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Prefer Structured Lifetimes: Local, Nested, Bounded, Deterministic

Enter Concurrency

All of the issues and costs associated with unstructured object lifetimes apply in full force to concurrency. And not only do we see "similar" issues and costs arise in the case of unstructured concurrency, but often we see the very same ones.

Threads and tasks have unstructured lifetimes by default on most systems, and therefore by default non-local, non-nested, unbounded, and nondeterministic. That's the root cause of most of threads' major problems. [4] As with unstructured object lifetimes, to manage unstructured thread and task lifetimes we need to perform tracking to make sure we don't try to wait for or communicate with a task after that task has already ended (similar to use-after-delete or use-after-dispose issues with objects); and to join with a thread before the end of the program to avoid risking undefined behavior on nearly all platforms (similar to unstructured "eventual" finalization at shutdown time). Unstructured threads and tasks also incur extra over-heads [5], such as extra blocking when we attempt to join with multiple pieces of work; they are also more likely to have ownership cycles that have to be detected and cleaned up, and similarly for waiting cycles and even deadlock (e.g., when two threads wait for each other's messages).

Structured thread and task lifetimes are certainly possible. You just have to make it happen by applying a dose of discipline when they aren't structured by default: A function that spawns some asynchronous work has to be careful to also join with that work before the function itself returns, so that the lifetime of the spawned work is a subset of the invoking function's lifetime and there are no outstanding tasks, no pending futures, no lazily deferred side effects that the caller will assume are already complete. Otherwise, chaos can result, as we'll see when we analyze an example of this in the next section.

With locks, the stakes are higher still to keep lifetimes bounded -- the shorter the better -- and deterministic. Deadlocks happen often enough when lock lifetimes all are structured, and being structured isn't enough by itself to avoid deadlock [6]; but toying with unstructured locking is just asking for a generous helping of latent deadlocks to be sprinkled throughout your application. Unstructured lock lifetimes also incur the other issues common to all unstructured lifetimes, including tracking overhead to manage the locks, and performance overhead from excessive waiting/blocking that can cause throttling and delay even when there isn't an outright deadlock.

Given the stakes, it's no surprise that every major OO language offers direct language support for bounded lock lifetimes where the lock is automatically released at the end of the scope or the function:

  • C++0x has std::lock_guard, which is used in a way that just leverages C++'s bounded stack-based variable rules.
  • C# has lock blocks, and can also leverage its using blocks with lock types that implement IDisposable.
  • Java has synchronized blocks, and can leverage the try/finally dispose pattern for disposable lock objects.

These tools exist for a reason. Strongly prefer using these structured lifetime mechanisms for scoped locking whenever possible, instead of following the dark path of unstructured locking by explicit standalone calls to Lock functions without at least a finally to Unlock at the end of the scope.

Having all that in mind, we'll turn to an example that illustrates a simple but common mistake that has its root in unstructuredness.

An Example, With a Common Initial Mistake

Let's take a fresh look at the same Quicksort example we considered briefly last month. [7] Here is a simplified traditional sequential implementation of quicksort:

// Example 1: Sequential quicksort
void Quicksort( Iter first, Iter last ) {
  // If the range is small, another sort is usually faster.
  if( distance( first, last ) < limit ) {
    OtherSort( first, last );
  // 1. Pick a pivot position somewhere in the middle.
  // Move all elements smaller than the pivot value to
  // the pivot's left, and all larger elements to its right.
  Iter pivot = <b>Partition( first, last );</b>
  // 2. Recurse to sort the subranges.
  Quicksort( first, pivot );
  Quicksort( pivot, last );

How would you parallelize this function? Here's one attempt, in Example 2 below. It gets several details right: For good performance, it correctly reverts to a synchronous sort for smaller ranges, just like a good synchronous implementation reverts to a non-quicksort for smaller ranges; and it keeps the last chunk of work to run synchronously on its own thread to avoid a needless extra context switch and preserve better locality.

// Example 2: Parallelized quicksort, take 1 (flawed)
void Quicksort( Iter first, Iter last ) {
  // If the range is small, <font color="red">synchronous</font> sort is usually faster.
  if( distance( first, last ) < limit ) {
    OtherSort( first, last );
  // 1. Pick a pivot position somewhere in the middle.
  // Move all elements smaller than the pivot value to
  // the pivot's left, and all larger elements to its right.
  Iter pivot = Partition( first, last );
  // 2. Recurse to sort the subranges. <font color="red">Run the first
  // asynchronously, while this thread continues with
  // the second.
  pool.run(  [=]{ </font>Quicksort( first, pivot <font color="red">); }  );</font>
  Quicksort( pivot, last );

Note: pool.run(x) just means to create task x to run in parallel, such as on a thread pool; and [=]{ f; } in C++, or ()=>{ f(); } in C#, is just a convenient lambda syntax for creating an object that can be executed (in Java, just write a runnable object by hand).

But now back to the main question: What's wrong with this code in Example 2? Think about it for a moment before reading on.

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