March 11, 2026
5% speedup, 500% comment chaos
Faster Asin() Was Hiding in Plain Sight
Small 5% speed win sparks “just use bigger tools” fight
TLDR: A developer got a small but real 5% speed boost by using a simple math shortcut for a graphics function. The comments exploded into a brawl: keep polishing tiny wins vs. go big with many-at-once computing (SIMD/GPU) or table lookups, with veterans pointing to old libraries hiding speed tricks—why it matters: faster visuals for everyone.
A coder chasing a math shortcut to speed up graphics math stumbled into a 5% faster way to compute a tricky function (the “arcsin,” used all over in graphics). The wild twist? The fancy trick he chased didn’t help, but a simple Taylor series did—and the comments erupted.
Some cheered the vibe. One user deadpanned “Ideal HN content,” while another stole the show with a zen-of-engineering joke about loving to throw away code—because then you can throw away more code. Others turned philosophical: if you want real speed, don’t polish one number at a time—crunch many at once. That camp pushed for SIMD (doing lots of numbers in one go) or even the GPU (your graphics card) for way bigger gains. One person basically yelled: “5% is cute—16x is better!”
Meanwhile, optimization detectives swooped in with alternatives. A data-table in the CPU’s tiny, super-fast memory (L1 cache) might beat the math altogether, one argued. Another dragged in legendary lore: the famous “fast inverse square root” trick? Versions of it were hiding in a math library for ages—receipts here: fdlibm. Translation: speed hacks often sit in plain sight.
Verdict: a small win in code sparked a big fight over strategy—micro-optimizations vs. throwing the problem at bigger hardware. And yes, the jokes slapped.
Key Points
- •Profiling a C++ ray tracer identified std::asin() as a performance hotspot during texturing.
- •A 4th‑order Taylor series approximation for asin was implemented for |x| ≤ 0.8, with std::asin() used outside this range due to higher error.
- •The Taylor+fallback approach delivered an approximate 5% performance improvement on the author’s hardware.
- •Padé approximants, derived from the Taylor series (e.g., a [3/4] form), were explored using Python.
- •Padé approximants did not provide additional performance benefits for this use case.