Performance

Benchmarks

Run benchmarks:

cargo bench --bench benchmark -- --save-baseline main
cargo bench --bench benchmark -- --baseline main

Targets

Tuning Guide

Small Workloads (<10k concepts)

#![allow(unused)]
fn main() {
let framework = ChaoticSemanticFramework::builder()
    .without_persistence()
    .with_reservoir_size(10_240)
    .with_concept_cache_size(128)
    .build()
    .await?;
}

Medium Workloads (10k-1M concepts)

#![allow(unused)]
fn main() {
let framework = ChaoticSemanticFramework::builder()
    .with_local_db("memory.db")
    .with_max_concepts(1_000_000)
    .with_concept_cache_size(1_000)
    .build()
    .await?;
}

Large Workloads (>1M concepts)

#![allow(unused)]
fn main() {
let framework = ChaoticSemanticFramework::builder()
    .with_remote_db(turso_url, auth_token)
    .with_connection_pool_size(20)
    .with_max_concepts(10_000_000)
    .with_max_probe_top_k(1_000)
    .with_concept_cache_size(10_000)
    .build()
    .await?;
}

Memory Footprint

Tips:

• Set max_concepts to enforce memory ceiling
• Use max_associations_per_concept to limit edges
• Cache hit rate improves with concept_cache_size

Optimization Techniques

SIMD Hypervector Operations

Automatic via std::simd on x86-64:

#![allow(unused)]
fn main() {
// These use SIMD when available
HVec10240::bundle(&vectors);
HVec10240::cosine_similarity(&a, &b);
}

Parallel Similarity Search

Automatic via Rayon:

#![allow(unused)]
fn main() {
// Uses all cores for similarity computation
framework.probe(vector, 100).await?;
}

Batch Operations

Prefer batch over individual:

#![allow(unused)]
fn main() {
// Slower: N round trips
for (id, vec) in concepts {
    framework.inject_concept(id, vec).await?;
}

// Faster: 1 round trip
framework.inject_concepts(&concepts).await?;
}

Connection Pooling

For remote databases:

#![allow(unused)]
fn main() {
.with_connection_pool_size(20) // More connections for concurrent access
}