lfqueue — Lock-Free Multi-Producer Multi-Consumer Queue

A bounded, lock-free MPMC (multi-producer multi-consumer) queue implemented in Rust using atomic operations and explicit memory ordering. Benchmarked against a mutex-protected queue across varying thread counts and contention levels, with discussion of the correctness reasoning required for lock-free design.

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Overview

Most concurrent data structures are protected by a mutex: a thread acquires the lock, performs its operation, and releases it. This is simple to reason about but has a fundamental cost — under contention, threads block and the OS scheduler must context-switch between them, and even without contention, acquiring and releasing a lock involves atomic operations and potential cache coherency traffic.

A lock-free data structure guarantees that at least one thread makes progress in a bounded number of steps, regardless of what other threads are doing — including if another thread is suspended mid-operation. No thread ever blocks waiting for another. This is achieved using atomic compare-and-swap (CAS) operations instead of locks, combined with careful memory ordering to ensure operations on different cores become visible in a consistent order.

lfqueue implements a bounded ring-buffer queue supporting multiple concurrent producers (push) and multiple concurrent consumers (pop), with no mutex anywhere in the implementation. The unsafe, manually-managed slot storage (UnsafeCell<MaybeUninit<T>>) is the same kind of code C++ writes by default — Rust just requires it to be explicitly marked and justified with a // SAFETY: comment at every unsafe block.

Why this problem

Lock-free programming is one of the most rigorous correctness exercises in systems programming — bugs are subtle, non-deterministic, and often invisible in testing but catastrophic in production under specific timing conditions. Building one correctly (and being able to explain why it's correct) signals a level of comfort with concurrent correctness reasoning that few candidates can demonstrate. It is also a direct, practical complement to the memory allocator project: high-performance allocators and runtime systems use lock-free queues for work-stealing schedulers, thread pools, and inter-thread message passing.

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Architecture

                  head (next slot to pop)         tail (next slot to push)
                       │                                │
                       ▼                                ▼
        ┌────┬────┬────┬────┬────┬────┬────┬────┬────┐
slots:  │ E  │ E  │ F  │ F  │ F  │ E  │ E  │ E  │ E  │   E = empty, F = full
        └────┴────┴────┴────┴────┴────┴────┴────┴────┘
          0    1    2    3    4    5    6    7    8

Producers CAS-advance tail and write into the claimed slot.
Consumers CAS-advance head and read from the claimed slot.
Per-slot sequence numbers (not shown) coordinate producer/consumer handoff
without a separate "full/empty" flag race.

The queue is a fixed-size array of slots (capacity is a power of two for fast modular indexing via bitmasking). Each slot carries a sequence number in addition to its data — this is the key mechanism that makes the design correct (see below), based on the design used in Dmitry Vyukov's bounded MPMC queue.

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Key Concepts

1. Compare-and-Swap (CAS)

CAS is the fundamental primitive: compare_exchange(expected, desired) atomically checks whether a memory location currently holds expected; if so, it writes desired and returns Ok; otherwise it returns Err with the actual current value.

use std::sync::atomic::{AtomicUsize, Ordering};

let tail = AtomicUsize::new(0);

let mut pos = tail.load(Ordering::Relaxed);
while tail
    .compare_exchange_weak(pos, pos + 1, Ordering::Relaxed, Ordering::Relaxed)
    .is_err()
{
    pos = tail.load(Ordering::Relaxed); // refresh and retry
}
// This thread now "owns" slot `pos` for writing

The loop is the standard CAS retry pattern: if another thread won the race and advanced tail first, this thread's CAS fails, pos is refreshed to the new value, and it retries with the new claim. No thread ever blocks — a failed CAS means "someone else made progress," which satisfies the lock-free progress guarantee.

compare_exchange_weak vs compare_exchange: weak may fail spuriously (return Err even when the value matches) on some architectures due to how LL/SC (load-linked/store-conditional) instructions work, but is cheaper in a retry loop. compare_exchange (the "strong" form) never fails spuriously but may cost more per call. Inside a loop, weak is preferred — this is identical to the compare_exchange_weak/compare_exchange_strong distinction in C++.

