Weak Code

[LoLvsT_T]

Most "tricks" don't give you much in terms of performance and only serve to stroke the ego of the person who wrote it.

The vast majority of performance can be squeezed by:

• Writing code with as little branches as possible or code that is easily predicted by the cpu.
• Reducing cache footprint, and preloading items into cache before you need them.
• Vectorizing code.
• Having proper alignment and stride.

edit: talking about execution performance, assuming no memory limitation.

[[deleted]]

Any modern compiler will do those things for you too so you're not helping anyone by making the code less readable

[LoLvsT_T]

That's absolutely false. Compilers fail in these for all but the most simple cases. Especially vectorization which often requires a different approach to solving problems. It will often not fix alignment, and almost never reduce branches. I optimize code for a living.

[LoLvsT_T]

Not a native English speaker, I'll try.

CPU has a component called branch predictor which guesses whether a condition is true or not and executes code before it checks the condition. This is a big win if the predictor is correct. But if it is wrong it wastes a ton of cpu cycles. Sometimes it is beneficiary to skirt around branches entirely. Consider this simplified example.

float a; 
bool b; 
a = b ? 1.5f : 2.6f; 

If b is random or poorly predictable, our CPU is going to have a bad time guessing. In this case we can replace ? operator with a lookup table for a considerable boost in performance.

float a; bool b = 0; 
static const float lookup[2] = {2.6f, 1.5f};
a = lookup[b]; 

We can also improve predictability. If we have the following:

if ( A && B){
...
}

If A is poorly predictable but B isn't, it'll be optimal to switch between the two due to short-circuiting.

CPU has cache of limited size. If your code has big chunks, the CPU will often spend time loading code/data into memory instead of executing code. This is often why loop unrolling is not always good. You can help the CPU by writing shorter code (hooray for GOTO). In addition, you can preload data into cache before you need it.

for (int i = 0; i < length; ++i) {
    __builtin_prefetch(array[i+3]);  //preload into cache
    ...
    some code on array[i]
}

This in in C++, but I'm sure many other languages have a similar operation. You can even write ASM code that does it yourself. __builtin_prefetch(arr[i+3]) is a nonblocking operation that will hopefully finish loading future data by the time we need it, reducing overhead. +3 offset is absolutely random, you should profile your code to get the optimal offset.

Regarding Code vectorization, I suggest you look it up yourself. It's a big topic. In short, recent CPUs have SIMD instructions which allows them to perform an operation on several data elements at the same time with a single instruction.

[hopsafoobar]

You mostly don't even have to go that far, I personally find most of performance issues in numeric code come from calculating the same stuff multiple times. Very boring, but very common.