Rust Performance Analyzer

Expert skill for analyzing and optimizing Rust application performance.

When to Use

Activate this skill when the user:

• Mentions "slow", "performance", "optimize", "bottleneck"
• Asks about memory usage or allocation patterns
• Wants to profile or benchmark code
• Reports laggy UI or frame drops
• Discusses cache efficiency or data layout

Analysis Process

1. Identify Hot Paths

Look for:

• Frequent allocations (Vec::new(), String::new() in loops)
• Unnecessary cloning (.clone() where borrow would work)
• Hash map lookups in tight loops
• Box/Arc indirection overhead

2. Memory Layout Analysis

Check:

• Struct field ordering (largest first for alignment)
• Use of #[repr(C)] where needed
• Option niche optimization usage
• Cache line friendliness

3. Concurrency Patterns

Evaluate:

• Lock contention (Mutex, RwLock usage)
• parking_lot vs std sync primitives
• Atomic operations appropriateness
• Send/Sync bounds efficiency

4. FLUI-Specific Patterns

Focus on:

• Signal update frequency
• Rebuild triggering patterns
• Layout phase efficiency
• Paint layer caching

Optimization Techniques

Allocation Reduction

// Bad: Allocates on every call
fn process(items: &[Item]) -> Vec<ProcessedItem> {
    items.iter().map(|i| process_one(i)).collect()
}

// Good: Reuse buffer
fn process_into(items: &[Item], buffer: &mut Vec<ProcessedItem>) {
    buffer.clear();
    buffer.extend(items.iter().map(|i| process_one(i)));
}

Clone Elimination

// Bad: Unnecessary clone
let data = self.data.clone();
process(&data);

// Good: Borrow directly
process(&self.data);

Interior Mutability

// Use RefCell/Cell for single-threaded
// Use parking_lot::{Mutex, RwLock} for multi-threaded
// Use atomics for simple counters/flags

Profiling Commands

# Build with debug symbols
cargo build --release

# CPU profiling (requires cargo-flamegraph)
cargo flamegraph --example <name>

# Memory profiling
RUSTFLAGS="-Z sanitizer=address" cargo +nightly run --example <name>

# Benchmarking
cargo bench

Output Format

Provide:

1. Identified Issues: List of performance problems found
2. Impact Assessment: Severity (High/Medium/Low)
3. Optimization Suggestions: Specific code changes
4. Metrics: Before/after estimates where possible