Going Lock-Free with Rust: Benefits, Drawbacks, and Examples

Tracy,Sun Jun 23 2024•rust concurrency atomics lock-free programming

Concurrency is a challenging but essential aspect of modern programming, especially for performance-critical applications. In Rust, the promise of "fearless concurrency" is a major draw for developers seeking to write safe and efficient concurrent code. While traditional locking mechanisms like mutexes and read-write locks ensure data integrity, they come with their own set of problems such as deadlocks, contention, and performance bottlenecks. An alternative approach is to use lock-free data structures and algorithms, which can offer significant performance improvements and scalability. This article explores the benefits and drawbacks of going lock-free in Rust, with an interesting example towards the end.

What is Lock-Free Programming?

Lock-free programming is a concurrency control technique that allows multiple threads to operate on shared data without the use of traditional locking mechanisms. Instead of using locks to ensure data integrity, lock-free programming relies on atomic operations and advanced memory management techniques to achieve synchronization. This approach offers significant advantages in terms of performance, scalability, and reliability, especially in high-concurrency environments. Here, we introduce the key concepts and principles behind lock-free programming.

Key Concepts of Lock-Free Programming

Atomic Operations

Atomic operations are the cornerstone of lock-free programming. These operations are performed in a way that ensures they are indivisible, meaning that they appear to occur instantaneously from the perspective of other threads. Common atomic operations include:

Compare-and-Swap (CAS): CAS is an atomic operation that compares the value at a memory location with a given value and, if they match, updates the memory location to a new value. This operation ensures that only one thread can successfully update the value at a time, providing a foundation for building lock-free algorithms.
Fetch-and-Add: This atomic operation increments a value at a memory location and returns the previous value. It is useful for implementing counters and other data structures that require atomic increments.
Load-Linked/Store-Conditional (LL/SC): This pair of operations is used in some architectures to achieve atomicity. LL reads a value from memory, and SC attempts to write a new value to the same location, succeeding only if the value has not changed since the LL operation.

Memory Ordering

Modern processors employ various optimizations that can reorder instructions to improve performance. Lock-free algorithms require specific memory ordering guarantees to ensure that operations occur in the correct order from the perspective of all threads. Rust's atomic operations support several memory orderings, including:

Relaxed: No ordering guarantees, used for performance optimization when ordering is not critical.
Acquire/Release: Ensures that operations before the acquire or release are not reordered past it.
Sequentially Consistent (SeqCst): Provides the strongest ordering guarantees, ensuring a consistent global order of operations.

Rust provides atomic types in the std::sync::atomic module, such as AtomicBool, AtomicI32, AtomicUsize, and AtomicPtr. These types support atomic operations with various memory ordering options. The memory ordering is specified as an argument to atomic operations and determines the level of synchronization guaranteed.

Rust offers five memory ordering options:

Relaxed: The weakest memory ordering. It only guarantees atomicity but provides no synchronization or ordering guarantees between threads. Use Relaxed ordering when you only need atomic operations without any synchronization between threads, such as for a global counter where the exact ordering of updates is not important.
Acquire: Ensures that all memory operations after the atomic operation are not reordered before it. It's typically used when reading shared data. Use Acquire when you want to ensure that all subsequent read and write operations in the current thread are not reordered before this operation.
Release: Ensures that all memory operations before the atomic operation are not reordered after it. It's typically used when writing shared data. Use Release when you want to ensure that all previous read and write operations in the current thread are not reordered after this operation.
AcqRel (Acquire and Release): Combines the effects of both Acquire and Release orderings. It's used for operations that both read and write memory, such as compare_and_swap or fetch_and_add. Use AcqRel for operations that need both Acquire and Release semantics, typically read-modify-write operations.
SeqCst (Sequentially Consistent): The strongest memory ordering. It guarantees a total order of all SeqCst operations across all threads. Use SeqCst when you need the strongest guarantees and when the performance cost is acceptable. It's often used for implementing higher-level synchronization primitives.

Non-blocking Algorithms

Lock-free programming employs non-blocking algorithms that allow threads to make progress independently. These algorithms fall into three categories based on their progress guarantees:

Wait-Free: Every operation completes in a bounded number of steps, ensuring that all threads make progress regardless of contention.
Lock-Free: At least one thread makes progress in a finite number of steps, ensuring system-wide progress even under contention.
Obstruction-Free: A thread makes progress as long as it does not encounter contention from other threads.

