好的,这是处理此类问题的方法。
尝试重现它。好的,它可以重现:
Benchmark Mode Cnt Score Error Units
LoopInc.directInc thrpt 15 175.678 ± 1.118 ops/ms
LoopInc.indirectInc thrpt 15 641.413 ± 9.722 ops/ms
尝试使用
-prof perfasm
查看生成的汇编代码。长话短说,它会生成大量的代码,所以我们可能希望限制循环展开。但这可能会影响性能,并且可能是原因之一。因此,让我们重新运行
-XX:LoopUnrollLimit=1
。好的,得分降低了,但差异仍然存在,非常好。
Benchmark Mode Cnt Score Error Units
LoopInc.directInc thrpt 15 161.147 ± 6.101 ops/ms
LoopInc.indirectInc thrpt 15 489.430 ± 1.698 ops/ms
再看一下生成的代码,没有什么特别引人注目的地方。嗯,这似乎很有趣。让我们好好研究一下。我们能够描述这个工作负载吗?当然可以,借助于
-prof perfnorm
的帮助,它可以将硬件计数器标准化为基准操作。让我们看看:
Benchmark Mode Cnt Score Error Units
LoopInc.directInc thrpt 15 161.875 ± 3.038 ops/ms
LoopInc.directInc:·CPI thrpt 3 0.967 ± 0.196 #/op
LoopInc.directInc:·L1-dcache-load-misses thrpt 3 0.394 ± 3.663 #/op
LoopInc.directInc:·L1-dcache-loads thrpt 3 2149.594 ± 228.166 #/op
LoopInc.directInc:·L1-dcache-store-misses thrpt 3 0.114 ± 1.001 #/op
LoopInc.directInc:·L1-dcache-stores thrpt 3 1073.666 ± 96.066 #/op
LoopInc.directInc:·L1-icache-load-misses thrpt 3 0.965 ± 22.984 #/op
LoopInc.directInc:·LLC-loads thrpt 3 0.204 ± 2.763 #/op
LoopInc.directInc:·LLC-stores thrpt 3 0.060 ± 0.633 #/op
LoopInc.directInc:·branch-misses thrpt 3 536.068 ± 43.293 #/op
LoopInc.directInc:·branches thrpt 3 3728.890 ± 220.539 #/op
LoopInc.directInc:·cycles thrpt 3 26219.146 ± 6287.590 #/op
LoopInc.directInc:·dTLB-load-misses thrpt 3 0.063 ± 0.124 #/op
LoopInc.directInc:·dTLB-loads thrpt 3 2136.942 ± 165.990 #/op
LoopInc.directInc:·dTLB-store-misses thrpt 3 0.022 ± 0.029 #/op
LoopInc.directInc:·dTLB-stores thrpt 3 1084.787 ± 417.281 #/op
LoopInc.directInc:·iTLB-load-misses thrpt 3 0.081 ± 0.333 #/op
LoopInc.directInc:·iTLB-loads thrpt 3 3.623 ± 19.955 #/op
LoopInc.directInc:·instructions thrpt 3 27114.052 ± 1843.720 #/op
LoopInc.indirectInc thrpt 15 489.164 ± 2.692 ops/ms
LoopInc.indirectInc:·CPI thrpt 3 0.281 ± 0.015 #/op
LoopInc.indirectInc:·L1-dcache-load-misses thrpt 3 0.503 ± 9.071 #/op
LoopInc.indirectInc:·L1-dcache-loads thrpt 3 2149.806 ± 369.040 #/op
LoopInc.indirectInc:·L1-dcache-store-misses thrpt 3 0.167 ± 1.370 #/op
LoopInc.indirectInc:·L1-dcache-stores thrpt 3 1073.895 ± 186.741 #/op
LoopInc.indirectInc:·L1-icache-load-misses thrpt 3 0.313 ± 1.275 #/op
LoopInc.indirectInc:·branch-misses thrpt 3 1.102 ± 0.375 #/op
LoopInc.indirectInc:·branches thrpt 3 2143.670 ± 228.475 #/op
LoopInc.indirectInc:·cycles thrpt 3 8701.665 ± 706.183 #/op
LoopInc.indirectInc:·dTLB-load-misses thrpt 3 0.020 ± 0.301 #/op
LoopInc.indirectInc:·dTLB-loads thrpt 3 2141.965 ± 135.852 #/op
LoopInc.indirectInc:·dTLB-store-misses thrpt 3 0.002 ± 0.029 #/op
LoopInc.indirectInc:·dTLB-stores thrpt 3 1070.376 ± 81.445 #/op
LoopInc.indirectInc:·iTLB-load-misses thrpt 3 0.007 ± 0.135 #/op
LoopInc.indirectInc:·iTLB-loads thrpt 3 0.310 ± 5.768 #/op
LoopInc.indirectInc:·instructions thrpt 3 30968.207 ± 3627.540 #/op
哦,两种基准测试的指令数量相当。速度较慢的需要更多的周期(这就是为什么directInc
中CPI也不理想;然而,在indirectInc
中,产生了接近理想的CPI)。如果您仔细查看可能的原因:没有太多的缓存未命中,没有太多的TLB未命中,但是慢基准测试有很多分支未命中。啊哈!现在我们知道要在生成的代码中查找什么。
让我们再次查看生成的代码。-prof perfasm
方便地突出显示跳转。然后你会看到这个...
