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(bits) of a Libc, Optimized for Wasm

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(bits) of a Libc, Optimized for Wasm

I make a no-CGO Go SQLite driver, by compiling the amalgamation to Wasm, then loading the result with wazero (a CGO-free Wasm runtime). To compile SQLite, I use wasi-sdk, which uses wasi-libc, which is based on musl. It's been said that musl is slow(er than glibc), which is true, to a point. musl uses SWAR on a size_t to implement various functions in string.h. This is fine, except size_t is just 32-bit on Wasm. I found that implementing a few of those functions with Wasm SIMD128 can make them go around 4x faster. Other functions don't even use SWAR; redoing those can make them 16x faster. Smooth sort also has trouble pulling its own weight; a Shell sort seems both simpler and faster, while similarly avoiding recursion, allocations and the addressable stack. I found that using SIMD intrinsics (rather than SWAR) makes it easier to avoid UB, but the code would definitely benefit from more eyeballs. See this for some benchmarks on both x86-64 and Aarch64: https://github.com/ncruces/go-sqlite3/actions/runs/145169318...

Visit productView on Hacker NewsApr 18, 2025
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Product HuntOn track for Day 1 leaderboard · Strong signals: using, code · Missing: mac, agents, macos
69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
69%69% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
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AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
37%37% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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