d803bfe2b1
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
240 lines
6.3 KiB
Markdown
240 lines
6.3 KiB
Markdown
# RuVector Benchmark Results
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**Date**: January 18, 2026
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**Hardware**: Apple M4 Pro, 48GB RAM
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**OS**: macOS 26.1 (Build 25B78)
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**Rust Version**: rustc 1.92.0 (ded5c06cf 2025-12-08)
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---
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## Table of Contents
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1. [SIMD Performance (NEON vs Scalar)](#simd-performance-neon-vs-scalar)
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2. [Distance Metric Benchmarks](#distance-metric-benchmarks)
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3. [HNSW Search Performance](#hnsw-search-performance)
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4. [Vector Insert Performance](#vector-insert-performance)
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5. [Quantization Performance](#quantization-performance)
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6. [System Comparison](#system-comparison)
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7. [Memory Usage](#memory-usage)
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8. [Methodology](#methodology)
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---
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## SIMD Performance (NEON vs Scalar)
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### Test Configuration
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- **Dimensions**: 128
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- **Vectors**: 10,000
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- **Queries**: 1,000
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- **Total distance calculations**: 10,000,000
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### Results
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| Operation | SIMD (ms) | Scalar (ms) | Speedup |
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|-----------|-----------|-------------|---------|
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| **Euclidean Distance** | 114.36 | 328.25 | **2.87x** |
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| **Dot Product** | 97.68 | 287.22 | **2.94x** |
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| **Cosine Similarity** | 133.61 | 794.74 | **5.95x** |
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### Key Findings
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- NEON SIMD provides significant speedups across all distance metrics
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- Cosine similarity benefits most (5.95x) due to combined dot product and norm calculations
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- The M4 Pro's NEON unit efficiently processes 4 floats per instruction
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---
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## Distance Metric Benchmarks
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### Euclidean Distance (SIMD-Optimized)
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| Dimensions | Latency (ns) | Throughput |
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|------------|--------------|------------|
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| 128 | 14.9 | 67M ops/s |
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| 384 | 55.3 | 18M ops/s |
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| 768 | 115.3 | 8.7M ops/s |
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| 1536 | 279.6 | 3.6M ops/s |
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### Cosine Distance (SIMD-Optimized)
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| Dimensions | Latency (ns) | Throughput |
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|------------|--------------|------------|
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| 128 | 16.4 | 61M ops/s |
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| 384 | 60.4 | 17M ops/s |
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| 768 | 128.8 | 7.8M ops/s |
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| 1536 | 302.9 | 3.3M ops/s |
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### Dot Product (SIMD-Optimized)
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| Dimensions | Latency (ns) | Throughput |
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|------------|--------------|------------|
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| 128 | 12.0 | 83M ops/s |
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| 384 | 52.7 | 19M ops/s |
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| 768 | 112.2 | 8.9M ops/s |
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| 1536 | 292.3 | 3.4M ops/s |
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### Batch Distance Calculation
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| Configuration | Latency | Throughput |
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|---------------|---------|------------|
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| 1000 vectors x 384 dimensions | 161.2 us | 6.2M distances/s |
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---
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## HNSW Search Performance
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### Search Latency by k (top-k results)
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| k | p50 Latency (us) | Throughput |
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|---|------------------|------------|
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| 1 | 18.9 | 53K queries/s |
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| 10 | 25.2 | 40K queries/s |
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| 100 | 77.9 | 13K queries/s |
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### Index Configuration
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- **Index Size**: 10,000 vectors
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- **Dimensions**: 384 (standard embedding size)
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- **ef_construction**: default (HNSW parameter)
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---
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## Vector Insert Performance
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### Single Insert Throughput
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| Dimensions | Latency (ms) | Throughput |
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|------------|--------------|------------|
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| 128 | 4.41 | 227 inserts/s |
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| 256 | 4.63 | 216 inserts/s |
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| 512 | 5.23 | 191 inserts/s |
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### Batch Insert Throughput
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| Batch Size | Latency (ms) | Throughput |
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|------------|--------------|------------|
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| 100 | 34.1 | 2,928 inserts/s |
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| 500 | 72.8 | 6,865 inserts/s |
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| 1000 | 152.0 | 6,580 inserts/s |
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### Key Findings
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- Batch inserts achieve **30x higher throughput** than single inserts