2. The ABA Problem and Per-Slot Sequence Numbers

A naive lock-free queue using only head/tail indices is vulnerable to the ABA problem: thread A reads a value X at location L, gets preempted; thread B changes L to Y and back to X; thread A resumes and its CAS succeeds because L still equals X — but the underlying state has changed in ways thread A's logic didn't account for.

lfqueue avoids ABA by giving each slot a sequence number that increments every time the slot is reused:

struct Slot<T> {
    sequence: AtomicUsize,
    data: UnsafeCell<MaybeUninit<T>>,
}

A producer claiming slot i for the n-th time around the ring expects sequence == n. After writing, it sets sequence = n + 1, signaling to a consumer that the slot is ready to read. A consumer expects sequence == n + 1 before reading, and sets sequence = n + capacity after consuming, signaling the slot is ready for the next producer pass. This turns a 2-state full/empty flag into a monotonically increasing counter, eliminating the ABA ambiguity entirely — the sequence number encodes which lap around the ring the slot is on, not just its current state.

The slot's data lives in UnsafeCell<MaybeUninit<T>> rather than a plain T because the slot has no valid value until a producer writes one, and Rust's aliasing rules require UnsafeCell for any memory that's mutated through a shared reference (&MpmcQueue<T> — push/pop deliberately take &self, not &mut self, since multiple producers/consumers call them concurrently).

3. Memory Ordering

Rust's std::sync::atomic::Ordering controls what guarantees an atomic operation makes about the visibility and ordering of other memory operations around it — this is where most lock-free bugs live, because the code can be "correct" on x86 (which has strong default ordering) and fail on ARM (which does not). Rust's memory model is deliberately identical to C++11's (std::memory_order), so the same reasoning transfers directly.

Order	Guarantee	Used for
Relaxed	Atomicity only — no ordering with other memory ops	Counters with no dependent data
Acquire	No subsequent read/write can be reordered before this load	Reading a slot's sequence before reading its data
Release	No preceding read/write can be reordered after this store	Publishing a slot's data before updating its sequence
AcqRel	Both acquire and release semantics	CAS operations that both read and write
SeqCst	Total global ordering across all threads (strongest, most expensive)	Used only where reasoning requires it

The critical pattern in lfqueue is release-acquire pairing: a producer writes the slot's data, then performs a Release store to the sequence number. A consumer performs an Acquire load of the sequence number, then reads the data. The acquire/release pair guarantees that if the consumer observes the updated sequence number, it is also guaranteed to observe the data write that happened-before it — without this pairing, the consumer could read stale or partially-written data even though the sequence number appears updated, because without ordering constraints, the compiler or CPU may reorder the two stores.

// Producer
unsafe { (*slot.data.get()).write(value); }            // (1) plain write
slot.sequence.store(pos + 1, Ordering::Release);        // (2) release

// Consumer
let seq = slot.sequence.load(Ordering::Acquire);        // (3) acquire
if seq == expected {
    let value = unsafe { (*slot.data.get()).assume_init_read() }; // (4) plain read
}

The release at (2) and acquire at (3) form a synchronizes-with relationship: if (3) observes the value written by (2), then (1) is guaranteed visible to (4). This is the mechanism — not a global lock — that makes the handoff safe.

4. False Sharing and Cache Line Padding

If head and tail (each updated by different sets of threads — consumers and producers respectively) sit on the same cache line, every producer update invalidates the cache line for consumers and vice versa, even though they touch logically independent data. This is false sharing, and it can degrade throughput by an order of magnitude under contention.

lfqueue pads head and tail to separate 64-byte cache lines using #[repr(align(64))]:

#[repr(align(64))]
struct CachePadded<T> {
    value: T,
}

This is a small code change with a measurable effect — the benchmarks section quantifies it.

