Справочник Пользователя для AMD 250
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Branch Optimizations
Chapter 6
25112
Rev. 3.06
September 2005
Software Optimization Guide for AMD64 Processors
6.3
Branches That Depend on Random Data
Optimization
Avoid conditional branches that depend on random data, as these branches are difficult to predict.
Application
This optimization applies to:
•
32-bit software
•
64-bit software
Rationale
Suppose a piece of code receives a random stream of characters “A” through “Z” and branches if the
character is before “M” in the collating sequence. Data-dependent branches acting upon basically
random data cause the branch-prediction logic to mispredict the branch about 50% of the time.
character is before “M” in the collating sequence. Data-dependent branches acting upon basically
random data cause the branch-prediction logic to mispredict the branch about 50% of the time.
If possible, design branch-free alternative code sequences that result in shorter average execution
time. This technique is especially important if the branch body is small.
time. This technique is especially important if the branch body is small.
Examples
The following examples illustrate this concept using the CMOVxx instruction.
Signed Integer ABS Function (x = labs(x))
mov ecx, [x] ; Load value.
mov ebx, ecx ; Save value.
neg ecx ; Negate value.
cmovs ecx, ebx ; If negated value is negative, select value.
mov [x], ecx ; Save labs result.
Unsigned Integer min Function (z = x < y ? x : y)
mov eax, [x] ; Load x value.
mov ebx, [y] ; Load y value.
cmp eax, ebx ; EBX <= EAX ? CF = 0 : CF = 1
cmovnc eax, ebx ; EAX = (EBX <= EAX) ? EBX : EAX
mov [z], eax ; Save min(X,Y).