High-throughput HNSW vector search engine written in Rust with runtime hardware dispatch (AVX-512 VNNI/FMA and AVX2 FMA), cache-aligned Flat-SoA graph layouts, and quantized INT8 SQ8 graph routing with exact FP32 top-pool reranking (4.0x primary vector memory compression).

1. Verify SHA-256 digests, C-ABI bindings, and 1-vCPU head-to-head confrontation

./reproduce_all.sh

2. Download and evaluate full TexMex SIFT-1M (1,000,000 vectors, 128D, 10,000 queries)

./reproduce_all.sh --sift1m

3. Evaluate High-Dimensional embeddings (Cohere-768D & OpenAI-1536D)

./reproduce_all.sh --high-dim

To compare directly against third-party engines (usearch, hnswlib, faiss-cpu) on the same CPU core (taskset -c 0):

pip install numpy pyyaml usearch hnswlib faiss-cpu python3 confrontation_arena.py --num-vectors 100000 --num-queries 1000 --ef 16,32,64

1. SIFT-128D Benchmark Results (AWS c7a.2xlarge — AMD EPYC 9R14, Zen 4 AVX-512)

Multi-Engine 1-vCPU Confrontation (SIFT-128D, k=10, M=16, efConstruction=100, taskset -c 0)

SIFT-100K 1-vCPU Index Build Time: 5.24 s (Silicium) vs 5.93 s (usearch), 7.71 s (hnswlib), 9.32 s (FAISS)

Dataset & efSearch Silicium (QPS / Recall@10) usearch (QPS / Recall@10) hnswlib (QPS / Recall@10) FAISS (QPS / Recall@10) Speedup vs usearch / hnswlib

SIFT-10K (ef=12) 190,281 QPS (0.9931) 146,653 QPS (0.9701) 134,801 QPS (0.9705) 135,261 QPS (0.9689) 1.30x / 1.41x

SIFT-10K (ef=16) 155,991 QPS (0.9979) 122,131 QPS (0.9867) 114,179 QPS (0.9870) 116,216 QPS (0.9875) 1.28x / 1.37x

SIFT-10K (ef=24) 118,027 QPS (1.0000) 94,813 QPS (0.9972) 89,588 QPS (0.9971) 89,930 QPS (0.9978) 1.24x / 1.32x

SIFT-100K (ef=16) 100,129 QPS (0.9223) 49,040 QPS (0.9002) 35,582 QPS (0.9069) 33,588 QPS (0.8885) 2.04x / 2.81x

SIFT-100K (ef=32) 55,835 QPS (0.9736) 29,534 QPS (0.9696) 21,799 QPS (0.9720) 19,595 QPS (0.9640) 1.89x / 2.56x

SIFT-100K (ef=64) 32,187 QPS (0.9909) 17,466 QPS (0.9937) 13,356 QPS (0.9917) 11,508 QPS (0.9868) 1.84x / 2.41x

SIFT-100K (ef=128) 19,027 QPS (0.9963) 10,644 QPS (0.9967) 8,385 QPS (0.9975) 6,968 QPS (0.9985) 1.79x / 2.27x

Official SIFT-1M Full-Scale Evaluation (1,000,000 vectors, 128D, 10,000 queries, k=10)

Default balanced Profile (M=24, efC=160): 27.79 s 8-vCPU index build (35,985 vectors/s), 85.69% Recall@10 at ef=16 (294,476 QPS) up to 99.81% Recall@10 at ef=256

High-Throughput fast_stream Profile (M=16, efC=100): 14.49 s 8-vCPU index build (68,994 vectors/s), 369,836 QPS peak throughput

ef Recall@10 (balanced M=24) 1-Core / 8-Core QPS (balanced) 1-Core P50 / P99 (balanced) Recall@10 (fast_stream M=16) 1-Core / 8-Core QPS (fast_stream)

16 0.8569 (+9.51%) 39,540 QPS / 294,476 QPS 24.91µs / 40.45µs 0.7618 49,261 QPS / 369,836 QPS

24 0.9078 (+7.75%) 29,404 QPS / 219,191 QPS 33.89µs / 50.81µs 0.8303 36,902 QPS / 278,597 QPS

32 0.9362 (+6.44%) 23,549 QPS / 175,582 QPS 42.70µs / 60.07µs 0.8718 29,787 QPS / 224,878 QPS

48 0.9649 (+4.71%) 16,873 QPS / 125,184 QPS 60.00µs / 79.60µs 0.9178 21,640 QPS / 162,608 QPS

64 0.9777 (+3.52%) 13,260 QPS / 99,105 QPS 77.21µs / 99.48µs 0.9425 17,101 QPS / 128,433 QPS

128 0.9936 (+1.49%) 7,316 QPS / 54,599 QPS 140.86µs / 174.26µs 0.9787 9,374 QPS / 71,169 QPS

256 0.9981 (+0.52%) 3,925 QPS / 29,393 QPS 262.42µs / 322.18µs 0.9929 5,048 QPS / 38,223 QPS

2. High-Dimensional Embeddings (OpenAI-1536D & Cohere-768D, 25,000 vectors, 1,000 queries)

Quantized INT8 SQ8 graph routing with exact FP32 reranking (balanced profile, M=24, efConstruction=160) reduces primary vector memory by 4.0x (36.62 MB vs 146.48 MB at 1536D; 18.31 MB vs 73.24 MB at 768D) while accelerating both index construction and query throughput on AMD EPYC 9R14 (AWS c7a.2xlarge, 8 vCPUs, Zen 4 AVX-512):