feat: simd double unary math ops - #19768
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| Severity | Findings |
|---|---|
| P0 | 0 |
| P1 | 1 |
| P2 | 0 |
| P3 | 0 |
| Total | 1 |
| Severity | Findings |
|---|---|
| P0 | 0 |
| P1 | 1 |
| P2 | 0 |
| P3 | 0 |
| Total | 1 |
Reviewed 36 of 36 changed files.
This is an automated review by Codex GPT-5.6-Sol
| final int upperBound = SPECIES.loopBound(currentSize); | ||
| int i = 0; | ||
| for (; i < upperBound; i += laneCount) { | ||
| DoubleVector.fromArray(SPECIES, input, i).lanewise(VectorOperators.SIN).intoArray(outValues, i); |
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[P1] Keep transcendental results stable across JIT warmup
VectorOperators.SIN initially matches Math.sin, but compiled SLEEF/SVML output can differ. On JDK 25/AArch64, this exact loop changed 665 of 8,192 lanes after warmup; most other added VO_MATHLIB operations also diverged. Compilation mid-query can therefore give identical inputs different bits, affecting exact filters or group keys. Keep these operations scalar unless stable semantics are established, and add warmed parity tests.
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Description
Adds a bunch of simd optimizations of the remaining double unary operators. The operators do not have direct intrinsics but are backed by a vectorized math library that depends on platform and jdk build, so performance may vary.
Along the way I found sinh, cosh, and tanh were not wired up to SQL, so have added those
Testing with a M1 mac I generally saw a pretty nice improvement: