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?

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Performance Decision → Implementation

Goal	Design Choice	Implementation
Reduce allocations	Pre-allocate, reuse	with_capacity, object pools
Improve cache	Contiguous data	Vec, SmallVec
Parallelize	Data parallelism	rayon, threads
Avoid copies	Zero-copy	References, Cow<T>
Reduce indirection	Inline data	smallvec, arrays

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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

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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)

Question	Trace To	Ask
Latency requirements	domain-*	What's acceptable response time?
Throughput needs	domain-*	How many requests per second?
Memory constraints	domain-*	What's the memory budget?

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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

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Quick Reference

Tool	Purpose
cargo bench	Micro-benchmarks
criterion	Statistical benchmarks
perf / flamegraph	CPU profiling
heaptrack	Allocation tracking
valgrind / cachegrind	Cache analysis

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

Technique	When	How
Pre-allocation	Known size	Vec::with_capacity(n)
Avoid cloning	Hot paths	Use references or Cow<T>
Batch operations	Many small ops	Collect then process
SmallVec	Usually small	smallvec::SmallVec<[T; N]>
Inline buffers	Fixed-size data	Arrays over Vec

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Common Mistakes

Mistake	Why Wrong	Better
Optimize without profiling	Wrong target	Profile first
Benchmark in debug mode	Meaningless	Always --release
Use LinkedList	Cache unfriendly	Vec or VecDeque
Hidden .clone()	Unnecessary allocs	Use references
Premature optimization	Wasted effort	Make it work first

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Anti-Patterns

Anti-Pattern	Why Bad	Better
Clone to avoid lifetimes	Performance cost	Proper ownership
Box everything	Indirection cost	Stack when possible
HashMap for small sets	Overhead	Vec with linear search
String concat in loop	O(n^2)	String::with_capacity or format!

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Related Skills

When	See
Reducing clones	m01-ownership
Concurrency options	m07-concurrency
Smart pointer choice	m02-resource
Domain requirements	domain-*