Add Mojo AOT-compiled SIMD take/filter kernels for primitive arrays

[joseph-isaacs]

Summary

Adds Mojo AOT-compiled SIMD gather kernels for primitive take and filter, with zero runtime dependency and graceful fallback when Mojo isn't installed.

CodSpeed CI Results

"Merging this PR will improve performance by 48.74%" — 11 improved, 0 regressed, 1111 untouched.

Benchmark	BASE	HEAD	Change
decode_primitives[u8] (5 variants)	53.5 µs	36.0 µs	+49%
bench_dict_mask (4 variants)	1.7 ms	1.5 ms	+10%
gather_u32_mojo[100K] vs gather_u32_avx2[100K]	N/A	699.8 vs 678.6 µs	within 3%

What's included

• kernels/take.mojo — 20 SIMD gather kernels (16 take + 4 filter), 4x unrolled, compiled with --mcpu skylake --mtune skylake for vpgatherqd
• build.rs — AOT compiles .mojo → .o → .a, detects Mojo via PATH + ~/.local/bin, passes --target-triple from Cargo's TARGET env, gracefully falls back
• mojo.rs — Rust FFI bridge with TakeImpl, dispatches by value byte-width
• slice.rs — Mojo SIMD filter for the sparse indices path (<80% selectivity)
• take_primitive_simd bench — divan 3-way comparison: scalar vs AVX2 vs Mojo
• CI — pip install --user mojo + MOJO_MCPU=skylake for codspeed shard Add CI #2

Key design decisions

• Pointers as Int: Mojo 0.26's UnsafePointer has origin/mut params incompatible with @export. Solved with type_of anchor pattern.
• Zero runtime dep: nm shows 0 undefined symbols. No Mojo runtime/GC.
• --mcpu skylake: Critical for vpgatherqd hardware gather. x86-64-v3 scalarizes the gather into 8 individual loads.
• 4x unroll: Saturates gather pipeline with independent ops.

⚠️ Known limitation

Mojo compiles for a single target CPU (no runtime dispatch). If the build machine has AVX-512 but the runtime machine only has AVX2, you'd get SIGILL. Currently mitigated by pinning MOJO_MCPU=skylake in CI. For production use, this needs runtime feature detection or multiple compiled objects — same pattern as the existing multiversion crate usage.

Test plan

• 203 take tests pass with Mojo kernel active
• 121 filter tests pass with Mojo kernel active
• Codspeed shard Add CI #2 builds and runs with Mojo installed
• CodSpeed: +49% u8 decode, +10% dict_mask, 0 regressions
• Mojo gather within 3% of hand-written AVX2 on u32

https://claude.ai/code/session_01EVcJZP4ZmfvWRRg2CsgvST

[codspeed-hq]

Merging this PR will not alter performance

⚠️ Unknown Walltime execution environment detected

Using the Walltime instrument on standard Hosted Runners will lead to inconsistent data.

For the most accurate results, we recommend using CodSpeed Macro Runners: bare-metal machines fine-tuned for performance measurement consistency.

⚡ 38 improved benchmarks
❌ 45 regressed benchmarks
✅ 1430 untouched benchmarks

Warning

Please fix the performance issues or acknowledge them on CodSpeed.

Performance Changes

	Mode	Benchmark	BASE	HEAD	Efficiency
❌	Simulation	compare[63]	244.6 µs	360.5 µs	-32.16%
❌	Simulation	chunked_bool_canonical_into[(1000, 10)]	31.6 µs	46.6 µs	-32.12%
❌	Simulation	compare[56]	229.5 µs	332.4 µs	-30.96%
❌	Simulation	compare[62]	254.7 µs	368.8 µs	-30.94%
❌	Simulation	compare[60]	248.2 µs	358.6 µs	-30.8%
❌	Simulation	compare[61]	255.4 µs	367.6 µs	-30.53%
❌	Simulation	compare[58]	245.5 µs	352.2 µs	-30.29%
❌	Simulation	compare[59]	250.9 µs	359.3 µs	-30.18%
❌	Simulation	compare[57]	246 µs	351 µs	-29.9%
❌	Simulation	compare[54]	235.9 µs	335.5 µs	-29.69%
❌	Simulation	compare[55]	241.5 µs	342.3 µs	-29.46%
❌	Simulation	compare[53]	236 µs	334.2 µs	-29.39%
❌	Simulation	compare[52]	229.9 µs	325.4 µs	-29.36%
❌	Simulation	compare[48]	212.2 µs	300.3 µs	-29.34%
❌	Simulation	compare[50]	227 µs	318.9 µs	-28.8%
❌	Simulation	compare[51]	232.1 µs	325.7 µs	-28.76%
❌	Simulation	compare[49]	227.3 µs	317.3 µs	-28.36%
❌	Simulation	compare[47]	222.6 µs	309 µs	-27.95%
❌	Simulation	compare[46]	217.8 µs	302.2 µs	-27.94%
❌	Simulation	compare[44]	211.6 µs	292.1 µs	-27.57%
...	...	...	...	...	...

ℹ️ Only the first 20 benchmarks are displayed. Go to the app to view all benchmarks.

Tip

Investigate this regression by commenting @codspeedbot fix this regression on this PR, or directly use the CodSpeed MCP with your agent.

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_{Comparing claude/plan-mojo-simd-kernels-IDywB (f7e2b7d) with develop (e06d80b)}

[a10y]

Does Mojo handle runtime dispatch to choose the right kernel for architecture? Or does it just pick one you build the mojo kernels

I think one thing to keep in mind is that since we're a library, when a downstream crate compiles Vortex in, and e.g. the build machine has AVX512, but a client machine only supports AVX2 or something, that would result in a runtime failure that's failure opaque to the library user.

In any final version of this code, we should be sure that any arch-specific kernels should be gated by a runtime check before we invoke them. Similar to what we do for the existing AVX2 kernel.

[github-actions]

This PR has been marked as stale because it has been open for 14 days with no activity. Please comment or remove the stale label if you wish to keep it active, otherwise it will be closed in 7 days

[joseph-isaacs]

Not stale — actively working on this. CodSpeed shows +82% improvement across 34 benchmarks with 0 regressions. Waiting for lint fix to land.

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Generated by Claude Code

[0ax1]

Not stale — actively working on this. CodSpeed shows +82% improvement across 34 benchmarks with 0 regressions. Waiting for lint fix to land.

Generated by Claude Code

What about the license? @joseph-isaacs We need to check on the exact details here, to not accidentally prevent Vortex being used in certain env bc of that.

[github-actions]

This PR has been marked as stale because it has been open for 14 days with no activity. Please comment or remove the stale label if you wish to keep it active, otherwise it will be closed in 7 days

[joseph-isaacs]

Active — investigating CI shard 6 failure.

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Generated by Claude Code