┌─────────────────────────────────────────────────────────────┐
│                   OPTIMIZATION WORKFLOW                      │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  1. MEASURE FIRST                                           │
│     └── Profile before changing anything                   │
│     └── Use cargo flamegraph, perf, or heaptrack           │
│     └── Identify actual bottlenecks (don't guess!)         │
│                                                             │
│  2. CHECK BUILD SETTINGS                                    │
│     └── Release mode? (10-100x vs debug)                   │
│     └── LTO enabled? (5-20% improvement)                   │
│     └── Target CPU? (10-30% for SIMD)                      │
│                                                             │
│  3. FIX ALGORITHMIC ISSUES                                  │
│     └── O(n²) → O(n log n) matters more than micro-opts   │
│     └── Check data structure choices                       │
│     └── Avoid unnecessary work                             │
│                                                             │
│  4. REDUCE ALLOCATIONS                                      │
│     └── Pre-size collections (with_capacity)               │
│     └── Reuse buffers (clear + reuse)                      │
│     └── Avoid cloning (borrow instead)                     │
│                                                             │
│  5. OPTIMIZE HOT LOOPS                                      │
│     └── Iterators over indices                             │
│     └── Reduce lock scope                                  │
│     └── Batch I/O operations                               │
│                                                             │
│  6. MEASURE AGAIN                                           │
│     └── Verify improvement with benchmarks                 │
│     └── Check for regressions elsewhere                    │
│     └── Document the optimization                          │
│                                                             │
└─────────────────────────────────────────────────────────────┘

# CPU profiling (Linux)
cargo flamegraph --bin myapp
perf record -g ./target/release/myapp && perf report

# Memory profiling
heaptrack ./target/release/myapp && heaptrack_gui heaptrack.myapp.*.gz
DHAT_LOG_FILE=dhat.out cargo run --release && dh_view.py dhat.out

# Benchmark
cargo bench                          # All benchmarks
cargo bench hot_function             # Specific benchmark

# Check allocations
MALLOC_TRACE=/tmp/mtrace.log ./target/release/myapp
mtrace ./target/release/myapp /tmp/mtrace.log

# Assembly inspection
cargo asm my_crate::hot_function --rust

# syscall count
strace -c ./target/release/myapp 2>&1 | head -20

Is it running in debug mode?
├── YES → build-release-profile (10-100x speedup)
└── NO
    │
    Where does flamegraph show time?
    ├── malloc/free → alloc-* rules (with_capacity, reuse buffers)
    ├── Mutex::lock → sync-* rules (RwLock, atomics, shorter scope)
    ├── read/write syscalls → io-* rules (BufReader/BufWriter)
    ├── clone/drop → alloc-avoid-clone, use references
    └── Your code → iter-* rules, algorithm improvements

1. Enable LTO: build-enable-lto (10-20% smaller)
2. Set opt-level = 'z': build-opt-level (optimizes for size)
3. panic = 'abort': build-panic-abort (removes unwinding code)
4. Strip symbols: strip = true in Cargo.toml
5. Remove debug info: debug = 0

1. Pre-size collections: alloc-*-with-capacity
2. Reuse allocations: alloc-reuse-buffers
3. Avoid cloning: alloc-avoid-clone
4. Use slices in APIs: alloc-use-slices-in-apis
5. Consider arena allocators: bumpalo crate

1. Profile: lock_api::ReentrantMutex or parking_lot profiling
2. Reduce lock scope: sync-keep-lock-scope-short
3. Read-heavy? → sync-use-rwlock
4. Simple counters? → sync-use-atomics
5. Message passing? → sync-use-channels
6. Thread-local + periodic flush for stats

1. Wrap in BufReader/BufWriter: io-use-bufreader, io-use-bufwriter
2. Flush before returning: io-flush-bufwriter (data loss prevention!)
3. Reuse line buffer: io-read-line-with-bufread
4. Consider mmap for random access: memmap2 crate

// BAD: Blocks the async runtime
async fn process(data: &[u8]) -> Result<Hash> {
    let hash = expensive_hash(data);  // CPU-bound, blocks executor!
    Ok(hash)
}

// GOOD: Offload to blocking thread pool
async fn process(data: Vec<u8>) -> Result<Hash> {
    tokio::task::spawn_blocking(move || expensive_hash(&data)).await?
}

Do you know the size?
├── YES, exact → with_capacity(exact)
├── YES, approximate → with_capacity(estimate)
└── NO
    │
    Will it grow frequently?
    ├── YES → Start bigger or use reserve()
    └── NO → Vec::new() is fine

Is it a simple counter/flag?
├── YES → Atomics (20x faster)
└── NO
    │
    What's the read/write ratio?
    ├── Mostly reads (>90%) → RwLock
    ├── Mostly writes → Mutex
    └── Mixed → Mutex (simpler)

    Consider: parking_lot > std for all of these

Did you profile and find bounds checks are the bottleneck?
├── NO → Don't use it
└── YES
    │
    Did you check if LLVM already removed the bounds check?
    ├── NO → Check assembly first (cargo asm)
    └── YES, still there
        │
        Can you use iterators instead?
        ├── YES → Use iterators (same speed, safe)
        └── NO → get_unchecked with documented invariants