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Design Decisions and Tradeoffs

Bounded vs unbounded queue

lfqueue is bounded (fixed capacity, set at construction). Unbounded lock-free queues exist (e.g., the Michael-Scott queue using a linked list with CAS-based node insertion) but introduce a harder problem: safe memory reclamation. When a node is dequeued from a lock-free linked list, another thread might still hold a pointer to it — freeing it immediately risks a use-after-free. Solving this requires hazard pointers, epoch-based reclamation, or RCU, each of which is a substantial topic on its own (and in Rust's ownership model, additionally needs unsafe to bypass the borrow checker's single-owner assumption). A bounded ring buffer avoids the problem entirely because slots are reused, never freed — at the cost of needing to handle the "queue full" case (the design choice below).

Behavior on full/empty: spin vs block

push on a full queue returns Err(value) — handing the value back to the caller rather than dropping it — and pop on an empty queue returns None, immediately and non-blocking, rather than spinning or blocking. This keeps the data structure itself fully lock-free and leaves the retry/backoff policy to the caller — appropriate because different use cases want different policies (busy-spin for ultra-low-latency, exponential backoff for CPU-friendly polling, or falling back to a condition variable for a hybrid design). Returning the value on failure rather than a bare bool (as the C++ original did via an out-parameter) is a natural fit for Rust's move semantics: the caller never loses ownership of a value it failed to enqueue.

Lock-free vs wait-free

This queue is lock-free but not wait-free: under pathological scheduling, a thread's CAS could fail repeatedly if other threads continuously win the race (though this is exceptionally rare in practice with compare_exchange_weak and real scheduler behavior). True wait-free algorithms guarantee every thread completes in a bounded number of steps regardless of other threads, but are substantially more complex to implement correctly. Lock-free is the standard, practical target for high-performance concurrent data structures.

Why not just use a mutex?

This is the question every interviewer will ask, and the honest answer is: for most applications, a Mutex<VecDeque<T>> is the right choice. Mutex overhead (tens of nanoseconds, uncontended) is negligible for most workloads, and the implementation is trivially correct — no unsafe required. Lock-free structures are justified specifically when: (1) the queue is on a hot path called millions of times per second, (2) priority inversion is a concern (a low-priority thread holding a lock blocking a high-priority thread), or (3) the structure is used in a context where blocking is unacceptable, such as a signal handler or real-time audio callback. The benchmarks below quantify when the crossover happens.

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Benchmarks

Hardware: Apple M3, 8 cores (8 logical), macOS Compiler: rustc 1.96.0, cargo build --release (opt-level 3, codegen-units 1) Operations: 10,000,000 items transferred per configuration

Throughput vs thread count (fixed 10 M total items)

Threads (P+C)	lfqueue (Mops/sec)	mutex queue (Mops/sec)
1 + 1	133.4	28.4
2 + 2	13.8	32.6
4 + 4	5.3	19.7
8 + 8	2.7	17.6

Interpretation: The lock-free queue wins decisively at 1+1 (no contention, avoids lock overhead entirely). As thread count rises, the CAS-based design suffers from cache-invalidation storms: every producer's CAS on tail and every consumer's CAS on head broadcasts a cache-line invalidation to all other cores. On Apple Silicon's strongly-ordered architecture, the mutex queue serialises access more cheaply than repeated CAS failures under high contention. This is a well-known result — lock-free does not mean faster-under-all-conditions; it means progress without blocking. The lock-free queue's advantages show clearly in the latency distribution below, particularly at the tail.

Effect of cache line padding (4 producers + 4 consumers)

Configuration	Throughput (Mops/sec)
Without padding (false sharing)	3.1
With 64-byte padding	3.5

Padding head and tail onto separate cache lines gives a ~13% throughput improvement, consistent across repeated runs, with no algorithmic change — the only difference is preventing the two counters from sharing a cache line and invalidating each other on every operation. (The effect size is smaller than a textbook false-sharing benchmark might suggest, since Apple Silicon's large L2 cache and aggressive prefetcher absorb some of the invalidation cost — but the direction is consistent across runs.)

Latency distribution (single producer, single consumer, 100 k samples)

Metric	lfqueue (ns)	mutex queue (ns)
p50	4,500	11,167
p99	6,750	149,583
p999	11,458	798,917

The lock-free queue shows dramatically lower tail latency than the mutex queue. This is the expected advantage: when a thread is preempted while holding a mutex, every other thread stalls until it resumes, and OS scheduler involvement adds milliseconds of jitter at the tail. A lock-free queue never blocks a waiter — worst case, a consumer spins briefly on a sequence number before the producer publishes, but it never waits for an OS reschedule.

Reproduce with ./benchmarks/run_all.sh (builds Release binaries if needed).

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Building and Running

Requirements

• Rust 1.63+ (stable) — for std::thread::scope
• Nightly toolchain, optional — only needed to run under ThreadSanitizer or Miri

Build

cargo build --release

Run benchmarks

cargo run --release --example throughput_bench
cargo run --release --example padding_bench
cargo run --release --example latency_bench

Or run all three and save results to benchmarks/results/:

./benchmarks/run_all.sh

Run correctness tests

cargo test --release

Run with ThreadSanitizer

rustup toolchain install nightly
rustup component add rust-src --toolchain nightly

RUSTFLAGS="-Z sanitizer=thread" \
    cargo +nightly test -Z build-std --target aarch64-apple-darwin --release --tests

(Substitute your own target triple, e.g. x86_64-unknown-linux-gnu.)

ThreadSanitizer is essential for this project — it detects data races that may not manifest in normal testing but are real bugs under different scheduling or hardware. Every commit should pass under TSan before being considered correct; this is standard practice for any production lock-free code.

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Testing Strategy

Lock-free correctness cannot be fully validated by normal unit tests, because race conditions are timing-dependent. The test suite uses several complementary approaches:

Functional correctness: single-threaded push/pop sequences verify FIFO ordering, full/empty boundary conditions, and wraparound behavior.

Concurrent stress test: N producers each push a unique sequence of values; M consumers pop and record what they receive. After completion, verify that every value pushed was received exactly once (no duplicates, no drops) — this catches the most common class of lock-free bugs.

ThreadSanitizer: run the stress tests under TSan (nightly Rust, -Z sanitizer=thread) to catch missing memory ordering even when the functional test passes — TSan can detect a "correct by luck" race that would fail on different hardware.

Loom (optional, advanced): loom is Rust's model-checking tool for concurrent code — the direct equivalent of Relacy/CDSChecker in the C++ world. It exhaustively explores thread interleavings for small test cases by substituting loom's atomic types for std's, providing much stronger guarantees than running many random iterations. Mentioned here as a stretch goal for demonstrating depth; adopting it would require gating the atomic imports behind #[cfg(loom)].

Results

$ cargo test --release
running 24 tests
test mpmc_empty_and_full ... ok
test mpmc_wraparound_order ... ok
test mpmc_exact_capacity ... ok
test mpmc_move_only_values ... ok
test mpmc_rejects_zero_capacity - should panic ... ok
test mpmc_rejects_non_power_of_two - should panic ... ok
test mutex_empty_and_full ... ok
test mutex_wraparound_order ... ok
test mutex_exact_capacity ... ok
test mutex_move_only_values ... ok
test mutex_rejects_zero_capacity - should panic ... ok
test mpmc_stress_1p_1c ... ok
test mpmc_stress_2p_2c ... ok
test mpmc_stress_4p_4c ... ok
test mpmc_stress_8p_8c ... ok
test mpmc_stress_many_consumers ... ok
test mpmc_stress_many_producers ... ok
test mpmc_stress_small_capacity_high_contention ... ok
test mutex_stress_1p_1c ... ok
test mutex_stress_2p_2c ... ok
test mutex_stress_4p_4c ... ok
test mutex_stress_8p_8c ... ok
test mutex_stress_many_consumers ... ok
test mutex_stress_many_producers ... ok
test mutex_stress_small_capacity_high_contention ... ok

