v0.12.11

What's Changed

• docs(bench): narrow README performance tables by @huacnlee in #32
• fix(jit): restore gpui-shell performance regressed in 0.12.10 by @huacnlee in #33

Full Changelog: v0.12.10...v0.12.11

v0.12.10

What's Changed

• perf(jit): V8/Bun roadmap and P0-P4 implementation by @huacnlee in #30
• docs(bench): refresh three-engine matrix for 889f7c1 (P0–P4) by @huacnlee in #31

Full Changelog: v0.12.9...v0.12.10

v0.12.9

What's Changed

• perf(jit): add bounded facts, effects, and exact recovery by @huacnlee in #29

Full Changelog: v0.12.8...v0.12.9

v0.12.8

What's Changed

• perf: stabilize mixed workloads and reduce JIT overhead by @huacnlee in #24
• docs(perf): require Bun comparisons and track mixed workload regression by @huacnlee in #25
• perf(jit): specialize mixed calls and native property guards by @huacnlee in #26
• feat: Introduce semantic SSA and bounded effect-free inlining by @huacnlee in #27

Full Changelog: v0.12.7...v0.12.8

v0.12.7

What's Changed

• perf(jit): reduce native entry, feedback, and PC validation overhead by @huacnlee in #18
• perf(jit): reuse native timing storage and settle Tier 2 benchmarks by @huacnlee in #19
• perf(jit): consolidate feedback and tier-state lookups by @huacnlee in #20
• docs(perf): compare main engines and set next optimization targets by @huacnlee in #21

Full Changelog: v0.12.6...v0.12.7

v0.12.6

What's Changed

• jit: M2 core opcodes, exact slow paths, Tier 2 ownership fixes, and amortized native entry by @huacnlee in #16

Full Changelog: v0.12.5...v0.12.6

v0.12.5

What's Changed

• jit: complete M1 element-loop and call-heavy optimizations by @huacnlee in #4
• feat: categorize JIT failures and prepare 0.12.5 by @huacnlee in #15

Full Changelog: v0.12.2...v0.12.5

v0.12.2

What's Changed

• jit: Add JIT by @huacnlee in #1

Performance progress

Lower latency is better. Published results use five discarded warmup processes, 30 interleaved fresh processes, ten independent one-second throughput windows, and identical checksums across engines.

Focused compute kernels

Scenario	QuickJS	QuickJS + forced Tier 2	Bun 1.4.0	QuickJS vs JIT	JIT vs Bun
Scalar loop	836.946 us	25.837 us	12.983 us	JIT 32.39x faster	JIT 1.99x slower
Numeric loop	823.818 us	25.715 us	12.358 us	JIT 32.04x faster	JIT 2.08x slower
Iterative Fibonacci, fib(40) x 2000	33.780 ms	753.907 us	1.093 ms	JIT 44.81x faster	JIT 1.45x faster

These kernels demonstrate a profitable machine-code path using guarded unboxed Int32/Float64 SSA, native arithmetic, loop-carried Phi values, exact deoptimization, and compiled-to-compiled scalar calls. They are focused forced-Tier-2 results, not a claim that arbitrary JavaScript is 30–45x faster.

Broad JavaScript matrix

The broad run compares interpreter, forced Tier 1, forced Tier 2, automatic tiering, and Bun using the same scripts and sampling policy. A JIT result is shown only if the worker actually entered native code; N/A means interpreter-only execution and is not reported as JIT performance.

Workload	QuickJS	Best native JIT	Bun 1.4.0	Result
Float64-dense	2.829 ms	322.982 us (Tier 2)	462.551 us	JIT 8.76x faster than QuickJS and 1.43x faster than Bun
Strings / RegExp	19.379 ms	N/A	2.436 ms	No native entry; Bun 7.96x faster than QuickJS
Arrays / TypedArrays	4.677 ms	13.141 ms (Tier 1)	770.318 us	JIT 2.81x slower than QuickJS; Bun 17.06x faster than JIT
Objects / polymorphic shapes	6.687 ms	N/A	829.039 us	No native entry; Bun 8.07x faster than QuickJS
Calls / recursion / closures	7.389 ms	N/A	1.595 ms	No native entry; Bun 4.63x faster than QuickJS
JSON codec	79.053 ms	N/A	8.844 ms	No native entry; Bun 8.94x faster than QuickJS
Map / Set / BigInt	15.308 ms	N/A	2.171 ms	No native entry; Bun 7.05x faster than QuickJS
Exceptions / Promise / async	2.483 ms	N/A	741.949 us	No native entry; Bun 3.35x faster than QuickJS

Float64 is profitable and exceeds Bun for this specific kernel. Arrays/TypedArrays reach Tier 1 but remain substantially unprofitable. The other six workers provide honest QuickJS/Bun reference points and identify native-coverage work instead of presenting fallback timing as acceleration.

New Contributors

• @huacnlee made their first contribution in #1

Full Changelog: https://github.com/longbridge/rquickjs/commits/v0.12.2