Hazard Pointers and Epoch-Based Reclamation

Proper memory management is crucial in lock-free programming to avoid use-after-free errors and memory leaks. Two common techniques are:

Hazard Pointers: Threads declare pointers they are currently accessing as "hazardous," preventing these pointers from being reclaimed. Once a thread is done with a pointer, it removes it from the hazard list, allowing safe reclamation.
Epoch-Based Reclamation: Time is divided into epochs, and threads update their local epochs when accessing shared data. Memory is reclaimed only when it is safe to do so, ensuring that no thread can access reclaimed memory.

Benefits of Lock-Free Programming

Performance and Scalability

Lock-free data structures can provide superior performance, especially under high contention. Traditional locks can cause threads to block, leading to idle CPU cycles and reduced throughput. Lock-free structures, on the other hand, allow multiple threads to operate on shared data simultaneously without blocking, leading to better CPU utilization and higher performance.

Reduced Contention

In a multithreaded environment, contention occurs when multiple threads attempt to access shared resources simultaneously. Traditional locking mechanisms like mutexes or read-write locks serialize access to these resources, meaning that only one thread can hold the lock at a time, while other threads must wait. This contention can lead to significant performance degradation, especially under high load.

Lock-free data structures, on the other hand, allow multiple threads to operate on shared resources concurrently without requiring mutual exclusion. This reduces contention and enables better utilization of CPU resources, leading to improved throughput and performance.

Higher Throughput

Throughput refers to the amount of work a system can perform in a given period. Lock-free algorithms generally achieve higher throughput because they avoid the overhead associated with acquiring and releasing locks. Each lock acquisition and release involves a context switch and synchronization operations, which can be costly in terms of CPU cycles.

By eliminating these overheads, lock-free structures allow more operations to be completed in the same amount of time. This is particularly beneficial in high-frequency trading systems, real-time data processing applications, and other performance-critical domains where even small improvements in throughput can have significant impacts.

Scalability Across Cores

Modern processors feature multiple cores, and scalable software should be able to utilize these cores effectively. Lock-based approaches often struggle to scale across many cores due to the overhead of lock contention and the serialized nature of lock acquisition.

Lock-free algorithms scale more efficiently because they leverage atomic operations, which are typically supported directly by the hardware. These atomic operations, such as compare-and-swap (CAS), allow multiple threads to update shared data simultaneously without blocking each other. As a result, lock-free data structures can scale linearly with the number of cores, making them ideal for multi-core and many-core systems.

Lower Latency

Latency is the time it takes for a system to respond to a request. In systems where low latency is crucial, such as real-time applications, the delays introduced by locking can be detrimental. Lock contention not only increases the time taken to complete an operation but can also lead to unpredictable delays.

Lock-free data structures typically exhibit lower and more predictable latency because operations do not involve waiting for locks. This is essential in applications where consistent and fast response times are required, such as high-frequency trading, telecommunications, and gaming.

Impact of Locking on Latency

Traditional locking mechanisms, like mutexes and read-write locks, ensure data integrity in concurrent applications. However, they introduce several sources of latency:

Lock Contention: When multiple threads attempt to acquire the same lock simultaneously, they contend for the lock. Only one thread can hold the lock at any given time, causing other threads to wait. This waiting time directly increases latency.
Context Switching: When a thread is blocked waiting for a lock, the operating system may perform a context switch to another thread. Context switching involves saving the state of the current thread and loading the state of the next thread, which incurs a performance penalty and increases latency.
Cache Coherence Overheads: Locks can lead to cache coherence issues in multi-core processors. When a lock is acquired or released, the cache lines containing the lock and related data must be synchronized across cores, adding to latency.
Priority Inversion: In real-time systems, priority inversion occurs when a higher-priority thread is blocked by a lower-priority thread holding a lock. This can cause unpredictable delays, further increasing latency.

Lock-Free Programming and Low Latency

Lock-free programming addresses these latency issues by avoiding locks altogether. Here's how lock-free techniques contribute to low latency:

Non-blocking Operations: Lock-free algorithms use atomic operations, such as compare-and-swap (CAS), to update shared data. These operations are non-blocking, meaning they do not cause threads to wait. If an atomic operation fails, the thread can immediately retry the operation, reducing waiting time and improving latency.
Reduced Context Switching: Since lock-free algorithms do not block threads, there are fewer context switches. This reduces the overhead associated with context switching and leads to lower latency.
Cache-friendly Operations: Lock-free algorithms often lead to more cache-friendly operations. By reducing the need for synchronization across cores, lock-free programming minimizes cache coherence overheads, resulting in faster data access and lower latency.
Predictable Performance: Lock-free algorithms exhibit more predictable performance characteristics because they avoid the variability introduced by locks. This is especially important in real-time and latency-sensitive applications where consistent response times are crucial.

