Break Up and Interleave Work to Keep Threads Responsive

Herb Sutter  @ 05:49 EDT | June 15, 2009
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Breaking up is hard to do, but interleaving can be even subtler

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.

In a recent article, we covered reasons why threads should strive to make their data private and communicate using asynchronous messages.[1] Each thread that needs to receive input should have an inbound message queue and organize its work around an event-driven message pump mainline that services the queue requests, idealized as follows: 


// An idealized thread mainline
//
do {
    message = queue.pop() // get the message
                          // (might wait)
    message->run();    // and execute it
} while( !done );         // check for exit

But what happens when this thread must remain responsive to new incoming messages that have to be handled quickly, even when we're in the middle of servicing an earlier lower-priority message that may take a long time to process?

If all the messages must be handled on this same thread, then we have a problem. Fortunately, we also have two good solutions, both of which follow the same basic strategy: somehow break apart the large piece of work to allow the thread to perform other work in between, interleaved between the chunks of the large item. Let's consider the two major ways to implement that interleaving, and their respective tradeoffs in the areas of fairness and performance.

Example: Breaking Up a Potentially Long-Running Operation

Consider this potentially expensive message we might be asked to execute: 


// A simplified message type to accomplish some
// long operation
//
class LongOperation : public Message {
public:
   void run() {
      LongHelper helper = GetHelper();
// issue: what if this loop could take a long time?
   for( int i = 0; i < items.size(); ++i ) {
      helper->render( items[i] );
   }
   helper->print();
 }
}

This thread may be a background worker that runs all the work we want to get off the GUI thread (see [1]). Alternatively, in cases where it is impossible to obey the good hygiene of getting all significant work off the GUI thread (for example, because for some reason the work may need to happen on the GUI thread itself for legacy or OS-specific reasons), this thread may be the GUI itself. Whatever the case, what matters is that to remain responsive to other messages we need to break up LongOperation.run into smaller pieces and interleave them with the processing of other messages.

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Real World Parallelism Webinar Series

  • November 17, 2009
    Visual Effects for Animation - presented by DreamWorks Animation
    Speaker: Ron Henderson (Bio)

    Ron Henderson manages the FX Tools group at DreamWorks Animation, where he is responsible for developing physical simulation and procedural modeling tools. These systems have been used for key visual effects in recent films such as Kung Fu Panda and Monsters vs. Aliens (March 2009).

    Prior to joining DreamWorks in 2002 he was a senior scientist at Caltech with a joint appointment to the Applied Math and Aeronautics departments, where he worked on efficient techniques for the direct numerical simulation of fluid turbulence.

    Abstract:
    In this webinar, Ron Henderson will show examples of visual effects, from hair and feathers to smoke and fire, from a variety of DreamWorks Animation feature films. He will discuss in general terms the kinds of techniques used to achieve particular visual effects. Finally, Henderson will show a detailed breakdown of the dam-breaking scene from Madagascar: Escape 2 Africa, demonstrating how different elements of key frame animation, simulation, and rendering are combined in a real production shot.

  • December 1, 2009
    A Quick and Easy Way to Parallelize a Legacy Codebase with Intel® Threading Building Blocks (TBBs)
    Speaker: Bernard Laberge, Avid, Senior Principal Engineer (Bio)

    Bernard Laberge is a senior principal engineer in the video editors division at Avid. During his seven years with the company he has been actively involved in the replacement of the legacy video processing engines used by Avid editors with a common hardware-abstracted, component-based video processing engine currently running on the CPU with SIMD optimized code, GPU, and dedicated hardware.

    Abstract:
    Learn how to overcome the limitations of a thread-based scheduler, including dealing with the absence of recursive parallelism support and the inefficient handling of unbalanced processing load. Bernard Laberge addresses how Avid resolved the expensive refactoring of their thread-based scheduler into a task-based solution by choosing Intel® Threading Building Blocks (TBBs). He explores how Avid was able to easily integrate the Intel TBBs into their video editor applications and more than 5 million lines of code.

  • December 15, 2009
    How to Use Intel® Parallel Studio to Streamline Code Development in a Multicore Environment
    Speaker: Matt Dunbar, Director for Performance Technology, SIMULIA (Bio)

    Matt Dunbar is the director for performance technology at SIMULIA. Since joining the company in 1993, he has worked on parallelization of the Abaqus suite of products, initially for shared memory architectures and more recently for distributed memory architectures. Dunbar has also been intimately involved in selecting both the hardware and software tools used in the development of the Abaqus product line.

    Abstract:
    Resolve elusive, costly multithreading errors quickly and efficiently with Intel® Parallel Studio. While many coding problems that lead to bugs in software applications are typically straightforward logic errors, errors in managing memory and in multithreading code can sometimes take weeks to months to diagnose and fix. Matt Dunbar explores how and why taking advantage of multicore processors through multithreaded code is critical for compute-intensive applications. While spotlighting his work on SIMULIA's Abaqus finite element solver, Dunbar addresses the need for multicore execution and shares his experiences using Intel Parallel Studio to streamline code development in a multicore environment.