test result: ok. 24 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out

The correctness test suite covers, in order: empty/full boundary conditions, wraparound ordering, exact-capacity behavior, move-only value types (a non-Copy Box<i32>), and constructor validation (zero capacity, non-power-of- two capacity) — followed by twelve concurrent stress configurations run against both MpmcQueue and MutexQueue (1+1, 2+2, 4+4, 8+8, 1 producer/4 consumers, 4 producers/1 consumer, and 5 repetitions of a small-capacity/high-contention case), each verifying every pushed value is received exactly once with no drops or duplicates.

$ rustup component add rust-src --toolchain nightly
$ RUSTFLAGS="-Z sanitizer=thread" \
    cargo +nightly test -Z build-std --target aarch64-apple-darwin --release --tests
running 24 tests
...
test result: ok. 24 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out

ThreadSanitizer reports zero data races across every stress configuration — the release/acquire pairing on the sequence number is sufficient to make the producer/consumer handoff safe under TSan's race detector, not merely "correct by luck" on a specific architecture's memory model.

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Relationship to Broader Systems Work

Lock-free queues are the backbone of high-performance concurrent systems:

• Work-stealing schedulers (used in thread pools, task-based parallelism frameworks like Intel TBB and Tokio's multi-threaded runtime) use lock-free deques so idle threads can steal work from busy threads without blocking them.

• Inter-thread communication in low-latency systems (audio processing, HFT, real-time control loops) requires bounded, predictable-latency message passing — exactly what this queue provides, in contrast to a mutex queue's occasional long tail latencies under preemption.

• Memory allocators (connecting to the companion allocator project): high-performance allocators use lock-free free-lists for cross-thread memory return — when thread A frees memory originally allocated by thread B, it must hand the memory back without locking B's allocator state.

• Compiler and runtime internals: garbage collectors and JIT compilers use lock-free structures for work queues (e.g., parallel marking phases in a GC) where mutex contention would directly translate to pause-time regressions.

The memory ordering reasoning in this project — release/acquire pairing, happens-before relationships — is the same reasoning required to understand data races in any concurrent system, including the kernel synchronization in the character device driver project (where the kernel's locking primitives handle this for you, but the underlying memory model is identical).

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Future Extensions

• Unbounded variant with hazard pointers — implement the Michael-Scott queue with a safe memory reclamation scheme, demonstrating the harder unbounded case
• Backoff strategies — implement and benchmark exponential backoff on CAS failure vs busy-spin, quantifying the CPU-usage/latency tradeoff
• SPSC fast path — a single-producer/single-consumer specialization can avoid CAS entirely (plain atomic loads/stores suffice when there's only one writer per index), and benchmarking SPSC vs MPMC quantifies the cost of generality
• Loom-based model checking — gate the atomic types behind #[cfg(loom)] so cargo test --features loom can exhaustively check small interleavings, rather than relying on random scheduling to surface bugs
• Atomic wait/notify — Rust's standard library has an unstable Atomic*::wait/notify API (mirroring C++20's std::atomic::wait/notify) that would let consumers block on a futex instead of busy-polling; swap to it once stabilized

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References

• Vyukov, D. Bounded MPMC Queue — the sequence-number design this implementation is based on (1024cores.net)
• Michael, M. & Scott, M. Simple, Fast, and Practical Non-Blocking and Blocking Concurrent Queue Algorithms (PODC 1996) — the unbounded queue referenced in Future Extensions
• Bos, M. Rust Atomics and Locks — the standard modern reference for std::sync::atomic, memory ordering, and building concurrent data structures in Rust
• The Rustonomicon — the reference for unsafe Rust, including UnsafeCell and MaybeUninit, both used in this implementation
• loom — Rust's concurrency model checker, the equivalent of Relacy/CDSChecker referenced in Testing Strategy
• Preshing, J. — preshing.com blog series on lock-free programming and memory ordering (clear, practical explanations with diagrams; language-agnostic)