Avoidance of Deadlocks

Deadlocks are a notorious problem in concurrent programming. They occur when two or more threads are unable to proceed with their execution because each is waiting for the other to release a resource. Deadlocks can cause systems to halt and require complex debugging and recovery strategies. Lock-free programming provides a compelling solution to avoid deadlocks altogether.

Understanding Deadlocks

To fully appreciate how lock-free programming avoids deadlocks, it's important to understand how deadlocks arise. Four conditions must hold simultaneously for a deadlock to occur:

Mutual Exclusion: At least one resource must be held in a non-shareable mode, meaning only one thread can use the resource at a time.
Hold and Wait: A thread holding at least one resource is waiting to acquire additional resources held by other threads.
No Preemption: Resources cannot be forcibly taken away from threads holding them; they must be released voluntarily.
Circular Wait: There exists a set of threads {T1, T2, ..., Tn} where T1 is waiting for a resource held by T2, T2 is waiting for a resource held by T3, and so on, with Tn waiting for a resource held by T1.

When all these conditions are met, a deadlock occurs. Breaking any of these conditions can prevent deadlocks, which is where lock-free programming comes into play.

How Lock-Free Programming Avoids Deadlocks

Lock-free programming eliminates the use of traditional locks, thereby inherently breaking the "mutual exclusion" condition required for deadlocks. Here's how it avoids the conditions for deadlocks:

No Mutual Exclusion: Lock-free data structures use atomic operations to manage shared resources. These operations, such as compare-and-swap (CAS), ensure that only one thread can successfully update the shared resource at a time without holding a lock. This eliminates the need for mutual exclusion, as threads do not wait for locks to be released.
No Hold and Wait: In lock-free programming, threads do not hold resources while waiting for other resources. Instead, they perform their operations in a way that does not require waiting. If an atomic operation fails, the thread simply retries the operation without holding any resource, thus preventing the hold-and-wait condition.
Preemption through Retries: Lock-free algorithms are designed to allow threads to retry operations until they succeed. This retry mechanism ensures that no thread is indefinitely waiting for resources, effectively allowing preemption and breaking the no-preemption condition.
No Circular Wait: Since lock-free programming does not involve holding multiple locks simultaneously, the circular wait condition is inherently avoided. Threads are not dependent on a chain of resource acquisitions, so circular dependencies do not arise.

Drawbacks of Lock-Free Programming

Complexity

While lock-free programming offers significant advantages in terms of performance, scalability, low latency, and deterministic performance, it also comes with a substantial drawback: complexity. Writing correct and efficient lock-free code is notoriously difficult and requires a deep understanding of concurrency, memory ordering, and atomic operations. This section provides an extended example to illustrate the complexity of lock-free programming, particularly in managing memory and ensuring correctness.

The Challenge of Lock-Free Programming

Lock-free programming involves using atomic operations to manage shared resources without the need for locks. This approach eliminates the problems associated with locking but introduces new challenges, such as:

Correctness: Ensuring that the code behaves correctly under all possible interleavings of concurrent operations is extremely difficult. Bugs in lock-free code can lead to subtle and hard-to-detect issues like data races, memory corruption, and infinite loops.
Memory Management: Proper memory management in a lock-free context is challenging. Reclaiming memory safely without introducing use-after-free errors requires careful handling, often involving techniques like hazard pointers or epoch-based reclamation.
Atomic Operations and Memory Ordering: Understanding and correctly using atomic operations and memory ordering semantics is essential. These operations are low-level and require precise handling to ensure that the desired synchronization effects are achieved.
Complexity of Implementation: Implementing lock-free data structures and algorithms typically results in more complex and less readable code compared to their lock-based counterparts. This complexity makes the code harder to maintain and debug.

Readability and Maintainability

Lock-free code can be harder to read and maintain compared to code that uses traditional locks. The complexity of ensuring atomicity and the use of advanced synchronization primitives can make the code less accessible to developers.