directInc:
╭│ 0x00007fa0a82a50ff: jmp 0x00007fa0a82a5116
11.39% 16.90% ││ ↗ 0x00007fa0a82a5101: inc %edx ;*iinc
││ │ ; - org.openjdk.LoopInc::directInc@46 (line 18)
12.52% 23.11% ││ │↗↗ 0x00007fa0a82a5103: mov %r10,0xe8(%r11) ;*invokevirtual putLong
││ │││ ; - java.util.concurrent.ThreadLocalRandom::nextSeed@27 (line 241)
12.00% 8.14% ││ │││ 0x00007fa0a82a510a: inc %r8d ;*iinc
││ │││ ; - org.openjdk.LoopInc::directInc@46 (line 18)
0.03% 0.03% ││ │││ 0x00007fa0a82a510d: cmp $0x3e8,%r8d
│╰ │││ 0x00007fa0a82a5114: jge 0x00007fa0a82a50c7 ;*aload_0
│ │││ ; - org.openjdk.LoopInc::directInc@11 (line 19)
0.80% 0.91% ↘ │││ 0x00007fa0a82a5116: mov 0xf0(%r11),%r10d ;*invokevirtual getInt
│││ ; - java.util.concurrent.ThreadLocalRandom::current@9 (line 222)
4.28% 1.23% │││ 0x00007fa0a82a511d: test %r10d,%r10d
╭│││ 0x00007fa0a82a5120: je 0x00007fa0a82a517b ;*ifne
││││ ; - java.util.concurrent.ThreadLocalRandom::current@12 (line 222)
2.11% 0.01% ││││ 0x00007fa0a82a5122: movabs $0x9e3779b97f4a7c15,%r10
0.01% 0.07% ││││ 0x00007fa0a82a512c: add 0xe8(%r11),%r10 ;*ladd
││││ ; - java.util.concurrent.ThreadLocalRandom::nextSeed@24 (line 242)
7.73% 1.89% ││││ 0x00007fa0a82a5133: mov %r10,%r9
1.21% 1.84% ││││ 0x00007fa0a82a5136: shr $0x21,%r9
1.90% 0.03% ││││ 0x00007fa0a82a513a: xor %r10,%r9
2.02% 0.03% ││││ 0x00007fa0a82a513d: movabs $0xff51afd7ed558ccd,%rcx
0.94% 1.82% ││││ 0x00007fa0a82a5147: imul %rcx,%r9 ;*lmul
││││ ; - java.util.concurrent.ThreadLocalRandom::mix32@9 (line 182)
7.01% 2.40% ││││ 0x00007fa0a82a514b: mov %r9,%rcx
││││ 0x00007fa0a82a514e: shr $0x21,%rcx
1.89% 0.70% ││││ 0x00007fa0a82a5152: xor %r9,%rcx
3.11% 2.55% ││││ 0x00007fa0a82a5155: movabs $0xc4ceb9fe1a85ec53,%r9
0.99% 1.50% ││││ 0x00007fa0a82a515f: imul %r9,%rcx
7.66% 2.89% ││││ 0x00007fa0a82a5163: shr $0x20,%rcx
3.70% 1.97% ││││ 0x00007fa0a82a5167: mov %ecx,%r9d
0.11% ││││ 0x00007fa0a82a516a: and $0x1,%r9d ;*iand
││││ ; - java.util.concurrent.ThreadLocalRandom::nextInt@34 (line 356)
3.76% 11.13% ││││ 0x00007fa0a82a516e: cmp $0x1,%r9d
│╰││ 0x00007fa0a82a5172: je 0x00007fa0a82a5101
10.48% 16.62% │ ││ 0x00007fa0a82a5174: test %r9d,%r9d
│ ╰│ 0x00007fa0a82a5177: je 0x00007fa0a82a5103 ;*lookupswitch
│ │ ; - org.openjdk.LoopInc::directInc@15 (line 19)
│ ╰ 0x00007fa0a82a5179: jmp 0x00007fa0a82a5103 ;*aload_0
│ ; - org.openjdk.LoopInc::directInc@11 (line 19)
↘ 0x00007fa0a82a517b: mov $0xffffff5d,%esi
间接增量:
0.01% 0.01% ↗ 0x00007f65588d8260: mov %edx,%r9d
0.01% │ 0x00007f65588d8263: nopw 0x0(%rax,%rax,1)
11.99% 11.38% │ 0x00007f65588d826c: data16 data16 xchg %ax,%ax ;*iconst_0
│ ; - org.openjdk.LoopInc::indirectInc@11 (line 34)
