该文章内容发布已经超过一年，请注意检查文章中内容是否过时。
在stackoverflow上有一个非常有名的问题：为什么处理有序数组要比非有序数组快？，可见分支预测对代码运行效率有非常大的影响。
现代CPU都支持分支预测(branch prediction)和指令流水线(instruction pipeline)，这两个结合可以极大提高CPU效率。对于像简单的if跳转，CPU是可以比较好地做分支预测的。但是对于switch跳转，CPU则没有太多的办法。switch本质上是据索引，从地址数组里取地址再跳转。
要提高代码执行效率，一个重要的原则就是尽量避免CPU把流水线清空，那么提高分支预测的成功率就非常重要。
那么对于代码里，如果某个switch分支概率很高，是否可以考虑代码层面帮CPU把判断提前，来提高代码执行效率呢？
在ChannelEventRunnable里有一个switch来判断channel state，然后做对应的逻辑：查看
一个channel建立起来之后，超过99.9%情况它的state都是ChannelState.RECEIVED，那么可以考虑把这个判断提前。
下面通过jmh来验证下：
public class TestBenchMarks {
public enum ChannelState {
CONNECTED, DISCONNECTED, SENT, RECEIVED, CAUGHT
}
@State(Scope.Benchmark)
public static class ExecutionPlan {
@Param({ "1000000" })
public int size;
public ChannelState[] states = null;
@Setup
public void setUp() {
ChannelState[] values = ChannelState.values();
states = new ChannelState[size];
Random random = new Random(new Date().getTime());
for (int i = 0; i < size; i++) {
int nextInt = random.nextInt(1000000);
if (nextInt > 100) {
states[i] = ChannelState.RECEIVED;
} else {
states[i] = values[nextInt % values.length];
}
}
}
}
@Fork(value = 5)
@Benchmark
@BenchmarkMode(Mode.Throughput)
public void benchSiwtch(ExecutionPlan plan, Blackhole bh) {
int result = 0;
for (int i = 0; i < plan.size; ++i) {
switch (plan.states[i]) {
case CONNECTED:
result += ChannelState.CONNECTED.ordinal();
break;
case DISCONNECTED:
result += ChannelState.DISCONNECTED.ordinal();
break;
case SENT:
result += ChannelState.SENT.ordinal();
break;
case RECEIVED:
result += ChannelState.RECEIVED.ordinal();
break;
case CAUGHT:
result += ChannelState.CAUGHT.ordinal();
break;
}
}
bh.consume(result);
}
@Fork(value = 5)
@Benchmark
@BenchmarkMode(Mode.Throughput)
public void benchIfAndSwitch(ExecutionPlan plan, Blackhole bh) {
int result = 0;
for (int i = 0; i < plan.size; ++i) {
ChannelState state = plan.states[i];
if (state == ChannelState.RECEIVED) {
result += ChannelState.RECEIVED.ordinal();
} else {
switch (state) {
case CONNECTED:
result += ChannelState.CONNECTED.ordinal();
break;
case SENT:
result += ChannelState.SENT.ordinal();
break;
case DISCONNECTED:
result += ChannelState.DISCONNECTED.ordinal();
break;
case CAUGHT:
result += ChannelState.CAUGHT.ordinal();
break;
}
}
}
bh.consume(result);
}
}
ChannelState.RECEIVEDbenchmark结果是：
Result "io.github.hengyunabc.jmh.TestBenchMarks.benchSiwtch":
576.745 ±(99.9%) 6.806 ops/s [Average]
(min, avg, max) = (490.348, 576.745, 618.360), stdev = 20.066
CI (99.9%): [569.939, 583.550] (assumes normal distribution)
# Run complete. Total time: 00:06:48
Benchmark (size) Mode Cnt Score Error Units
TestBenchMarks.benchIfAndSwitch 1000000 thrpt 100 1535.867 ± 61.212 ops/s
TestBenchMarks.benchSiwtch 1000000 thrpt 100 576.745 ± 6.806 ops/s
可以看到提前if判断的确提高了代码效率，这种技巧可以放在性能要求严格的地方。
Benchmark代码：https://github.com/hengyunabc/jmh-demo