我决定使用不同的锁定策略并使用JMH来测量增量。
我正在使用JMH来检查吞吐量和平均时间，以及用于检查正确性的简单自定义测试。
有六种策略:
基准代码:
@State(Scope.Benchmark)
@BenchmarkMode({Mode.Throughput, Mode.AverageTime})
@OutputTimeUnit(TimeUnit.MICROSECONDS)
@Fork(1)
@Warmup(iterations = 5)
@Measurement(iterations = 5)
public class UnsafeCounter_Benchmark {
public Counter unsync, syncNoV, syncV, lock, atomic, unsafe, unsafeGA;
@Setup(Level.Iteration)
public void prepare() {
unsync = new UnsyncCounter();
syncNoV = new SyncNoVolatileCounter();
syncV = new SyncVolatileCounter();
lock = new LockCounter();
atomic = new AtomicCounter();
unsafe = new UnsafeCASCounter();
unsafeGA = new UnsafeGACounter();
}
@Benchmark
public void unsyncCount() {
unsyncCounter();
}
@CompilerControl(CompilerControl.Mode.DONT_INLINE)
public void unsyncCounter() {
unsync.increment();
}
@Benchmark
public void syncNoVCount() {
syncNoVCounter();
}
@CompilerControl(CompilerControl.Mode.DONT_INLINE)
public void syncNoVCounter() {
syncNoV.increment();
}
@Benchmark
public void syncVCount() {
syncVCounter();
}
@CompilerControl(CompilerControl.Mode.DONT_INLINE)
public void syncVCounter() {
syncV.increment();
}
@Benchmark
public void lockCount() {
lockCounter();
}
@CompilerControl(CompilerControl.Mode.DONT_INLINE)
public void lockCounter() {
lock.increment();
}
@Benchmark
public void atomicCount() {
atomicCounter();
}
@CompilerControl(CompilerControl.Mode.DONT_INLINE)
public void atomicCounter() {
atomic.increment();
}
@Benchmark
public void unsafeCount() {
unsafeCounter();
}
@CompilerControl(CompilerControl.Mode.DONT_INLINE)
public void unsafeCounter() {
unsafe.increment();
}
@Benchmark
public void unsafeGACount() {
unsafeGACounter();
}
@CompilerControl(CompilerControl.Mode.DONT_INLINE)
public void unsafeGACounter() {
unsafeGA.increment();
}
public static void main(String[] args) throws RunnerException {
Options baseOpts = new OptionsBuilder()
.include(UnsafeCounter_Benchmark.class.getSimpleName())
.threads(100)
.jvmArgs("-ea")
.build();
new Runner(baseOpts).run();
}
}
和板凳的结果:
JDK 8u20
Benchmark Mode Samples Score Error Units
o.k.u.u.UnsafeCounter_Benchmark.atomicCount thrpt 5 42.178 ± 17.643 ops/us
o.k.u.u.UnsafeCounter_Benchmark.lockCount thrpt 5 24.044 ± 2.264 ops/us
o.k.u.u.UnsafeCounter_Benchmark.syncNoVCount thrpt 5 22.849 ± 1.344 ops/us
o.k.u.u.UnsafeCounter_Benchmark.syncVCount thrpt 5 20.235 ± 2.027 ops/us
o.k.u.u.UnsafeCounter_Benchmark.unsafeCount thrpt 5 12.460 ± 1.326 ops/us
o.k.u.u.UnsafeCounter_Benchmark.unsafeGACount thrpt 5 39.106 ± 2.966 ops/us
o.k.u.u.UnsafeCounter_Benchmark.unsyncCount thrpt 5 93.076 ± 9.674 ops/us
o.k.u.u.UnsafeCounter_Benchmark.atomicCount avgt 5 2.604 ± 0.133 us/op
o.k.u.u.UnsafeCounter_Benchmark.lockCount avgt 5 4.161 ± 0.546 us/op
o.k.u.u.UnsafeCounter_Benchmark.syncNoVCount avgt 5 4.440 ± 0.523 us/op
o.k.u.u.UnsafeCounter_Benchmark.syncVCount avgt 5 5.073 ± 0.439 us/op
o.k.u.u.UnsafeCounter_Benchmark.unsafeCount avgt 5 9.088 ± 5.964 us/op
o.k.u.u.UnsafeCounter_Benchmark.unsafeGACount avgt 5 2.611 ± 0.164 us/op
