Introducing tbb::parallel_invoke

Alexey Murashov  @ 08:52 EDT | April 03, 2009
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Running multiple functions in parallel

I would like to introduce you to a new template function recently added to Threaded Building Blocks -- tbb::parallel_invoke. It provides TBB users a simple way to run several functions in parallel. So, for example, if you have three functions that do some work and you would like to run them simultaneously, you may write the following TBB code (I skipped some things like scheduler initialization): 


    void Function1();
    void Function2();
    void Function3();


    void RunFunctions() {
        tbb::parallel_invoke(Function1, Function2, Function3);
    }

Looks simple, doesn't it? You do not have to define any specific classes or write extra code to use parallel_invoke. It is possible to pass function pointers or functor objects to the template function using the same syntax: 


    void (*FuncPtr1)(void), (*FuncPtr2)(void);
    void RunFuncPtrs
        tbb::parallel_invoke(FuncPtr1, FuncPtr2);
    }
    class FunctorClass {
    public:
        void operator() () const {}
    } Functor1, Functor2;
    void RunFunctors
        tbb::parallel_invoke(Functor1, Functor2);
    }

It also supports lambda functions available in C++0x: 


    tbb::parallel_invoke(
        []() { std::cout < < "Hello!"; },
        []() { std::cout < <  "Greetings!"; }
    );

Up to 10 functions can be run by parallel_invoke: 


    tbb::parallel_invoke(Func1, Func2, Func3, Func4, Func5, Func6, Func7, Func8, Func9, Func10);

Obviously, you could write your own code to run the functions in parallel, but when you use parallel_invoke you get all usual benefits from TBB. Since parallel_invoke uses a task-based approach, the code will run on any platform and on different numbers of cores.

However in order to be run by parallel_invoke, the functions should have no arguments and no return value. The second restriction is not strict -- actually you can pass a non-void function, but the return value will be ignored, so doing this is not a good design.

tbb::parallel_invoke also includes exception handling and cancellation support. It behaves like other TBB template algorithms: 


    try{
        tbb::parallel_invoke (Function1, Function2, Function3)
    }catch (tbb::captured_exception &exc) {
        // Processing exc
    }

And now a little bit about implementation details. As I mentioned above, TBB tasks are used, so each user-defined function is run by a separate task. The tasks form a tree, each leaf runs up to three user functions. For example, a 5 functions version looks like this (each box represents a task):

Note each sub-root task runs a user-defined function in its body to optimize the number of tasks. The most complicated case with 10 user functions looks like:

The tasks aren't blocked at the inner level. Sub-root tasks use continuation-passing style to prevent it; wait_for_all is called only at the top level.

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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.