Challenges to Readability and Maintainability

Complex Control Flow: Lock-free algorithms typically involve complex control flow to handle retries and ensure atomic updates. This makes the code harder to follow and understand, especially for developers who are not deeply familiar with concurrency concepts.
Extensive Use of Atomic Operations: The pervasive use of atomic operations and memory fences adds to the complexity. Understanding these operations requires knowledge of low-level hardware details and memory ordering semantics, which can be daunting for many developers.
Error-Prone: Lock-free code is more prone to subtle bugs such as data races, memory corruption, and infinite loops. These bugs are often difficult to reproduce and diagnose, making the codebase harder to maintain.
Difficulty in Refactoring: Refactoring lock-free code is challenging due to the tight coupling between different parts of the algorithm. Any change to the code can introduce new bugs or break existing guarantees, requiring careful validation and testing.
Lack of Abstraction: Lock-free code often lacks the high-level abstractions found in lock-based code, making it less intuitive and harder to reason about. The focus on performance and atomicity often leads to lower-level, less abstract implementations.

Limited Use Cases

Lock-free techniques can be highly effective for certain types of problems but are not universally applicable. This limitation arises due to the inherent complexity and specific requirements of lock-free algorithms, which make them suitable for some use cases but impractical or inefficient for others. This section explores the limited use cases of lock-free programming, providing examples to illustrate when it is appropriate and when it is not.

Why Lock-Free Programming Has Limited Use Cases

Specific Data Structures: Lock-free algorithms are well-suited for certain data structures like queues, stacks, and hash maps, where atomic operations can be used to manage concurrency effectively. However, for more complex data structures like balanced trees or graphs, developing efficient lock-free implementations is extremely challenging.
Complexity and Development Cost: The complexity of designing and implementing lock-free algorithms makes them less suitable for general-purpose programming. The development cost, in terms of time and expertise required, can be prohibitive for many projects, especially when simpler lock-based solutions can suffice.
Memory Management Challenges: Proper memory management in lock-free programming is difficult, often requiring sophisticated techniques like hazard pointers, epoch-based reclamation, or reference counting. These techniques add complexity and overhead, making lock-free solutions less attractive for applications

where memory management is already complex. 4. Limited Applicability to Business Logic: Lock-free programming is primarily concerned with low-level data structure manipulation. It does not easily extend to higher-level business logic, where the need for concurrency control often involves more than just data structure access. In such cases, traditional locking mechanisms or higher-level concurrency abstractions may be more appropriate. 5. Hardware Dependency: Lock-free algorithms rely heavily on atomic operations provided by the hardware. The availability and performance of these operations can vary across different architectures, making lock-free programming less portable and efficient on some platforms.

Appropriate Use Cases for Lock-Free Programming

Lock-free programming shines in scenarios where high performance and low latency are critical, and the data structures involved are simple enough to manage with atomic operations. Some appropriate use cases include:

Real-Time Systems: Systems that require predictable and consistent response times, such as real-time operating systems (RTOS), benefit from lock-free programming. The elimination of lock contention and blocking makes it easier to meet real-time constraints.
High-Frequency Trading: In high-frequency trading (HFT), where financial transactions are executed in microseconds, lock-free data structures ensure minimal latency and high throughput, providing a competitive edge.
Network Servers: Servers handling high volumes of concurrent requests, such as web servers or database servers, can benefit from lock-free data structures to improve scalability and reduce response times.
Low-Level Libraries: Low-level libraries, such as those providing concurrent data structures or memory allocators, are ideal candidates for lock-free programming. These libraries can encapsulate the complexity of lock-free algorithms, providing high-performance building blocks for other applications.

Inappropriate Use Cases for Lock-Free Programming

For many applications, lock-free programming may not be the best choice due to its complexity and limited applicability. Some scenarios where lock-free programming is less appropriate include:

Complex Data Structures: Implementing lock-free versions of complex data structures, like balanced trees (e.g., AVL or red-black trees) or graphs, is extremely difficult. The intricate balancing and structural modifications required are not easily managed with atomic operations.
Business Logic: Applications with complex business logic that involves more than just data structure manipulation often require higher-level concurrency control. Traditional locks or higher-level concurrency abstractions, like transactions or actor models, are more suitable.
Memory-Intensive Applications: Applications with complex memory management requirements, such as those dealing with large object graphs or intricate data lifecycles, may find the additional complexity of lock-free memory reclamation techniques prohibitive.
Cross-Platform Applications: Lock-free algorithms rely on atomic operations that may not be consistently available or efficient across different hardware architectures. For cross-platform applications, traditional locking mechanisms may offer better portability and maintainability.