o.k.u.u.UnsafeCounter_Benchmark.unsyncCount avgt 5 1.047 ± 0.050 us/op
正如我所期望的那样，除了
UnsafeCounter_Benchmark.unsafeCount与sun.misc.Unsafe.compareAndSwapLong循环一起使用while之外，大多数测量都是如此。它是最慢的锁定。public void increment() {
long before = counter;
while (!unsafe.compareAndSwapLong(this, offset, before, before + 1L)) {
before = counter;
}
}
我建议性能低下是由于while循环和JMH引起更高的争用，但是当我通过
Executors检查正确性时，得到的数字与我期望的一样:Counter result: UnsyncCounter 97538676
Time passed in ms:259
Counter result: AtomicCounter 100000000
Time passed in ms:1805
Counter result: LockCounter 100000000
Time passed in ms:3904
Counter result: SyncNoVolatileCounter 100000000
Time passed in ms:14227
Counter result: SyncVolatileCounter 100000000
Time passed in ms:19224
Counter result: UnsafeCASCounter 100000000
Time passed in ms:8077
Counter result: UnsafeGACounter 100000000
Time passed in ms:2549
正确性测试代码:
public class UnsafeCounter_Test {
static class CounterClient implements Runnable {
private Counter c;
private int num;
public CounterClient(Counter c, int num) {
this.c = c;
this.num = num;
}
@Override
public void run() {
for (int i = 0; i < num; i++) {
c.increment();
}
}
}
public static void makeTest(Counter counter) throws InterruptedException {
int NUM_OF_THREADS = 1000;
int NUM_OF_INCREMENTS = 100000;
ExecutorService service = Executors.newFixedThreadPool(NUM_OF_THREADS);
long before = System.currentTimeMillis();
for (int i = 0; i < NUM_OF_THREADS; i++) {
service.submit(new CounterClient(counter, NUM_OF_INCREMENTS));
}
service.shutdown();
service.awaitTermination(1, TimeUnit.MINUTES);
long after = System.currentTimeMillis();
System.out.println("Counter result: " + counter.getClass().getSimpleName() + " " + counter.getCounter());
System.out.println("Time passed in ms:" + (after - before));
}
public static void main(String[] args) throws InterruptedException {
makeTest(new UnsyncCounter());
makeTest(new AtomicCounter());
makeTest(new LockCounter());
makeTest(new SyncNoVolatileCounter());
makeTest(new SyncVolatileCounter());
makeTest(new UnsafeCASCounter());
makeTest(new UnsafeGACounter());
}
}
我知道这是一个非常糟糕的测试，但是在这种情况下，不安全的CAS比Sync变体快两倍，并且一切都按预期进行。
有人可以澄清所描述的行为吗？
有关更多信息，请参见GitHub存储库:Bench，Unsafe CAS counter
最佳答案
大声思考:人们每隔90％的乏味工作会多么频繁，而将10％(从乐趣开始的地方)留给别人，这是很了不起的!好吧，我正在享受所有的乐趣!
让我首先在我的i7-4790K 8u40 EA上重复实验:
Benchmark Mode Samples Score Error Units
UnsafeCounter_Benchmark.atomicCount thrpt 5 47.669 ± 18.440 ops/us
UnsafeCounter_Benchmark.lockCount thrpt 5 14.497 ± 7.815 ops/us
UnsafeCounter_Benchmark.syncNoVCount thrpt 5 11.618 ± 2.130 ops/us
UnsafeCounter_Benchmark.syncVCount thrpt 5 11.337 ± 4.532 ops/us
UnsafeCounter_Benchmark.unsafeCount thrpt 5 7.452 ± 1.042 ops/us
UnsafeCounter_Benchmark.unsafeGACount thrpt 5 43.332 ± 3.435 ops/us
UnsafeCounter_Benchmark.unsyncCount thrpt 5 102.773 ± 11.943 ops/us
的确，有关
unsafeCount测试的事情似乎有些可疑。确实，您必须先验证所有数据，然后才能对其进行验证。对于nanobenchmark，您必须验证生成的代码，以查看是否实际测量了要测量的东西。在JMH中，可以使用-prof perfasm非常快速地实现和。实际上，如果您查看那里最热的unsafeCount区域，您会发现一些有趣的事情: 0.12% 0.04% 0x00007fb45518e7d1: mov 0x10(%r10),%rax
