CPU pipelines: when more is less

2017/10/03 julia microoptimization

I have been working on micro-optimizations for some simulation code, and was reminded of a counter-intuitive artifact of modern CPU architecture, which is worth a short post.

Consider (just for the sake of example) a very simple (if not particularly meaningful) function,

\[ f(x) = \begin{cases} (x+2)^2 & \text{if } x \ge 0,\\ 1-x & \text{otherwise} \end{cases} \]

with implementations

f1(x) = ifelse(x  0, abs2(x+2), 1-x)
f2(x) = x  0 ? abs2(x+2) : 1-x

f1 calculates both possibilities before choosing between them with ifelse, while f2 will only calculate values on demand. So, intuitively, it should be faster.

But it isn't...

julia> x = randn(1_000_000);

julia> using BenchmarkTools

julia> @btime f1.($x);
  664.228 μs (2 allocations: 7.63 MiB)

julia> @btime f2.($x);
  6.519 ms (2 allocations: 7.63 MiB)

...it is about 10x slower.

This can be understood as an artifact of the instruction pipeline: your x86 CPU likes to perform similar operations in staggered manner, and it does not like branches (jumps) because they break the flow.

Comparing the native code reveals that while f1 is jump-free, the if in f2 results in a jump (jae):

julia> @code_native f1(1.0)
        .text
Filename: REPL[2]
        pushq   %rbp
        movq    %rsp, %rbp
        movabsq $139862498743472, %rax  # imm = 0x7F34468E14B0
        movsd   (%rax), %xmm2           # xmm2 = mem[0],zero
Source line: 1
        addsd   %xmm0, %xmm2
        mulsd   %xmm2, %xmm2
        movabsq $139862498743480, %rax  # imm = 0x7F34468E14B8
        movsd   (%rax), %xmm3           # xmm3 = mem[0],zero
        subsd   %xmm0, %xmm3
        xorps   %xmm1, %xmm1
        cmpnlesd        %xmm0, %xmm1
        andpd   %xmm1, %xmm3
        andnpd  %xmm2, %xmm1
        orpd    %xmm3, %xmm1
        movapd  %xmm1, %xmm0
        popq    %rbp
        retq
        nopw    %cs:(%rax,%rax)

julia> @code_native f2(1.0)
        .text
Filename: REPL[3]
        pushq   %rbp
        movq    %rsp, %rbp
Source line: 1
        xorps   %xmm1, %xmm1
        ucomisd %xmm1, %xmm0
        jae     L37
        movabsq $139862498680736, %rax  # imm = 0x7F34468D1FA0
        movsd   (%rax), %xmm1           # xmm1 = mem[0],zero
        subsd   %xmm0, %xmm1
        movapd  %xmm1, %xmm0
        popq    %rbp
        retq
L37:
        movabsq $139862498680728, %rax  # imm = 0x7F34468D1F98
        addsd   (%rax), %xmm0
        mulsd   %xmm0, %xmm0
        popq    %rbp
        retq
        nopl    (%rax)

In my application the speed gain was more modest, but still sizeable. Benchmarking a non-branching version of your code is sometimes worth it, especially if it the change is simple and both branches of the conditional can be run error-free. If, for example, we had to calculate

g(x) = x  0 ? (x+2) : 1-x

then we could not use ifelse without restricting the domain, since √(x+2) would fail whenever x < -2.

Julia Base contains many optimizations like this: for a particularly nice example see functions that use Base.null_safe_op.

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