Hardware Dependency

Lock-free programming relies heavily on atomic operations and specific memory ordering guarantees provided by the underlying hardware. While these operations enable high-performance and non-blocking concurrency, they introduce a significant dependency on hardware capabilities. This dependency can limit the portability and efficiency of lock-free algorithms across different platforms. This section explores the hardware dependency of lock-free programming, detailing the challenges it presents and providing examples to illustrate these issues.

Hardware Dependency in Lock-Free Programming

Atomic Operations: Lock-free programming relies on atomic operations such as compare-and-swap (CAS), fetch-and-add, and load-linked/store-conditional (LL/SC). These operations must be supported by the hardware to ensure that updates to shared data are performed atomically without the possibility of intermediate states.
Memory Ordering: Modern processors employ various optimizations that can reorder instructions to improve performance. Lock-free algorithms require specific memory ordering guarantees to ensure that operations appear to occur in the correct order from the perspective of all threads. These guarantees are provided by hardware memory models, which can vary across different architectures.
Performance Variability: The performance of atomic operations can vary significantly across different hardware platforms. Some processors may implement these operations more efficiently than others, leading to variability in the performance of lock-free algorithms.
Portability Challenges: Lock-free algorithms designed for one hardware platform may not perform well or may require significant modifications to work correctly on another platform. This lack of portability can be a significant drawback for software intended to run on diverse hardware environments.

Practical Examples in Rust

To illustrate the use of lock-free programming in Rust, we will look at a very simplified version of a lock-free HashMap implementation using only the std library.

use std::collections::hash_map::DefaultHasher;
use std::collections::HashMap;
use std::hash::{Hash, Hasher};
use std::sync::{
    atomic::{AtomicPtr, AtomicUsize, Ordering},
    Arc, Mutex,
};
use std::thread;
use std::time::{Duration, Instant};
 
struct Node<K, V> {
    key: K,
    value: V,
    next: AtomicPtr<Node<K, V>>,
}
 
struct Bucket<K, V> {
    head: AtomicPtr<Node<K, V>>,
}
 
pub struct LockFreeHashMap<K, V> {
    buckets: Vec<Bucket<K, V>>,
    size: AtomicUsize,
}
 
impl<K, V> LockFreeHashMap<K, V>
where
    K: Eq + Hash + Clone,
    V: Clone,
{
    pub fn new(capacity: usize) -> Self {
        let mut buckets = Vec::with_capacity(capacity);
        for _ in 0..capacity {
            buckets.push(Bucket {
                head: AtomicPtr::new(std::ptr::null_mut()),
            });
        }
        LockFreeHashMap {
            buckets,
            size: AtomicUsize::new(0),
        }
    }
 
    fn hash(&self, key: &K) -> usize {
        let mut hasher = DefaultHasher::new();
        key.hash(&mut hasher);
        hasher.finish() as usize % self.buckets.len()
    }
 
    pub fn insert(&self, key: K, value: V) -> Option<V> {
        let hash = self.hash(&key);
        let bucket = &self.buckets[hash];
        let new_node = Box::into_raw(Box::new(Node {
            key: key.clone(),
            value,
            next: AtomicPtr::new(std::ptr::null_mut()),
        }));
 
        loop {
            let head = bucket.head.load(Ordering::Relaxed);
            unsafe {
                (*new_node).next.store(head, Ordering::Relaxed);
                if bucket
                    .head
                    .compare_exchange(head, new_node, Ordering::Release, Ordering::Relaxed)
                    .is_ok()
                {
                    self.size.fetch_add(1, Ordering::Relaxed);
                    return None;
                }
            }
        }
    }
 
    pub fn get(&self, key: &K) -> Option<V> {
        let hash = self.hash(key);
        let bucket = &self.buckets[hash];
        let mut current = bucket.head.load(Ordering::Acquire);
 
        while !current.is_null() {
            unsafe {
                if (*current).key == *key {
                    return Some((*current).value.clone());
                }
                current = (*current).next.load(Ordering::Relaxed);
            }
        }
 
