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Parallel

The Power of "In Progress"


When and How To Do the Work

This brings us to the key question: So, when and how is the pending work performed?

One option is to do the pending work interleaved with other work, such as in response to timer messages or using explicit continuations as in [1]. This approach is especially useful when the updates must be performed by a single thread, either for historical reasons (e.g., on systems that require a single GUI thread) or to avoid complex locking and synchronization of internal data structures by making the data isolated to a particular thread.

A second approach is to do the work when idle and there is no other work to do. For example, Word normally performs pagination and similar operations in incremental chunks at idle time. This approach is usually used in combination with a fallback to one of the other approaches if we discover that some part of the work needs to happen more immediately, for example if the user jumps to not-yet-paginated part of the document. There are multiple ways to implement "when idle":

  • If all of the updates must be performed by a single thread, we can register callbacks that the system will invoke when idle (e.g., using a Windows WM_IDLE message); this is a traditional approach for GUI applications that have legacy requirements to do their work on the GUI thread.
  • If the updates can be performed by a different thread, we can perform the work on one or more low-priority background threads, each of which pauses every time it completes a chunk of work. To minimize the potential for priority inversion, we want to avoid being in the middle of processing an item (and holding a lock on the shared data) when the background thread is preempted by the operating system, so each chunk of work should fit into an OS scheduling quantum and the pause between work items should include yielding the remainder of the quantum (e.g., using Sleep(0) on Windows).

Third, we can do the work asynchronously and concurrently with other work, such as on one or more normal background worker threads, each of which locks the structure long enough to perform a single piece of pending work and then pauses to let other threads make progress. For example, in Excel 2007 and later, cell recalculation uses a lock-free algorithm that executes in parallel in the background while the user is interacting with the spreadsheet; it may run on several worker threads whose number is scaled to match the amount of hardware parallelism available on the user's machine.

Fourth, in some cases it can be appropriate to do the work lazily on use, where each use of the data structure also performs some pending work to contribute to overall progress; or similarly we may do it on demand specifically in the case of traditional lazy evaluation. With these approaches, note that if the data structure is unused for a time then no progress will be made; that might be desirable, or it might not. Also, if the accesses can come from different threads, it must be safe and appropriate to run different pieces of pending work on whatever threads happen to access the data.

Summary

Embrace change: For high-contention data that may be the target of long-running operations, consider designing for "partially updated" as a normal case by making pending work a first-class part of the shared data structure. Doing so enables greater concurrency and better responsiveness. It lets us shorten the length of time we need to hold exclusion on a given piece of shared data at any time, while still allowing for operations that take a long time to complete -- but can now run to completion without taking the data hostage the whole time.

We can express the pending work in a number of ways, including as a queue of work, as cookies representing the state of operations still in progress, or using lazy evaluation for its concurrency and responsiveness benefits as well as for its traditional optimization value. Then we can execute the work using one or more strategies that make sense; common ones include executing it interleaved with other work, during idle processing, asynchronously on one or more other threads, on use, or on demand.

It's true that we'll typically incur extra overhead to store and "rediscover" how to resume the longer operation at the appropriate point, but the benefits to overall system maintainability and understandability will often far outweigh the cost. Especially when the interleaved work may need to be canceled or restarted in response to other actions, as in the pagination and recalculation examples, it's easier to write the code correctly when the work still in progress is a well-defined part of the overall state of the system.

Acknowledgments

Thanks to Terry Crowley, director of development for Microsoft Office, for providing the motivation to write about this topic and several of the points and examples. Other examples were drawn from the development of Visual C++ 2010.

Notes

[1] H. Sutter. "Break Up and Interleave Work to Keep Threads Responsive" (Dr. Dobb's Digest, June 2009). Available online at http://www.ddj.com/architect/217801299.


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