Agent skill

m10-performance

CRITICAL: Use for performance optimization. Triggers: performance, optimization, benchmark, profiling, flamegraph, criterion, slow, fast, allocation, cache, SIMD, make it faster, 性能优化, 基准测试

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npx add-skill https://github.com/actionbook/rust-skills/tree/main/skills/m10-performance

SKILL.md

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

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

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)
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?

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

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

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

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!

Related Skills

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

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