Distance Functions: Optimization Roadmap

Date: 2025-10-30 Status: ✅ Phase 2 Complete - 15-39% Improvement Achieved Current Performance: 94ns (short), 374-492ns (medium)

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Executive Summary

Based on comprehensive benchmarking, profiling, and research, this document outlines the optimization strategy for Levenshtein distance functions. Priority is given to high-impact, low-effort improvements.

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Phase 2: Quick Wins (Estimated: 1-2 days, 10-20% speedup)

1. Use FxHash for Cache Keys ⭐⭐⭐⭐⭐

Current: Default hasher (SipHash for security) Proposed: FxHash (faster for small keys)

Rationale:

• SipHash: ~20-30ns per hash (DoS-resistant but slow)
• FxHash: ~5-10ns per hash (non-cryptographic, fast)
• Cache keys are trusted internal data (no DoS risk)

Implementation:

use std::hash::BuildHasherDefault;
use rustc_hash::FxHasher;

type FastHashMap<K, V> = HashMap<K, V, BuildHasherDefault<FxHasher>>;

Expected gain: 10-15ns per cached operation (~10-15% for recursive)

Files to modify:

• src/distance/mod.rs: Change HashMap to use FxHasher
• Cargo.toml: Add rustc-hash = "1.1" dependency

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2. Inline Hot Path Functions ⭐⭐⭐⭐

Current: Compiler decides inlining Proposed: Force inline for small, hot functions

Targets:

#[inline(always)]
fn substring_from(s: &str, char_offset: usize) -> &str { ... }

#[inline(always)]
fn SymmetricPair::new(a: &str, b: &str) -> Self { ... }

Rationale:

• These functions are called in every recursive step
• Very small (< 10 instructions)
• Inlining eliminates call overhead (~2-3ns each)

Expected gain: 5-10ns per operation (~5-10%)

Files to modify:

• src/distance/mod.rs: Add #[inline(always)] annotations

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3. Use SmallVec for Character Buffers ⭐⭐⭐⭐

Current: Vec<char> allocation for each string Proposed: SmallVec<[char; 32]> (stack allocation for small strings)

Rationale:

• Most strings are < 32 chars (fits on stack)
• Avoids heap allocation (~20-30ns overhead)
• Zero cost for short strings, small overhead for long strings

Implementation:

use smallvec::SmallVec;

// Instead of:
let source_chars: Vec<char> = source.chars().collect();

// Use:
let source_chars: SmallVec<[char; 32]> = source.chars().collect();

Expected gain: 20-30ns for short strings (~20-30%)

Note: smallvec is already a dependency!

Files to modify:

• src/distance/mod.rs: Replace Vec<char> with SmallVec<[char; 32]>

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4. Enable Common Suffix Elimination ⭐⭐⭐

Current: Code exists but commented out Proposed: Integrate strip_common_affixes() into recursive functions

Rationale:

• Already implemented (no new code needed)
• Significant speedup for strings with common suffixes
• Low risk (already tested in C++ version)

Expected gain: 10-50% for strings with common suffixes (variable)

Files to modify:

• src/distance/mod.rs: Uncomment and integrate suffix stripping

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Phase 3: SIMD Vectorization (Estimated: 3-5 days, 2-4x speedup for medium/long strings)

Overview

Target: Inner loop of DP matrix computation Approach: Compute 4-16 cells in parallel using SIMD instructions

Implementation Strategy

Step 1: Research and Prototype (1 day)

Options:

1. std::simd (Rust standard library, nightly)

   • Portable across platforms
   • Requires nightly compiler
   • Clean API

2. packed_simd2 (crate, stable)

   • Works on stable Rust
   • Similar API to std::simd
   • Well-maintained

3. Raw intrinsics (std::arch)

   • Maximum control
   • Platform-specific (AVX2, SSE, NEON)
   • Complex, error-prone

Recommendation: Start with packed_simd2 (stable + portable)

Step 2: Vectorize Inner Loop (2-3 days)

Current scalar code:

for j in 1..=n {
    let cost = if source_chars[i - 1] == target_chars[j - 1] { 0 } else { 1 };
    curr_row[j] = (prev_row[j] + 1)              // deletion
        .min(curr_row[j - 1] + 1)                // insertion
        .min(prev_row[j - 1] + cost);            // substitution
}

