Johnny's Software Lab

I was preparing an article about Highway – portable vectorization library by Google – so I ported a few examples from my vectorization workshop from AVX to Highway.

Floating-point errors are unavoidable, but how you detect and handle them can make the difference between clean, high-performance C++ code and a debugging nightmare.

For most of my career I lived in the world of C and C++, and I honestly believed that these languages are the pinnacle of software performance.

At Johnny’s Software Lab we’ve spent a lot of time deep-diving into advanced performance topics — vectorization, cache hierarchies, memory bandwidth, you name it.

We’ve discussed SIMD and vectorization extensively on this blog, and it was only a matter of time before SIMD (or vector) functions came up.

Does it matter if we are compiling with optimizations off (O0) or optimizations on (O3) if the problem is memory bound?

Copying data can be expensive in some cases, especially since it it doesn’t change the data, it’s just moves it.

We debug our performance problem by simulating it with llvm-mca!

Flamegraphs are great way to visualize resource consumption in your program and I am their big fan (I have written about them on two occasions – here and here).

This is the last memory optimization that we are covering in this blog.