Performance Optimization

Layer 2: Design Choices

Core Question

What's the bottleneck, and is optimization worth it?

Before optimizing:

• Have you measured? (Don't guess)
• What's the acceptable performance?
• Will optimization add complexity?

Performance Decision → Implementation

Thinking Prompt

Before optimizing:

1. Have you measured?
   • Profile first → flamegraph, perf
   • Benchmark → criterion, cargo bench
   • Identify actual hotspots
2. What's the priority?
   • Algorithm (10x-1000x improvement)
   • Data structure (2x-10x)
   • Allocation (2x-5x)
   • Cache (1.5x-3x)
3. What's the trade-off?
   • Complexity vs speed
   • Memory vs CPU
   • Latency vs throughput

Trace Up ↑

To domain constraints (Layer 3):

"How fast does this need to be?"
    ↑ Ask: What's the performance SLA?
    ↑ Check: domain-* (latency requirements)
    ↑ Check: Business requirements (acceptable response time)

Trace Down ↓

To implementation (Layer 1):

"Need to reduce allocations"
    ↓ m01-ownership: Use references, avoid clone
    ↓ m02-resource: Pre-allocate with_capacity

"Need to parallelize"
    ↓ m07-concurrency: Choose rayon or threads
    ↓ m07-concurrency: Consider async for I/O-bound

"Need cache efficiency"
    ↓ Data layout: Prefer Vec over HashMap when possible
    ↓ Access patterns: Sequential over random access

Quick Reference

Optimization Priority

1. Algorithm choice     (10x - 1000x)
2. Data structure       (2x - 10x)
3. Allocation reduction (2x - 5x)
4. Cache optimization   (1.5x - 3x)
5. SIMD/Parallelism     (2x - 8x)

Common Techniques

Common Mistakes

Anti-Patterns

Related Skills