Comment by glouwbug
6 hours ago
Learn which instructions SIMD nicely (sqrt / fabs, etc). Use ternaries in loops for masking. Use trig identities and lookup tables (don't recompute sin(3t) when you can use two vector multiples using a table of sin(t) eg. sin(t) * sin(t) * sin(t)). Use divisible constexpr constants in loops to eliminate the SIMD tail. Be careful with type casts and floats. `float x; x += 0.5` will introduce *cvt instructions even if the compiler statically knew better otherwise (use 0.5f). Compile with --fast-math and friends so errno doesn't invalidate your SIMD pipeline.
Most applications (including most applications that care about numerical performance) should not use -ffast-math.
That has a similar problem to the article, it's trying to fit far too much into too small a format.
What you've written mostly makes sense to someone who already has a solid understanding of SIMD and of C++ (although I can't say I follow all of it), but the target audience is people who don't. For them, each point needs a much lengthier explanation.
Likely the best tip would to `objdump -d` and inspect the assembly then checking performance counters. Prepending (__attribute__((used)) will allow you to inspect your functions.
A quick restrict example:
copy1 is 52 lines, copy2 is 28 lines, copy3 is 2 lines (just a call to memcpy).
This is a good starting point for self teaching. The impact of your TLB, L1, and overall instruction count (with IPC) can further be measured with `./perf stat -d -d -d ./a.out`. If you want a quick rule of thumb, no instructions are fast instructions.
This seems like domain specific advice.