rust-performance

name	rust-performance
description	| Use when this capability is needed.

Rust Performance Optimization

Comprehensive guide to profiling, measuring, and optimizing Rust code. Based on "The Rust Performance Book" and real-world optimization patterns.

Quick Navigation

• references/profiling.md - Profiling tools and techniques
• references/allocations.md - Reducing heap allocations
• references/concurrency.md - Parallel and async optimization
• references/compiler_optimizations.md - Release profiles, LTO, PGO, CPU targets

Golden Rules

1. Measure first - Profile before optimizing
2. Optimize hot paths - 90% of time in 10% of code
3. Benchmark changes - Verify improvements
4. Consider tradeoffs - Speed vs memory vs complexity

Release Mode

Always benchmark in release mode with optimizations:

# Cargo.toml
[profile.release]
opt-level = 3
lto = true
codegen-units = 1
panic = "abort"

# For profiling (symbols but optimized)
[profile.release-with-debug]
inherits = "release"
debug = true

cargo build --release
cargo run --release

Quick Performance Wins

1. Use &str Instead of String

// Allocates on every call
fn greet_slow(name: String) {
    println!("Hello, {}!", name);
}

// Zero allocation
fn greet_fast(name: &str) {
    println!("Hello, {}!", name);
}

2. Pre-allocate Collections

// Reallocates as it grows
let mut v = Vec::new();
for i in 0..1000 {
    v.push(i);
}

// Single allocation
let mut v = Vec::with_capacity(1000);
for i in 0..1000 {
    v.push(i);
}

// Even better: use iterators
let v: Vec<_> = (0..1000).collect();

3. Avoid Unnecessary Clones

// Bad: clones unnecessarily
fn process(items: Vec<Item>) -> Vec<Result> {
    items.iter()
        .map(|item| item.clone())  // Don't clone if you don't need it
        .map(|item| transform(item))
        .collect()
}

// Good: take ownership or borrow
fn process(items: Vec<Item>) -> Vec<Result> {
    items.into_iter()  // Consume the Vec
        .map(transform)
        .collect()
}

4. Use Cow<str> for Maybe-Owned Strings

use std::borrow::Cow;

fn normalize(input: &str) -> Cow<'_, str> {
    if input.contains(' ') {
        Cow::Owned(input.replace(' ', "_"))
    } else {
        Cow::Borrowed(input)  // No allocation
    }
}

5. Use collect() Strategically

// Creates intermediate Vec
let sum: i32 = items.iter()
    .map(|x| x * 2)
    .collect::<Vec<_>>()  // Unnecessary
    .iter()
    .sum();

// Direct iteration
let sum: i32 = items.iter()
    .map(|x| x * 2)
    .sum();

Benchmarking

Criterion.rs

The gold standard for Rust benchmarks:

[dev-dependencies]
criterion = "0.5"

[[bench]]
name = "my_benchmark"
harness = false

// benches/my_benchmark.rs
use criterion::{black_box, criterion_group, criterion_main, Criterion};

fn fibonacci(n: u64) -> u64 {
    match n {
        0 | 1 => n,
        _ => fibonacci(n - 1) + fibonacci(n - 2),
    }
}

fn criterion_benchmark(c: &mut Criterion) {
    c.bench_function("fib 20", |b| {
        b.iter(|| fibonacci(black_box(20)))
    });
    
    // Compare implementations
    let mut group = c.benchmark_group("String Ops");
    group.bench_function("clone", |b| {
        b.iter(|| String::from("hello").clone())
    });
    group.bench_function("to_string", |b| {
        b.iter(|| "hello".to_string())
    });
    group.finish();
}

criterion_group!(benches, criterion_benchmark);
criterion_main!(benches);

cargo bench
cargo bench -- "fib"  # Run specific benchmarks

Key Points

• Use black_box() to prevent optimization
• Multiple iterations for statistical significance
• Compare baseline vs optimized
• Watch for outliers

Profiling

CPU Profiling

perf (Linux):

perf record -g --call-graph dwarf target/release/myapp
perf report

flamegraph:

cargo install flamegraph
cargo flamegraph --bin myapp
# Open flamegraph.svg in browser

samply (Cross-platform):

cargo install samply
samply record target/release/myapp

Memory Profiling

DHAT (Heap profiling):

