Lock-Free Data Structures For Fast, Scalable Game Logic

The Need for Concurrency in Games

Modern video games simulate complex virtual worlds with thousands of simultaneous entities. Game engines must update positions, render graphics, process inputs, and run AI logic for these entities every frame while maintaining a high, consistent framerate. This requires highly concurrent software architectures that fully utilize multi-core CPUs.

The game loop must dispatch physics, animation, and gameplay code across threads while avoiding serialization bottlenecks. Long-running tasks like pathfinding, simulation, and rendering must not block the critical frame dispatch thread. Even brief stalls can cause framerate drops or input lag, severely degrading responsiveness.

Issues with Locks and Mutexes

The traditional approach to concurrency uses mutex locks to protect shared data during updates. But widespread mutex usage severely impacts game performance and responsiveness due to death embrace scenarios like deadlocks, livelocks, and priority inversions.

The biggest issue is that locks are blocking, meaning threads wait if a mutex is unavailable. This causes stalls that prevent time-critical processing from occurring on schedule, introducing lag, jitter, and framerate issues.

Introducing Lock-Free Data Structures

The lock-free paradigm avoids mutexes entirely, instead using atomic hardware primitives like compare-and-swap (CAS) for coordination. These non-blocking algorithms ensure threads make progress regardless of scheduling delays. This prevents stalls even under heavy loads, providing optimal responsiveness.

Constructing practical lock-free data structures that live in shared memory requires care, as threads must communicate without locks. But the reward is exceptionally scalable concurrent objects that service extreme workloads while maintaining real-time determinism, a perfect fit for demanding game engines.

Lock-Free Linked Lists

Linked lists see ubiquitous use across game logic, but suffer from terrible concurrency issues. Lock-free linked lists use CAS operations on pointer values to allow concurrent inserts, removes, and iterates without blocking:

template
class LockFreeLinkedList {
  struct Node {
    T value; 
    Node* next;
  };
  
  Node* head;

  bool insert(T value) {
    Node* new_node = new Node({value, nullptr});  
    Node* prev_head = head;
    new_node->next = prev_head;
    while (!CAS(&head, prev_head, new_node)) {
      prev_head = head;
      new_node->next = prev_head; 
    }
    return true; 
  } 
};

The compare-and-swap primitive atomically updates head if the previous value matched prev_head, allowing multiple concurrent insertions without corruption. More advanced techniques like hazard pointers manage safe memory reclamation.

Lock-Free Queues

Concurrent producer-consumer queues see heavy use to efficiently distribute game tasks like AI decisions across threads. The Micheal-Scott lock-free queue uses CAS operations to synchronize enqueue and dequeue invocations between threads:

template
class LockFreeQueue {
  struct Node {
    T value;
    Node* next;
  };
   
  Node* head;
  Node* tail;

  void enqueue(T item) {
    Node* node = new Node(item);
    Node* prevTail = tail;
    node->next = nullptr;

    if (CAS(&tail->next, nullptr, node)) {
      CAS(&tail, prevTail, node); 
    } else {
      enqueue(item);
    }
  }

  T dequeue() {
    Node* h = head;
    Node* t = tail;
    Node* newHead = h->next;  
    if (head == tail) {
      if (h == nullptr) return nullptr;
      CAS(&tail, t, newHead); 
    } else {
      CAS(&head, h, newHead);
    }
    return newHead->value;
  }

};  

This design allows threads to concurrently access both ends of the queue without external locking, eliminating contention bottlenecks under scale. The CAS loops handle coordination safely even with worst-case timing.

Other Lock-Free Data Structures

The lock-free paradigm extends beyond lists and queues to more complex data structures like hash tables, trees, and graphs used extensively in game code:

• Sets: Concurrent lock-free hash sets offer fast O(1) contains() and add() suitable for real-time game object lookups.
• Maps: Lock-free concurrent hash maps allow scalable key-value state storage for mutable game data like entity positions or world state.
• Stacks: Lock-free stacks helpfully manage pooled object allocations common during genre-typical spikes in gameplay loads.

Carefully designed lock-free data structures excel at managing the read-frequently, write-rarely access patterns prevalent in game code. Compare-and-swap provides enough synchronization to allow practical concurrent implementations of complex data types.

Tips for Applying Lock-Freedom

Integrating lock-free data structures into game engines requires care. Follow these tips:

• Identify bottlenecks: Profile first, then attack hot locks slowing critical paths with lock-free algorithms.
• Limit memory reclamation: Leaked nodes are better than crashes from premature freeing. Manage lifecycles explicitly.
• Handle ABA issues: The same value can recur, invalidating stale CAS operations. Use tagged pointers or memory barriers.

Conclusion

Lock-free programming techniques allow game developers to eliminate scaling bottlenecks imposed by excessive synchronization. By providing responsive concurrent data structures, lock-freedom takes full advantage of multi-core power while preventing stalls and jitter.

Future research will uncover new lock-free algorithms for broader classes of data structures useful in games, unlocking new horizons for innovation in real-time graphics, simulation, and AI.