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Threading Building Blocks with C++


You don't have to be a threading expert to get the most out of your C++ applications. You can use Intel Threading Building Blocks to get the best performance out of multi-core processors. This two-part article introduces you to the most important concepts and benefits of this approach. It is excerpted from chapter one of the book Intel Threading Building Blocks: Outfitting C++ for Multi-core Processor Parallelism, written by James Reinders (O'Reilly; ISBN: 9780596514808). Copyright © 2007 O'Reilly Media, Inc. All rights reserved. Used with permission from the publisher. Available from booksellers or direct from O'Reilly Media.

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By: O'Reilly Media
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April 14, 2011
TABLE OF CONTENTS:
  1. · Threading Building Blocks with C++
  2. · Benefits of Thread Building Blocks in C

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Threading Building Blocks with C++ - Benefits of Thread Building Blocks in C
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As mentioned, the goal of a programmer in a modern computing environment is scalability: to take advantage of both cores on a dual-core processor, all four cores on a quad-core processor, and so on. Threading Building Blocks makes writing scalable applications much easier than it is with traditional threading packages.

There are a variety of approaches to parallel programming, ranging from the use of platform-dependent threading primitives to exotic new languages. The advantage of Threading Building Blocks is that it works at a higher level than raw threads, yet does not require exotic languages or compilers. You can use it with any compiler supporting ISO C++. This library differs from typical threading packages in these ways:

Threading Building Blocks enables you to specify tasks instead of threads

Most threading packages require you to create, join, and manage threads. Programming directly in terms of threads can be tedious and can lead to inefficient programs because threads are low-level, heavy constructs that are close to the hardware. Direct programming with threads forces you to do the work to efficiently map logical tasks onto threads. In contrast, the Threading Building Blocks runtime library automatically schedules tasks onto threads in a way that makes efficient use of processor resources. The runtime is very effective at load-balancing the many tasks you will be specifying.

By avoiding programming in a raw native thread model, you can expect better portability, easier programming, more understandable source code, and better performance and scalability in general.

Indeed, the alternative of using raw threads directly would amount to programming in the assembly language of parallel programming. It may give you maximum flexibility, but with many costs.

Threading Building Blocks targets threading for performance

Most general-purpose threading packages support many different kinds of threading, such as threading for asynchronous events in graphical user interfaces. As a result, general-purpose packages tend to be low-level tools that provide a foundation, not a solution. Instead, Threading Building Blocks focuses on the particular goal of parallelizing computationally intensive work, delivering higher-level, simpler solutions.

Threading Building Blocks is compatible with other threading packages

Threading Building Blocks can coexist seamlessly with other threading packages. This is very important because it does not force you to pick among Threading Building Blocks, OpenMP, or raw threads for your entire program. You are free to add Threading Building Blocks to programs that have threading in them already. You can also add an OpenMP directive, for instance, somewhere else in your program that uses Threading Building Blocks. For a particular part of your program, you will use one method, but in a large program, it is reasonable to anticipate the convenience of mixing various techniques. It is fortunate that Threading Building Blocks supports this.

Using or creating libraries is a key reason for this flexibility, particularly because libraries are often supplied by others. For instance, Intelís Math Kernel Library (MKL) and Integrated Performance Primitives (IPP) library are implemented internally using OpenMP. You can freely link a program using Threading Building Blocks with the Intel MKL or Intel IPP library.

Threading Building Blocks emphasizes scalable, data-parallel programming

Breaking a program into separate functional blocks and assigning a separate thread to each block is a solution that usually does not scale well because, typically, the number of functional blocks is fixed. In contrast, Threading Building Blocks emphasizes data-parallel programming, enabling multiple threads to work most efficiently together. Data-parallel programming scales well to larger numbers of processors by dividing a data set into smaller pieces. With data-parallel programming, program performance increases (scales) as you add processors. Threading Building Blocks also avoids classic bottlenecks, such as a global task queue that each processor must wait for and lock in order to get a new task.

Threading Building Blocks relies on generic programming

Traditional libraries specify interfaces in terms of specific types or base classes. Instead, Threading Building Blocks uses generic programming, which is defined in Chapter 12. The essence of generic programming is to write the best possible algorithms with the fewest constraints. The C++ Standard Template Library (STL) is a good example of generic programming in which the interfaces are specified by requirements on types. For example, C++ STL has a template function that sorts a sequence abstractly, defined in terms of iterators on the sequence.

Generic programming enables Threading Building Blocks to be flexible yet efficient. The generic interfaces enable you to customize components to your specific needs.  

Please check back for the conclusion to this article.


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