│ 0x00007f65588d8270: mov 0xf0(%r8),%r10d ;*invokevirtual getInt
│ ; - java.util.concurrent.ThreadLocalRandom::current@9 (line 222)
│ 0x00007f65588d8277: test %r10d,%r10d
│ 0x00007f65588d827a: je 0x00007f65588d8331 ;*ifne
│ ; - java.util.concurrent.ThreadLocalRandom::current@12 (line 222)
0.01% │ 0x00007f65588d8280: movabs $0x9e3779b97f4a7c15,%r10
11.80% 11.49% │ 0x00007f65588d828a: add 0xe8(%r8),%r10 ;*ladd
│ ; - java.util.concurrent.ThreadLocalRandom::nextSeed@24 (line 242)
0.01% 0.01% │ 0x00007f65588d8291: mov %r10,0xe8(%r8) ;*invokevirtual putLong
│ ; - java.util.concurrent.ThreadLocalRandom::nextSeed@27 (line 241)
│ 0x00007f65588d8298: mov %r9d,%edx
0.01% 0.01% │ 0x00007f65588d829b: inc %edx
11.12% 12.40% │ 0x00007f65588d829d: mov %r10,%rcx
0.01% │ 0x00007f65588d82a0: shr $0x21,%rcx
0.03% │ 0x00007f65588d82a4: xor %r10,%rcx
0.06% 0.03% │ 0x00007f65588d82a7: movabs $0xff51afd7ed558ccd,%r10
12.38% 13.94% │ 0x00007f65588d82b1: imul %r10,%rcx ;*lmul
│ ; - java.util.concurrent.ThreadLocalRandom::mix32@9 (line 182)
0.03% 0.01% │ 0x00007f65588d82b5: mov %rcx,%r10
│ 0x00007f65588d82b8: shr $0x21,%r10
0.03% │ 0x00007f65588d82bc: xor %rcx,%r10
11.43% 12.62% │ 0x00007f65588d82bf: movabs $0xc4ceb9fe1a85ec53,%rcx
0.01% │ 0x00007f65588d82c9: imul %rcx,%r10
0.34% 0.30% │ 0x00007f65588d82cd: shr $0x20,%r10
0.85% 0.76% │ 0x00007f65588d82d1: mov %r10d,%r10d
11.81% 11.51% │ 0x00007f65588d82d4: and $0x1,%r10d
2.16% 1.78% │ 0x00007f65588d82d8: cmp $0x1,%r10d
3.45% 3.00% │ 0x00007f65588d82dc: cmovne %r9d,%edx <----- HERE IT IS
17.55% 15.86% │ 0x00007f65588d82e0: inc %r11d ;*iinc
│ ; - org.openjdk.LoopInc::indirectInc@56 (line 33)
│ 0x00007f65588d82e3: cmp $0x3e8,%r11d
╰ 0x00007f65588d82ea: jl 0x00007f65588d8260 ;*if_icmpge
; - org.openjdk.LoopInc::indirectInc@8 (line 33)
请注意,这里使用的是cmovne
而不是jmp
,这就是为什么我们有更多"可预测的"分支。HotSpot对分支进行了剖析,并在分支剖析非常平稳时发出条件移动指令。换句话说,通过支付一点额外的延迟来避免非常可能的分支错误预测。然而,在这种情况下,switch语句是特殊的:它有超过两个选项(0、1和"nothing")。因此,我猜想result
自增不会被合并到cmov
中。(一般来说,HotSpot本可以把零存储到"default"中的result
中,但它失败了,哎呀)
为了证实这个假设,让我们创建一个directCompleteInc
的例子,其中我们仍然使用switch
,但现在涵盖了所有情况:
@Benchmark
public int directCompleteInc() {
int result = 0;
for (int i = 0; i < 1000; i++) {
switch (getValue()) {
case 1:
result++;
break;
default:
break;
}
}
return result;
}
...并且测量它,这一次不使用任何选项,就像OP所做的那样:
Benchmark Mode Cnt Score Error Units
LoopInc.directCompleteInc thrpt 5 644.414 ± 0.371 ops/ms
LoopInc.directInc thrpt 5 174.974 ± 0.103 ops/ms
LoopInc.indirectInc thrpt 5 644.015 ± 0.533 ops/ms
THERE.
确认 directCompleteInc
是否使用了 cmov
并开启了 -prof perfasm
。确实是这样。
喝完它。
getValue
更改为返回预生成的序列,indirectInc
仍然会显著更快。 - apangin