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	TABLE OF CONTENTS Threads: In a Nutshell Example 1: GUI Example 2: Sockets Example 3: Pipeline Stages

Explicit threads are undisciplined. They need some structure to keep them in line. In this column, we're going to see what that structure is, as we motivate and illustrate best practices for using threads -- techniques that will make our concurrent code easier to write correctly and to reason about with confidence.

Where Threads Fit (and Thread Pools, Sometimes)

In [1], I described the three pillars of concurrency. The first two pillars summarize the two main kinds of concurrency we need to be able to express: 1. Keep things separate, so that independent parts of the program can run asynchronously. 2. Use more cores to get the answer faster using data-parallel and similar techniques. (The third pillar is about controlling concurrency once it has been expressed, using tools like locks and atomics.)

Table 1 summarizes these two pillars, and also summarizes how well each is served by four major tools at our disposal today for expressing concurrency: threads, thread pools, work stealing runtimes, and data-parallel facilities like OpenMP.

Threads are about expressing Pillar 1 only, and this article will focus on that column: How to effectively use today's tools, notably threads and in some cases thread pools, to express independent work. We'll look at Pillar 2 in a future article.)

Threads: In a Nutshell

Here are the key things to know about threads:

• Threads are for expressing asynchronous work. The point of being asynchronous is to let the units of independent work in the application all run at their own speeds and better tolerate each other's latency.
• Threads are a low-level tool. Threads are just "sequential processes that share memory," and that kind of freewheeling anything-goes model doesn't provide any abstraction or guard rails to make good practices easy and bad practices hard. As aptly criticized by Edward Lee in his paper "The Problem with Threads" [2], threads let you do anything, and do it nondeterministically by default.
• "Up-level" them by replacing shared data with asynchronous messages. As much as possible, prefer to keep each thread's data isolated (unshared), and let threads instead communicate via asynchronous messages that pass copies of data. This best practice inherently encourages writing threads that are event-driven message processing loops, which gives inherent structure and synchronization and also improves determinism:

  
  // An idealized thread mainline
  //
  do {
     message = queue.Receive();
     // this could block (wait)
     // ...
     // handle the message
     // ...
     } while( !done );
  // check for exit
  
  

  Ideally, each thread's logic should be built around the model of servicing its message queue, whether a simple FIFO queue or a priority queue (the latter if some messages should be given priority even if they arrive later).

• Highly responsive threads should not perform significant work directly. Some threads are responsible for interacting with the user (e.g., GUI threads) or with other processes or machines (e.g., socket and communications threads), or for other reasons need to respond to messages quickly. Such threads should perform nearly all their work asynchronously by posting the work to one or more helper threads or, where appropriate, to a pool thread. In particular, highly responsive threads should never block by waiting for a message or trying to acquire a lock.

A final note: In some cases, a thread that is short-running and will not block (wait idly for other events, including inbound messages or locks) can be expressed instead as a thread pool work item for efficiency, to avoid the overhead of creating a new thread from scratch. That's the one valid use of a thread pool for Pillar 1.

1 Threads: In a Nutshell | 2 Example 1: GUI | 3 Example 2: Sockets | 4 Example 3: Pipeline Stages Next Page

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