[AURON #2160] perf: SIMD short-circuit in JoinHashMap probe#2161
[AURON #2160] perf: SIMD short-circuit in JoinHashMap probe#2161yew1eb wants to merge 2 commits intoapache:masterfrom
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Pull request overview
Optimizes the SIMD-based probe path in the native-engine join hash map by short-circuiting the “empty slot” SIMD comparison when a hash match is found, targeting reduced instruction count in typical high-hit-rate join workloads.
Changes:
- Splits the probe condition into a fast-path (hash match) and slow-path (empty slot) to avoid an unconditional empty-mask SIMD compare.
- Returns
MapValue::EMPTYdirectly when an empty slot is detected in the probed group.
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ShreyeshArangath
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ShreyeshArangath
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How was the performance tested? Can you share some logs/numbers in the PR description as well?
We should probably set up a microbenchmark for lookup_many with controlled hit rates (0%, 50%, 100%) if possible, WDYT?
@ShreyeshArangath Done. Added benches/join_hash_map.rs with 0%/50%/100% hit rates across 5M/10M/20M keys. The numbers are in the PR description: on M2 Pro the win is ~4–5% between hit=0% and hit=100%, which is modest but expected since this is a small hot-path cleanup. Should be safe to merge. |
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[AURON-2160] Optimize join hash map probe by checking hash_matched first before computing empty mask. This reduces ~50% SIMD instructions when hash hit rate is high (typical join scenarios). Before: Always compute both hash_matched and empty SIMD masks. After: Only compute empty mask when hash_matched has no hits. Also add a criterion microbenchmark (benches/join_hash_map.rs) covering realistic BHJ build sizes (5M/10M/20M keys) × three hit rates (0/50/100%). Results on Apple M2 Pro (probe_size=4096): build size | hit=0% | hit=50% | hit=100% ----------------+---------+---------+--------- 5M (~128 MB) | 6.63 µs | 6.52 µs | 6.35 µs 10M (~256 MB) | 6.68 µs | 6.50 µs | 6.36 µs 20M (~512 MB) | 6.70 µs | 6.59 µs | 6.36 µs Latency stays flat because prefetch_read_data (4-step ahead) fully pipelines cache misses. The hit=100% path is consistently ~4-5% faster, aligning with the optimization goal. Instruction-count savings can be confirmed on x86 via: perf stat -e instructions Run benchmark: cargo bench --bench join_hash_map -p datafusion-ext-plans
Which issue does this PR close?
Closes #2160
Rationale for this change
Optimize join hash map probe by checking hash_matched first before computing empty mask.
What changes are included in this PR?
Changes:
hash_matchedbefore computingemptymask.benches/join_hash_map.rswith 0%/50%/100% hit rates × 5M/10M/20M keys.Are there any user-facing changes?
How was this patch tested?
Benchmark (M2 Pro, probe_size=4096):
hit=100% is consistently ~4–5% faster than hit=0%.