[dependencies]
dhat = "0.3"

#[cfg(feature = "dhat")]
#[global_allocator]
static ALLOC: dhat::Alloc = dhat::Alloc;

fn main() {
    #[cfg(feature = "dhat")]
    let _profiler = dhat::Profiler::new_heap();
    
    // Your code here
}

Heaptrack (Linux):

heaptrack target/release/myapp
heaptrack --analyze heaptrack.myapp.*.zst

Finding Allocations

// Track allocations with a custom allocator
use std::alloc::{GlobalAlloc, Layout, System};
use std::sync::atomic::{AtomicUsize, Ordering};

static ALLOCATED: AtomicUsize = AtomicUsize::new(0);

struct CountingAlloc;

unsafe impl GlobalAlloc for CountingAlloc {
    unsafe fn alloc(&self, layout: Layout) -> *mut u8 {
        ALLOCATED.fetch_add(layout.size(), Ordering::SeqCst);
        System.alloc(layout)
    }
    
    unsafe fn dealloc(&self, ptr: *mut u8, layout: Layout) {
        ALLOCATED.fetch_sub(layout.size(), Ordering::SeqCst);
        System.dealloc(ptr, layout)
    }
}

#[global_allocator]
static A: CountingAlloc = CountingAlloc;

fn main() {
    let before = ALLOCATED.load(Ordering::SeqCst);
    // Code to measure
    let after = ALLOCATED.load(Ordering::SeqCst);
    println!("Allocated {} bytes", after - before);
}

Common Optimizations

String Operations

// Slow: Many allocations
let mut s = String::new();
for i in 0..100 {
    s = s + &i.to_string();  // New allocation each time
}

// Better: Single buffer
let mut s = String::with_capacity(300);
for i in 0..100 {
    use std::fmt::Write;
    write!(s, "{}", i).unwrap();
}

// Best: Join iterator
let s: String = (0..100).map(|i| i.to_string()).collect();

Hash Maps

use std::collections::HashMap;

// Pre-size when you know the count
let mut map = HashMap::with_capacity(1000);

// Use entry API (single lookup)
*map.entry(key).or_insert(0) += 1;

// Consider FxHashMap for integer keys
use rustc_hash::FxHashMap;
let mut map: FxHashMap<u64, Value> = FxHashMap::default();

Iterators vs Loops

// Iterators often optimize better
let sum: i32 = data.iter().map(|x| x * 2).filter(|x| *x > 10).sum();

// But sometimes explicit loops are clearer and equally fast
let mut sum = 0;
for x in &data {
    let doubled = x * 2;
    if doubled > 10 {
        sum += doubled;
    }
}

Avoid Bounds Checking

// Bounds check on each access
for i in 0..v.len() {
    process(v[i]);
}

// No bounds checks (iterator)
for item in &v {
    process(*item);
}

// Unsafe: skip bounds check (only when proven safe!)
unsafe {
    for i in 0..v.len() {
        process(*v.get_unchecked(i));
    }
}

Stack vs Heap

// Heap allocated (Box)
let data = Box::new([0u8; 1024]);

// Stack allocated (if small enough)
let data = [0u8; 1024];

// Use arrays for fixed-size, known at compile time
// Use Vec for dynamic size

Async Performance

// Avoid holding locks across awaits
let data = {
    let guard = mutex.lock().await;
    guard.clone()  // Clone and release lock
}; // Lock released here
process(data).await;  // Await without lock

// Use tokio::spawn for CPU-bound work
let result = tokio::task::spawn_blocking(|| {
    expensive_computation()
}).await?;

// Buffer I/O
use tokio::io::BufReader;
let reader = BufReader::new(file);

Data Structure Choice

Use Case	Data Structure	Why
Sequential access	Vec<T>	Cache-friendly
Key-value lookup	HashMap / FxHashMap	O(1) average
Sorted + lookup	BTreeMap	O(log n), ordered
Unique elements	HashSet	O(1) contains
FIFO queue	VecDeque	O(1) push/pop both ends
Small fixed set	ArrayVec / SmallVec	No heap allocation
Bit flags	bitflags	Memory efficient