17.03% 23.44% 0x00007fb45518e7d5: test %eax,0x17318825(%rip)
0.21% 0.07% 0x00007fb45518e7db: mov 0x18(%r10),%r11 ; getfield offset
30.33% 10.77% 0x00007fb45518e7df: mov %rax,%r8
0.00% 0x00007fb45518e7e2: add $0x1,%r8
0.01% 0x00007fb45518e7e6: cmp 0xc(%r10),%r12d ; typecheck
0x00007fb45518e7ea: je 0x00007fb45518e80b ; bail to v-call
0.83% 0.48% 0x00007fb45518e7ec: lock cmpxchg %r8,(%r10,%r11,1)
33.27% 25.52% 0x00007fb45518e7f2: sete %r8b
0.12% 0.01% 0x00007fb45518e7f6: movzbl %r8b,%r8d
0.03% 0.04% 0x00007fb45518e7fa: test %r8d,%r8d
0x00007fb45518e7fd: je 0x00007fb45518e7d1 ; back branch
翻译:a)
offset字段在每次迭代时都会重新读取-因为CAS内存影响意味着 Volatile 读取，因此需要悲观地重新读取该字段； b)有趣的是，出于相同的原因，也正在重新读取unsafe字段以进行类型检查。这就是为什么高性能代码应如下所示的原因:
--- a/utils bench/src/main/java/org/kirmit/utils/unsafe/concurrency/UnsafeCASCounter.java
+++ b/utils bench/src/main/java/org/kirmit/utils/unsafe/concurrency/UnsafeCASCounter.java
@@ -5,13 +5,13 @@ import sun.misc.Unsafe;
public class UnsafeCASCounter implements Counter {
private volatile long counter = 0;
- private final Unsafe unsafe = UnsafeHelper.unsafe;
- private long offset;
- {
+ private static final Unsafe unsafe = UnsafeHelper.unsafe;
+ private static final long offset;
+ static {
try {
offset = unsafe.objectFieldOffset(UnsafeCASCounter.class.getDeclaredField("counter"));
} catch (NoSuchFieldException e) {
- e.printStackTrace();
+ throw new IllegalStateException("Whoops!");
}
}
如果这样做，
unsafeCount性能将立即提高:Benchmark Mode Samples Score Error Units
UnsafeCounter_Benchmark.unsafeCount thrpt 5 9.733 ± 0.673 ops/us
给定误差范围，它现在非常接近同步测试。如果现在看
-prof perfasm，这是一个unsafeCount循环: 0.08% 0.02% 0x00007f7575191900: mov 0x10(%r10),%rax
28.09% 28.64% 0x00007f7575191904: test %eax,0x161286f6(%rip)
0.23% 0.08% 0x00007f757519190a: mov %rax,%r11
0x00007f757519190d: add $0x1,%r11
0x00007f7575191911: lock cmpxchg %r11,0x10(%r10)
47.27% 23.48% 0x00007f7575191917: sete %r8b
0.10% 0x00007f757519191b: movzbl %r8b,%r8d
0.02% 0x00007f757519191f: test %r8d,%r8d
0x00007f7575191922: je 0x00007f7575191900
这个循环非常紧密，似乎没有什么可以使它运行得更快。我们花费大部分时间来加载“更新的”值并实际对其进行CAS-ing。但是我们竞争很多!为了弄清楚争用是否是主要原因，让我们添加退避:
--- a/utils bench/src/main/java/org/kirmit/utils/unsafe/concurrency/UnsafeCASCounter.java
+++ b/utils bench/src/main/java/org/kirmit/utils/unsafe/concurrency/UnsafeCASCounter.java
@@ -20,6 +21,7 @@ public class UnsafeCASCounter implements Counter {
long before = counter;
while (!unsafe.compareAndSwapLong(this, offset, before, before + 1L)) {
before = counter;
+ Blackhole.consumeCPU(1000);
}
}
...运行中:
Benchmark Mode Samples Score Error Units
UnsafeCounter_Benchmark.unsafeCount thrpt 5 99.869 ± 107.933 ops/us
瞧我们在循环中需要做更多的工作，但是这使我们免于竞争。我之前曾尝试在"Nanotrusting the Nanotime"中对此进行解释，可能最好回到那里并进一步了解基准测试方法，尤其是在测量重量级操作时。这不仅突出了
unsafeCount，还突出了整个实验的陷阱。OP和感兴趣的读者的练习:解释为什么
unsafeGACount和atomicCount的执行速度比其他测试快得多。您现在有了工具。附言在具有C(C
P.P.S.时间检查:10分钟进行性能分析和其他实验，20分钟进行编写。您浪费了多少时间手动复制结果？ ;)