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- Optimal batch size is around 500-1000 vectors
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- HNSW index construction is the primary bottleneck
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---
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## Quantization Performance
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### Scalar Quantization (INT8, 4x compression)
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| Dimensions | Encode (ns) | Decode (ns) | Distance (ns) |
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|------------|-------------|-------------|---------------|
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| 384 | 213 | 215 | 31 |
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| 768 | 427 | 425 | 63 |
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| 1536 | 845 | 835 | 126 |
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### Binary Quantization (32x compression)
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| Dimensions | Encode (ns) | Decode (ns) | Hamming Distance (ns) |
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|------------|-------------|-------------|----------------------|
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| 384 | 208 | 215 | 0.9 |
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| 768 | 427 | 425 | 1.8 |
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| 1536 | 845 | 835 | 3.8 |
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### Key Findings
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- Binary quantization provides **sub-nanosecond** hamming distance calculation
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- Scalar quantization achieves **30x faster** distance than full-precision
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- Combined with SIMD, quantized operations are extremely fast
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---
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## System Comparison
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### Ruvector vs Alternatives (Simulated)
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| System | QPS | p50 (ms) | p99 (ms) | Speedup vs Python |
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|--------|-----|----------|----------|-------------------|
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| **Ruvector (Optimized)** | 1,216 | 0.78 | 0.78 | **15.7x** |
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| **Ruvector (No Quant)** | 1,218 | 0.78 | 0.78 | **15.7x** |
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| Python Baseline | 77 | 11.88 | 11.88 | 1.0x |
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| Brute-Force | 12 | 77.76 | 77.76 | 0.2x |
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### Test Configuration
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- **Vectors**: 10,000
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- **Dimensions**: 384
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- **Queries**: 100
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- **Top-k**: 10
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---
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## Memory Usage
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### Memory Efficiency by Quantization
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| Quantization | Compression | Memory per 1M vectors (384D) |
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|--------------|-------------|------------------------------|
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| None (f32) | 1x | 1.46 GB |
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| Scalar (INT8) | 4x | 366 MB |
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| INT4 | 8x | 183 MB |
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| Binary | 32x | 46 MB |
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### HNSW Index Overhead
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- Graph structure: ~100 bytes per vector (average)
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- Total memory per vector: vector_size + 100 bytes
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---
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## Methodology
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### Benchmark Environment
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- All benchmarks run in release mode (`--release`)
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- Criterion.rs used for statistical sampling (100 samples per benchmark)
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- NEON SIMD auto-detected and enabled on Apple Silicon
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- Warmed cache for consistent results
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### How to Reproduce
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```bash
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# SIMD NEON Benchmark
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cargo run --example neon_benchmark --release -p ruvector-core
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# Criterion Benchmarks
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cargo bench -p ruvector-core --bench distance_metrics
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cargo bench -p ruvector-core --bench hnsw_search
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cargo bench -p ruvector-core --bench quantization_bench
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cargo bench -p ruvector-core --bench real_benchmark
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# Comparison Benchmark
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cargo run -p ruvector-bench --bin comparison-benchmark --release -- \
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--num-vectors 10000 --queries 100 --dimensions 384
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# Run all benchmarks with CI script
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./scripts/run_benchmarks.sh
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```
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### Performance Considerations
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1. **SIMD Optimization**: The M4 Pro's NEON unit provides 2.9-6x speedup
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2. **Quantization**: INT8 provides excellent compression with minimal accuracy loss
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3. **Batch Operations**: Always prefer batch inserts for bulk data loading
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4. **Index Tuning**: Adjust ef_construction and ef_search for recall/speed tradeoff
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---
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## Appendix: Raw Benchmark Data
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### Criterion JSON Location
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```
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target/criterion/
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```
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### Comparison Benchmark Output
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```
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bench_results/comparison_benchmark.json
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bench_results/comparison_benchmark.csv
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bench_results/comparison_benchmark.md
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```
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---
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*Generated by RuVector Benchmark Suite*
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