Compiler Hints

// Likely/unlikely branches (nightly)
#![feature(core_intrinsics)]
use std::intrinsics::{likely, unlikely};

if unlikely(error_condition) {
    handle_error();
}

// Inline hints
#[inline]           // Suggest inlining
#[inline(always)]   // Force inlining
#[inline(never)]    // Prevent inlining
#[cold]             // Rarely called (optimize for space)
fn rarely_used() { ... }

// Target-specific optimization
#[cfg(target_feature = "avx2")]
fn simd_process(data: &[f32]) { ... }

Arena Allocators

For batch allocations freed together (parsers, request processing):

use bumpalo::Bump;

// All allocations from one arena — freed together when arena drops
fn parse<'a>(input: &str, arena: &'a Bump) -> Vec<&'a Node> {
    tokenize(input).map(|t| arena.alloc(Node::new(t))).collect()
}

// Per-request: allocate freely, free everything at once
async fn handle(req: Request) -> Response {
    let arena = Bump::new();
    let parsed = parse(&req.body, &arena);
    generate_response(parsed)
} // arena dropped: all parsed nodes freed in one dealloc

SmallVec — Inline Small Collections

use smallvec::SmallVec;

// Up to 4 items stored inline (stack), spills to heap beyond that
let mut tags: SmallVec<[&str; 4]> = SmallVec::new();
tags.push("performance");
tags.push("rust");
// No heap allocation for ≤ 4 items

Write! Over format!

use std::fmt::Write;

// Bad: allocates a new String every time
let s = format!("key={} val={}", key, val);

// Good: write into pre-allocated buffer, clear and reuse
let mut buf = String::with_capacity(128);
for (key, val) in &map {
    buf.clear();
    write!(buf, "key={key} val={val}").unwrap();
    send(&buf);
}

Entry API for HashMap

// Bad: two lookups
if let Some(v) = map.get_mut(&k) { *v += 1; } else { map.insert(k, 1); }

// Good: single lookup
*map.entry(k).or_insert(0) += 1;

Struct Size — Keep It Small

use std::mem::size_of;

// Catch size regressions at compile time
const _: () = assert!(size_of::<MyEvent>() <= 64);

// Large enum variant → box it
enum Message {
    Ping,
    Text(String),
    // HugeThing(VeryLargeStruct), // Makes ALL variants huge!
    HugeThing(Box<VeryLargeStruct>), // Pointer-sized, heap only when needed
}

Advanced Compiler Optimizations

1. Compile with CPU Native Target

In release mode, use target-cpu=native to allow the compiler to generate machine instructions specifically optimized for the host CPU (enabling AVX, SSE4, etc.).

RUSTFLAGS="-C target-cpu=native" cargo build --release

2. Instruct the Compiler on Cold Paths

Mark rarely executed logic (like initialization, configuration loading, or panic routes) with the #[cold] attribute. This instructs the compiler to optimize the layout of code instructions to prioritize hot execution flows.

#[cold]
fn parse_dev_configurations() {
    // rarely called; keeps instruction caches clean
}

3. Explicit SIMD Vectorization

For high-performance numerical routines, consider using std::simd to process multiple elements in parallel.

#![feature(portable_simd)]
use std::simd::f32x4;

pub fn add_vectors(a: &[f32; 4], b: &[f32; 4]) -> [f32; 4] {
    let sa = f32x4::from_slice(a);
    let sb = f32x4::from_slice(b);
    let sum = sa + sb;
    let mut out = [0.0; 4];
    sum.copy_to_slice(&mut out);
    out
}

Quick Checklist

• Profiled to find actual bottleneck?
• Running in release mode?
• Using &str instead of String where possible?
• Pre-allocating collections?
• Avoiding unnecessary clones?
• Using iterators over index loops?
• Using appropriate data structures?
• Benchmarked before and after?
• No intermediate .collect() in hot paths?
• Using entry API for HashMap inserts?
• Considered SmallVec or arena for short-lived allocations?

References

• The Rust Performance Book
• bumpalo — arena allocator
• smallvec
• cargo-flamegraph
• samply — cross-platform profiler

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Source: adxptived/Rust-Skills — distributed by TomeVault.