        None
    }
 
    pub fn remove(&self, key: &K) -> Option<V> {
        let hash = self.hash(key);
        let bucket = &self.buckets[hash];
        let mut head = bucket.head.load(Ordering::Acquire);
        let mut prev: *mut Node<K, V> = std::ptr::null_mut();
 
        while !head.is_null() {
            unsafe {
                if (*head).key == *key {
                    let next = (*head).next.load(Ordering::Relaxed);
                    if prev.is_null() {
                        if bucket
                            .head
                            .compare_exchange(head, next, Ordering::Release, Ordering::Relaxed)
                            .is_ok()
                        {
                            self.size.fetch_sub(1, Ordering::Relaxed);
                            return Some(Box::from_raw(head).value);
                        }
                    } else {
                        (*prev).next.store(next, Ordering::Release);
                        self.size.fetch_sub(1, Ordering::Relaxed);
                        return Some(Box::from_raw(head).value);
                    }
                }
                prev = head;
                head = (*head).next.load(Ordering::Relaxed);
            }
        }
 
        None
    }
 
    pub fn len(&self) -> usize {
        self.size.load(Ordering::Relaxed)
    }
}
 
fn test_lock_free(num_threads: usize, operations_per_thread: usize) -> Duration {
    let map = Arc::new(LockFreeHashMap::<usize, usize>::new(16));
    let start = Instant::now();
 
    let handles: Vec<_> = (0..num_threads)
        .map(|i| {
            let map_clone = Arc::clone(&map);
            thread::spawn(move || {
                for j in 0..operations_per_thread {
                    let key = (i * operations_per_thread + j) % 100;
                    map_clone.insert(key, j);
                    map_clone.get(&key);
                    if j % 10 == 0 {
                        map_clone.remove(&key);
                    }
                }
            })
        })
        .collect();
 
    for handle in handles {
        handle.join().unwrap();
    }
 
    start.elapsed()
}
 
fn test_mutex_based(num_threads: usize, operations_per_thread: usize) -> Duration {
    let map = Arc::new(Mutex::new(HashMap::<usize, usize>::new()));
    let start = Instant::now();
 
    let handles: Vec<_> = (0..num_threads)
        .map(|i| {
            let map_clone = Arc::clone(&map);
            thread::spawn(move || {
                for j in 0..operations_per_thread {
                    let key = (i * operations_per_thread + j) % 100;
                    map_clone.lock().unwrap().insert(key, j);
                    map_clone.lock().unwrap().get(&key);
                    if j % 10 == 0 {
                        map_clone.lock().unwrap().remove(&key);
                    }
                }
            })
        })
        .collect();
 
    for handle in handles {
        handle.join().unwrap();
    }
 
    start.elapsed()
}
 
fn main() {
    let num_threads = 8;
    let operations_per_thread = 1_000;
 
    println!(
        "Testing with {} threads, {} operations per thread",
        num_threads, operations_per_thread
    );
 
    let lock_free_time = test_lock_free(num_threads, operations_per_thread);
    println!("Lock-free version took: {:?}", lock_free_time);
 
    let mutex_time = test_mutex_based(num_threads, operations_per_thread);
    println!("Mutex-based version took: {:?}", mutex_time);
 
    println!(
        "Lock-free version was {:.2}x faster",
        mutex_time.as_secs_f64() / lock_free_time.as_secs_f64()
    );
}

You'll notice I added a comparison between my lock-free HashMap and a Mutex-based version. Well, here's the result:

Lock-free version took: 1.0481ms
Mutex-based version took: 15.923701ms
Lock-free version was 15.19x faster

15.19x faster for the lock-free implementation. Now, the downside of this is it is not production-ready. This code is probably riddled with bugs but hey, I wrote it quickly for this article to just prove a point.

If you are looking for other great examples of lock-free programming, check out the following:

@jonhoo (opens in a new tab)'s implementation of HazardPointers in Rust: https://github.com/jonhoo/haphazard (opens in a new tab)
Stable Concurrent Containers provides utilities for writing concurrent code: https://github.com/wvwwvwwv/scalable-concurrent-containers (opens in a new tab)
The crossbeam crate is a collection of concurrent and lock-free data structures and tools for concurrent programming: https://github.com/crossbeam-rs/crossbeam (opens in a new tab)

Conclusion

Lock-free programming in Rust offers significant benefits in terms of performance, scalability, and avoiding common concurrency issues like deadlocks. However, it comes with challenges, including increased complexity and maintainability concerns. The haphazard and flurry crates provide powerful tools for implementing lock-free data structures in Rust, making it possible to achieve high concurrency and efficiency.

While lock-free programming is not a silver bullet and may not be suitable for all scenarios, it is a valuable approach for developers looking to maximize the performance and scalability of their concurrent applications. By understanding the trade-offs and leveraging the right tools and libraries, developers can harness the full potential of lock-free programming in Rust.