22.3 Choosing the Right Model: Threads vs. Async for I/O-Bound vs. CPU-Bound Tasks

The choice between using OS threads (std::thread) and async tasks often depends on whether the concurrent tasks are primarily I/O-bound or CPU-bound.

22.3.1 OS Threads (std::thread)

Native OS threads, as managed by std::thread, are preemptively scheduled by the operating system kernel.

  • Best Suited For: CPU-bound tasks. Computationally intensive work (e.g., complex calculations, data processing, simulations) can run in parallel on different cores, potentially leading to substantial speedups on multi-core hardware. If one OS thread blocks (e.g., waiting for synchronous I/O or a lock), the OS can schedule other threads to run.
  • Drawbacks: Creating and managing OS threads incurs overhead. Each thread requires its own stack (consuming memory), and context switching between threads involves the OS scheduler, which has a performance cost. Spawning a very large number of threads (thousands or more) can become inefficient or hit OS limits. For workloads involving many short-lived tasks or tasks that mostly wait, OS threads might not scale well. A common pattern to mitigate this is using a thread pool, which maintains a fixed number of reusable worker threads.

Note: In Rust, if a thread created with std::thread::spawn panics, it terminates only that specific thread. The main thread or other threads can detect this panic if they call join() on the panicked thread’s JoinHandle; join() will return an Err value containing the panic payload. This allows for more controlled error handling compared to C/C++ where an unhandled exception or signal in one thread might terminate the entire process depending on the context and platform.

22.3.2 Async Tasks (async/.await) (Brief Overview)

Async tasks use cooperative scheduling, managed by a user-space runtime library.

  • Best Suited For: I/O-bound tasks. When an async task needs to wait for an external event (like network data arrival or a timer), it yields control using .await, allowing the runtime to schedule another task on the same OS thread. This enables a small pool of OS threads to handle potentially thousands or millions of concurrent operations efficiently, as threads don’t remain idle while waiting. Context switching between async tasks within the same OS thread is significantly cheaper than switching OS threads.
  • Drawbacks: If an async task performs a long, CPU-intensive computation without yielding (i.e., without reaching an .await point), it can “starve” other tasks scheduled on the same OS thread, preventing them from making progress. This is often referred to as “blocking the executor.” CPU-bound work within an async context is usually best delegated to a dedicated thread pool (e.g., using functions like tokio::task::spawn_blocking or integrating with Rayon).

22.3.3 Matching Concurrency Model to Workload

  • I/O-Bound Tasks (e.g., network servers/clients, database interactions, file system operations): Often spend most of their time waiting. Async tasks generally offer better scalability and resource efficiency.
  • CPU-Bound Tasks (e.g., scientific computing, image/video processing, cryptography, complex algorithms): Spend most of their time performing calculations. OS threads (managed directly, via thread pools, or through libraries like Rayon) are typically preferred to leverage true hardware parallelism across multiple cores.

Many real-world applications involve a mix. For example, a web server might use async tasks for handling network connections and I/O, but use a thread pool (like Rayon’s) to execute CPU-intensive parts of request processing. Rust’s safety guarantees apply regardless of the chosen model when dealing with shared data.