了解jvm中的循环性能 [英] Understanding loops performance in jvm

问题描述

我正在玩 jmh 以及关于

IF =取指令，ID =指令解码，EX =执行，MEM =存储器访问，WB =寄存器回写

来自 Oracle白皮书：

...标准编译器优化，可实现更快的循环执行。循环展开增加了循环体尺寸，同时减少了迭代次数。循环展开还可以提高其他优化的效率。

有关流水线的更多信息：经典RISC管道

I'm playing with jmh and in the section about looping they said that

You might notice the larger the repetitions count, the lower the "perceived" cost of the operation being measured. Up to the point we do each addition with 1/20 ns, well beyond what hardware can actually do. This happens because the loop is heavily unrolled/pipelined, and the operation to be measured is hoisted from the loop. Morale: don't overuse loops, rely on JMH to get the measurement right.

I tried it myself

    @Benchmark
    @OperationsPerInvocation(1)
    public int measurewrong_1() {
        return reps(1);
    }      

    @Benchmark
    @OperationsPerInvocation(1000)
    public int measurewrong_1000() {
        return reps(1000);
    }      

and got the following result:

Benchmark                      Mode  Cnt  Score    Error  Units
MyBenchmark.measurewrong_1     avgt   15  2.425 ±  0.137  ns/op
MyBenchmark.measurewrong_1000  avgt   15  0.036 ±  0.001  ns/op

It indeed shows that the MyBenchmark.measurewrong_1000 is dramatically faster than MyBenchmark.measurewrong_1. But I cannot really understand the optimization JVM does to make this performance improvement.

What do they mean the loop is unrolled/pipelined?

解决方案

Loop unrolling makes pipelining possible. So the pipeline-able CPU (for example RISC) can execute the unrolled code in parallel.

So if your CPU is able to execute 5 pipelines in parallel, your loop will be unrolled in the way:

// pseudo code
int pipelines = 5;
for(int i = 0; i < length; i += pipelines){
    s += (x + y);
    s += (x + y);
    s += (x + y);
    s += (x + y);
    s += (x + y);
}

IF = Instruction Fetch, ID = Instruction Decode, EX = Execute, MEM = Memory access, WB = Register write back

From Oracle White paper:

... a standard compiler optimization that enables faster loop execution. Loop unrolling increases the loop body size while simultaneously decreasing the number of iterations. Loop unrolling also increases the effectiveness of other optimizations.

more information about pipelining: Classic RISC pipeline

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