Vectorized code (process 8 cells at once with AVX2):

use packed_simd::u32x8;

// Process 8 cells in parallel
for j in (1..=n).step_by(8) {
    let prev = u32x8::from_slice_unaligned(&prev_row[j..j+8]);
    let curr = u32x8::from_slice_unaligned(&curr_row[j-1..j+7]);
    let diag = u32x8::from_slice_unaligned(&prev_row[j-1..j+7]);

    let cost_vec = /* compute match/mismatch cost vector */;

    // Parallel min operations
    let result = (prev + 1).min(curr + 1).min(diag + cost_vec);

    result.write_to_slice_unaligned(&mut curr_row[j..j+8]);
}

Challenges:

• Character comparison vectorization
• Edge case handling (string ends)
• Alignment and padding

Expected speedup: 2-4x for strings > 20 chars

Step 3: Benchmark and Validate (1 day)

• Run comprehensive benchmarks
• Verify correctness with property tests
• Compare SIMD vs scalar performance
• Add feature flag for SIMD (optional opt-in)

Feature flag approach:

[features]
simd = ["packed_simd2"]

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Phase 4: Cache Optimization (Estimated: 2-3 days, improves memory usage)

1. Implement LRU Eviction ⭐⭐⭐⭐

Problem: Unbounded cache growth Solution: Least Recently Used (LRU) eviction policy

Implementation options:

1. lru crate (simple, battle-tested)

use lru::LruCache;

pub struct MemoCache {
    cache: RwLock<LruCache<SymmetricPair, usize>>,
}

2. Custom LRU (more control)

• HashMap + doubly-linked list
• More complex but more flexible

Recommendation: Use lru crate

Expected benefit: Bounded memory usage, no performance regression

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2. Add Cache Statistics ⭐⭐⭐

Goal: Monitor cache effectiveness

Metrics to track:

• Hit rate (cache hits / total queries)
• Miss rate
• Eviction count
• Current size

Implementation:

pub struct CacheStats {
    pub hits: AtomicUsize,
    pub misses: AtomicUsize,
    pub evictions: AtomicUsize,
}

impl MemoCache {
    pub fn stats(&self) -> CacheStats { ... }
    pub fn hit_rate(&self) -> f64 { ... }
}

Usage:

let cache = create_memo_cache();
// ... perform queries ...
println!("Hit rate: {:.2}%", cache.hit_rate() * 100.0);

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3. Tune Cache Size ⭐⭐

Current: Unbounded (grows indefinitely) Proposed: Configurable max size (default: 1000 entries)

Implementation:

pub fn create_memo_cache_with_capacity(capacity: usize) -> MemoCache {
    MemoCache::with_capacity(capacity)
}

Tuning guidance:

• Small (100): Low memory, higher miss rate
• Medium (1000): Balanced (recommended default)
• Large (10000): High memory, very high hit rate

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Phase 5: Advanced Optimizations (Estimated: 1-2 weeks, variable gains)

1. Profile-Guided Optimization (PGO) ⭐⭐⭐

Approach: Use actual usage patterns to guide compiler optimizations

Steps:

1. Build with instrumentation: cargo pgo build
2. Run representative workload to collect profiles
3. Rebuild with PGO data: cargo pgo optimize

Expected gain: 5-15% across the board

Effort: Low (mostly automated)

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2. Algorithmic Alternatives ⭐⭐

Ukkonen's Algorithm:

• Optimal for small edit distances (k << min(m,n))
• Computes only "band" around diagonal
• Complexity: O(k × min(m,n)) instead of O(m×n)

When beneficial:

• Queries with small max_distance (k ≤ 5)
• Automaton can guide k parameter

Implementation:

pub fn ukkonen_distance(source: &str, target: &str, max_k: usize) -> Option<usize>

Expected gain: 2-10x for small k values

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3. Block Processing for Long Strings ⭐

Problem: Cache locality degrades for very long strings Solution: Process DP matrix in blocks (cache-oblivious algorithm)

Only beneficial for: Strings > 1000 chars (rare in fuzzy search)

Effort: High, benefit: Marginal for typical use case

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Phase 6: Cross-Validation (Estimated: 3-4 days)

Build Test Harness

Goal: Verify exact equivalence with C++ implementation

Approach:

1. Build C++ liblevenshtein with test interface
2. Generate comprehensive test corpus (10k string pairs)
3. Run both implementations, compare results
4. Document any differences

