quite early on since the array contains 220 elements.
稍后JIT编译管道，很可能在编译的基准测试中内联方法，并且由于内联限制无法将其全部编译为一个方法。恰好在热循环中达到了那些内联限制，并且没有内联调用map或sum，因此热循环无意中被去优化。
Later on the JIT compiles the pipeline, most likely inlined within the compiled benchmark method, and due to inlining limits fails to compile it all into one method. It just so happens those inlining limits are reached in the hot loop, and calls to map or sum are not inlined, so the hot loop is unintentionally "de-optimized".
使用选项 -XX：+ UnlockDiagnosticVMOptions -XX：+ PrintCompilation -XX：+ PrintInlining 运行基准测试时，您应该看到以下输出：
Use the options -XX:+UnlockDiagnosticVMOptions -XX:+PrintCompilation -XX:+PrintInlining when running the benchmark and early on you should see output like the following:
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)
这是编译的热循环。 （％表示它是在堆栈替换，或OSR'）
That is the hot loop getting compiled. (The % means it is On Stack Replaced, or OSR'ed)
稍后进一步编译流管道发生（我怀疑基准方法的约10,000次迭代，但我还没有验证）：
Later on further compilation of the stream pipeline occurs (at i suspect ~10,000 iterations of the benchmark method, but I have not verified):
@ 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$5ReducingSink::get (8 bytes) inline (hot)
@ 4 java.lang.Integer::valueOf (32 bytes) inline (hot)
@ 28 java.lang.Integer::<init> (10 bytes) inline (hot)
@ 1 java.lang.Number::<init> (5 bytes) inline (hot)
@ 1 java.lang.Object::<init> (1 bytes) inline (hot)
@ 12 java.lang.Integer::intValue (5 bytes) accessor
注意热循环中方法发生的内联太深。
Note the "inlining too deep" that occurs for methods in the hot loop.
即使在生成的JMH测量循环后面也会编译：
Even later on the generated JMH measurement loop is compiled:
26857 685 3 test.generated.Measure_multiply::multiply_avgt_jmhLoop (55 bytes)
@ 7 java.lang.System::nanoTime (0 bytes) intrinsic
@ 16 test.Measure::multiply (23 bytes)
@ 4 java.util.Arrays::stream (8 bytes)
@ 4 java.util.Arrays::stream (11 bytes)
@ 3 java.util.Arrays::spliterator (10 bytes)
@ 6 java.util.Spliterators::spliterator (25 bytes) callee is too large
@ 7 java.util.stream.StreamSupport::intStream (14 bytes)
@ 6 java.util.stream.StreamOpFlag::fromCharacteristics (37 bytes) callee is too large
@ 10 java.util.stream.IntPipeline$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停止了，请参阅 被调用者太大，从而重新优化热循环。
Note that there is no attempt to inline the whole stream pipeline, it stops well before it reached the hot loop, see "callee is too large", thereby re-optimizing the hot loop.
可以增加内联限制以避免此类行为，例如 -XX ：MaxInlineLevel = 12 。
The inline limit can be increased to avoid such behaviour, for example -XX:MaxInlineLevel=12.
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