Swarm Performance

Swarm: Performance

Workflow

1. Profile current performance with cargo bench --bench benchmark
2. Identify hot path from flamegraph or benchmark results
3. Implement optimization behind feature flag if experimental
4. Benchmark before/after with criterion baseline
5. Ensure SIMD has scalar fallback for non-SIMD targets
6. Run all gates before claiming improvement

SIMD Implementation

// Note: std::simd requires nightly Rust and #![feature(portable_simd)]
#[cfg(all(feature = "simd", nightly))]
use std::simd::u128x2;

pub fn cosine_similarity_simd(&self, other: &Self) -> f32 {
    // For stable Rust, use platform-specific intrinsics (AVX2/NEON)
    // as seen in src/hyperdim_simd.rs or src/bundle_simd.rs.
}

Connection Pooling

Use deadpool for async connection pooling, gated for remote Turso only. Keep per-operation model for local SQLite.

Batch API Pattern

pub async fn inject_concepts(
    &self,
    concepts: &[(String, HVec10240)]
) -> Result<()> {
    // Validate all inputs first
    // Batch insert to singularity
    // Batch save to persistence
    // Single transaction for DB
}

Caching Pattern

• Prefer cached values stored as Arc<[T]> so cache hits are cheap (Arc::clone).
• Avoid keying caches via temporary Vec materializations; hash fixed-size words/arrays directly.

Performance Targets

• Batch similarity: 10k ops/ms
• Connection pool: <1ms acquire time
• Cache hit rate: >80% for repeated access patterns
• Reservoir step: maintain <100μs @ 50k

Test Files

Run performance tests:

cargo test --test <test_name>

LOC Constraint

All files must remain ≤ 500 lines. Refactor to new modules if needed.