Test corpus:

• Random strings (various lengths)
• Common prefixes/suffixes
• Unicode characters
• Edge cases

Validation criteria:

• 100% exact match on all test cases
• Performance within 20% (expected)

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Optimization Priority Matrix

Optimization	Effort	Expected Gain	Priority	Phase
FxHash	1 hour	10-15% recursive	⭐⭐⭐⭐⭐	2
SmallVec	2 hours	20-30% short strings	⭐⭐⭐⭐⭐	2
Inline annotations	1 hour	5-10%	⭐⭐⭐⭐	2
Suffix elimination	2 hours	10-50% (variable)	⭐⭐⭐⭐	2
SIMD vectorization	3-5 days	2-4x medium/long	⭐⭐⭐⭐	3
LRU eviction	1 day	Better memory	⭐⭐⭐⭐	4
Cache statistics	4 hours	Monitoring	⭐⭐⭐	4
PGO	1 day	5-15%	⭐⭐⭐	5
Ukkonen's	3-5 days	2-10x (k small)	⭐⭐	5
Block processing	1 week	Marginal	⭐	5

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Realistic Timeline

Week 1: Quick Wins

• Day 1-2: Implement FxHash, SmallVec, inlining, suffix elimination
• Day 3: Benchmark and validate
• Day 4-5: Tune and document

Deliverable: 30-50% speedup for typical workloads

Week 2: SIMD

• Day 1: Research and prototype
• Day 2-4: Implement vectorized inner loop
• Day 5: Benchmark, validate, document

Deliverable: 2-4x speedup for medium/long strings

Week 3: Cache + Advanced

• Day 1-2: LRU eviction and statistics
• Day 3: PGO optimization
• Day 4-5: Documentation and final benchmarks

Deliverable: Production-ready optimized implementation

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Expected Final Performance

Current (Baseline)

Short strings:   99 ns
Medium strings: 740 ns
Long strings:    ~5 µs

After Quick Wins (Phase 2)

Short strings:   70 ns  (-30%)
Medium strings: 520 ns  (-30%)
Long strings:    ~3.5 µs (-30%)

After SIMD (Phase 3)

Short strings:   70 ns  (no change, too small for SIMD)
Medium strings: 200 ns  (-70% from baseline!)
Long strings:    ~1 µs   (-80% from baseline!)

After Full Optimization

Short strings:   60 ns  (-40% from baseline)
Medium strings: 150 ns  (-80% from baseline)
Long strings:   ~800 ns  (-84% from baseline)

Target: Competitive with hand-tuned assembly implementations!

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Risk Assessment

Low Risk (Safe to implement)

• ✅ FxHash: Drop-in replacement
• ✅ SmallVec: Already tested extensively
• ✅ Inline annotations: Compiler hint only
• ✅ Suffix elimination: Already in C++

Medium Risk (Needs careful validation)

• ⚠️ SIMD: Requires extensive testing
• ⚠️ LRU eviction: Could impact performance if not done right

High Risk (Proceed with caution)

• ❌ Algorithmic changes: Could break existing behavior
• ❌ Unsafe code: Only if absolutely necessary (avoid!)

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Success Criteria

Performance

• [ ] 30% speedup from quick wins
• [ ] 2-4x speedup for medium/long strings (SIMD)
• [ ] No regression for short strings
• [ ] Consistent performance across platforms

Quality

• [ ] All tests still passing
• [ ] No new bugs introduced
• [ ] Property tests validate correctness
• [ ] Cross-validation with C++ passes

Documentation

• [ ] Benchmark results documented
• [ ] Optimization techniques explained
• [ ] Usage recommendations updated
• [ ] Performance characteristics well-understood

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Conclusion

The optimization roadmap is clear and achievable:

1. Phase 2 (Quick Wins): Low-hanging fruit, 30-50% improvement
2. Phase 3 (SIMD): Major speedup for medium/long strings
3. Phase 4 (Cache): Better memory usage
4. Phase 5 (Advanced): Marginal gains, optional

Recommended approach: Implement Phase 2 first, then reassess whether Phase 3-5 are needed based on actual performance requirements.

Current status: Production-ready baseline established. Ready to optimize! 🚀

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Generated: 2025-10-30 Based on: Comprehensive benchmarking, profiling, and research