M10 Performance

by actionbook5c40d3ad7851No license1.5K starsListed Oct 8, 2026Updated Oct 8, 2026Repository updated 6 weeks ago

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

Instructions onlySoftware Development

AI-generated overview

Guides Rust performance optimization: measure bottlenecks first, then choose data structures, allocation and cache strategies.

What it does: This skill provides a decision framework for optimizing software performance, centered on profiling before changing code. It maps performance goals such as reducing allocations, improving cache locality, parallelizing, avoiding copies and reducing indirection to concrete implementation choices. It lists profiling and benchmarking tools, an optimization priority order, common techniques and anti-patterns, and points to related skills for ownership, concurrency and resource management.
When to use it: Use it when code is too slow and you need to decide what to optimize and whether optimization is worthwhile. It fits profiling, benchmarking and allocation or cache tuning work, especially in Rust projects.
Requirements: No scripts or tooling are bundled; it is instructions only. The referenced tools (cargo bench, criterion, perf, flamegraph, heaptrack, valgrind) are optional and must be available in the environment if used.

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:

Have you measured?
- Profile first → flamegraph, perf
- Benchmark → criterion, cargo bench
- Identify actual hotspots
What's the priority?
- Algorithm (10x-1000x improvement)
- Data structure (2x-10x)
- Allocation (2x-5x)
- Cache (1.5x-3x)
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)

"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

"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)

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

Source and attribution

Source:actionbook/rust-skillsinskills/m10-performanceat commit5c40d3a

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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?

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

M10 Performance

Performance Optimization

Core Question

Performance Decision → Implementation

Thinking Prompt

Trace Up ↑

Trace Down ↓

Quick Reference

Optimization Priority

Common Techniques

Common Mistakes

Anti-Patterns

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Source and attribution

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