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Use Thread Pools Correctly: Keep Tasks Short and Nonblocking

Herb Sutter is a bestselling author and consultant on software development topics, and a software architect at Microsoft. He can be contacted at www.gotw.ca

What are thread pools for, and how can you use them effectively? As shown in Figure 1, thread pools are about letting the programmer express lots of pieces of independent work as a "sea" of tasks to be executed, and then running that work on a set of threads whose number is automatically chosen to spread the work across the hardware parallelism available on the machine (typically, the number of hardware cores [1]). Conceptually, this lets us execute the tasks correctly one at a time on a single-core machine, execute them faster by running four at a time on a four-core machine, and so on.

Figure 1: Thread pools are about taking a sea of work items and spreading them across available parallel hardware.

Besides scalable tasks, one other good candidate of work to run on a thread pool is the small "one-shot" thread. This is work that we might ordinarily express as a separate thread, but that is so short that the overhead of creating a thread is comparable to the work itself. Instead of creating a brand new thread and quickly throwing it away again, we can avoid the thread creation overhead by running the work on a thread pool, in effect playing "rent-a-thread" to reuse an existing pool thread instead. (See [2] for more about using threads correctly, including running small threads as pool work items.)

But the thread pool is a leaky abstraction. That is, the pool hides a lot of details from us, but to use it effectively we do need to be aware of some things a pool does under the covers so that we can avoid inadvertently hitting performance and correctness pitfalls. Here's the summary up front:

  • Tasks should be small, but not too small, otherwise performance overheads will dominate.
  • Tasks should avoid blocking (waiting idly for other events, including inbound messages or contested locks), otherwise the pool won't consistently utilize the hardware well -- and, in the extreme worst case, the pool could even deadlock.

Let's see why.

Tasks Should Be Small, but Not Too Small

Thread pool tasks should be as small as possible, but no smaller.

One reason to prefer making tasks short is because short tasks can spread more evenly and thus use hardware resources well. In Figure 1, notice that we keep the full machine busy until we start to run out of work, and then we have a ragged ending as some threads complete their work sooner and sit idle while others continue working for a time. The larger the tasks, the more unwieldy the pool's workload is, and the harder it will be to spread the work evenly across the machine all the time.

On the other hand, tasks shouldn't be too short because there is a real cost to executing work as a thread pool task. Consider this code:

// Example 1: Running work on a thread pool.
pool.run( <b>[=] { SomeWork(); }</b> );

By definition, SomeWork must be queued up in the pool and then run on a different thread than the original thread. This means we necessarily incur queuing overhead plus a context switch just to move the work to the pool. If we need to communicate an answer back to the original thread, such as through a message or Future or similar, we will incur another context switch for that. Clearly, we aren't going to want to ship int result = int1 + int2; over to a thread pool as a distinct task, even if it could run independently of other work. It's just like the sign you see in a theme park at the entrance to the roller coaster: "You must be at least this big to go on this ride."

So although we like to keep thread pool tasks small, a task should still be big enough to be worth the overhead of executing it on the pool. Measure the overhead of shipping an empty task on your particular thread pool implementation, and as a rule of thumb, aim to make the work you actually ship an order of magnitude larger.

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