Effective Threading Models For Game Ai And Simulation

The Need for Speed: Why Multithreading is Critical for Performant AI and Physics

As games continue to increase in scope and complexity, the AI and physics systems that power them are frequently becoming performance bottlenecks. Single-threaded execution on modern multi-core CPUs often cannot keep up with the computational demands of advanced game logic and simulations. By utilizing multiple threads and dividing work across CPU cores, developers can achieve dramatic speedups for critical systems.

Multithreading allows games to scale their most performance-sensitive tasks across available computing resources. For example, multithreaded AI systems enable thousands of enemies to independently assess situations and determine intelligent actions in parallel. Physics engines can subplot time-intensive calculations like rigid body dynamics across threads. This parallelization unlocks new possibilities for sophisticated in-game logic.

Without multithreading, games risk severe frame rate drops or instability from overwhelmed single threads. Players notice poor responsiveness and lag, significantly damaging engagement and enjoyment. Performance issues in AI and physics also limit the complexity developers can achieve. Utilizing multiple threads is thus crucial for achieving both smooth, high frame rate experiences and advanced in-game logic.

Understanding Threading Models: Comparing Pros and Cons of Different Approaches

There are several dominant paradigms for multithreaded programming, each with their own strengths and weaknesses. Choosing the right approach depends on workload characteristics and system architecture.

Data Parallelism with Job Systems

Data parallelism focuses on distributing large datasets across threads, allowing independent parallel computation on separate data. This approach is common in graphics, physics, and AI workflows dealing with batch data.

Game logic tasks like pathfinding, navigation mesh building, and spatial queries lend themselves well to data parallelism – the navigation map or query space can be divided into partitions with each handled concurrently. Graphics and physics pipelines also exhibit abundant data parallelism opportunities from large inputs like textures and vertex buffers.

Job systems provide automatic handling of data and task distribution across threads, avoiding low-level thread management overheads. Developers specify job dependencies and requirements, enabling optimal scheduling and execution. Graphics APIs like DirectX 12 and Vulkan use job systems for scaling graphics workloads across GPU and CPU cores.

The main downside of data parallelism is contention for shared data access if not properly managed. Appropriate dataset partitioning and synchronization is required.

Task Parallelism with Thread Pools

Task parallelism focuses on concurrently executing multiple separate tasks or operations. This approach is common for game logic and simulation systems that deal with many entities making independent decisions.

The AI systems governing thousands of units in strategy games exhibit abundant task parallelism opportunities from individually assessing game state and determining actions per entity. Physics engines also exhibit considerable task parallelism from separately handling collisions, joints, and constraints in parallel.

Thread pools provide queues for tracking tasks and reuse threads to limit allocation overheads. Task prioritization and dependencies can also be handled efficiently.

Oversubscription on thread pools can cause contention and reduce performance gains. Appropriate tuning of thread count targetting optimal utilization is required.

Hybrid Approaches

Combining data and task parallelism can help better structure application logic while maximizing scaling across threads. Graphics and physics pipelines exhibit such hybrid parallelism – job systems handle parallelism within pipeline stages, while task distribution occurs across the overall workflow.

For example, a cloth simulation system could use task parallelism from concurrently simulating separate cloth objects. Within each cloth object simulation, data parallelism could improve performance of intensive math operations like matrix transformations. Achieving this hybrid model requires using both job queues and thread pools.

The main challenge with hybrid approaches lies in balancing data versus task parallelism tradeoffs. Performance testing various strategies is necessary to determine optimal utilization of both models.

Designing a Thread-Safe Architecture

Creating performant and reliable multithreaded programs requires architectures Limiting shared state access pathways. Various methodologies help restrict shared memory contention:

Limiting Shared State

Keeping shared data access to the absolute minimum necessary greatly reduces contention issues. Where possible, it is best to fully partition data into thread local storage versus global access. For any shared data structures, limit scope to the narrowest spans.

In an AI decision making system, read-only game state data can be fully replicated across threads without issue. Mutable state like unit locations must utilize more advanced synchronization mechanisms to prevent conflicts but should likewise restrict scope as much as feasible.

Immutability

Making key data structures and entities completely immutable eliminates a wide array of synchronization issues. While mutation is often critical for game state changes, selectively identifying hot path areas to enforce immutability on can improve safety.

For example, messages passed between game logic systems could be implemented as immutable packet payloads. Their contents would be guaranteed to not change after publication, eliminating entire classes of race conditions.

Locks and Signaling

Despite best efforts, shared mutable state often cannot be fully avoided in complex applications. Lock primitives like mutexes and semaphores help enforce coordinated access by serializing critical sections. Signaling constructs like events allow notifying threads of state changes.

For example, a resource manager for game assets may require locking to govern requests that could deplete capacity. Waiting threads can utilize signaling to resume responsiveness when capacity frees again. These structures introduce own overheads but can enable practical shared data mutation.

Common Pitfalls and Solutions

Several classes of bugs notoriously plague multithreaded programming. Awareness of these hazards along with defensive coding practices helps mitigate issues:

Race Conditions

When threads access and mutate shared data without synchronization, race conditions can corrupt program state. Defensive copying of shared data before mutation is one remediation. Locking access with primitives like mutexes can also enforce synchronized mutation ordering.

Deadlocks

Deadlocks occur when threads become blocked waiting on locks held by other blocked threads. Strict hierarchical lock ordering, timeout mechanisms, and avoiding nested locks mitigate occurrences. Debugging tools can also help detect deadlock cycles.

Scalability Issues

As thread counts scale, contention for shared data structures can nullify or reverse performance gains. Benchmarking against varying core utilization identifies scalability limits. Refactoring architecture with hierarchical data partitioning and loosening shared data dependencies allows pushing scalability higher.

Example Implementations

Several game genres exhibit prime examples of how multithreaded programming achieves tremendous speedups for key systems:

C++ Thread Pool for Pathfinding

Pathfinding for thousands of units across massive maps requires parallelization to avoid severe lag. By dividing the navigation graph into regions with a work queue, individual path requests can be scaled across CPU cores and SIMD vectors for order of magnitude improvements.

Unity Job System for Navigation Mesh Building

Dynamically modifiable navigation meshes must rebuild in real-time to allow AI responsiveness. Unity’s job system and Burst compiler enable scaling costly navmesh calculations across all available CPU resources for rapid updates.

Hybrid Threading in Unreal SmartObjects

The SmartObject system employs task parallelism from computing object behavior concurrently. Data parallelism is also leveraged in expensive auxiliary systems like navigation within each object. Blend both models for optimal single-frame performance.

Benchmarking and Profiling Threads

Quantifying multithreading overheads and bottlenecks is vital for reaching peak game performance. Dedicated tools provide actionable insights:

Tools for Identifying Bottlenecks

Profilers like Intel VTune integrate with compilers to attribute function costs to specific threads and identify contention. Tracing APIs, e.g. Event Tracing for Windows, provide similar insight. GPU profilers like RenderDoc trace thread utilization for graphics operations.

Strategies for Optimization

Granular profiling directing optimization priority based on cost-benefit. For example, improving spatial partitioning and batching for draw calls may provide higher returns than further graphics pipeline threading. Load testing against varying thread counts can also reveal scalability limits.

The Future of Parallelism

While multicore scaling has slowed, new avenues like heterogeneous computing and upcoming API evolutions promise continued threading performance gains:

Potential of New Hardware

Specialized processing units like GPUs, ML accelerators, and FPGAs enable scaling highly parallel workloads beyond CPUs. Heterogeneous systems present software complexity but can exponentially increase throughput by matching problems to ideal processors.

Upcoming APIs and Frameworks

Standards like SYCL and CUDA aim to simplify cross-platform heterogeneous programming compared to specialized APIs like Vulkan and Metal. New generations of console APIs also aim to help developers better leverage custom hardware for more easily accessible acceleration.