Intel® Threading Building Blocks (Intel® TBB)

 Widely used C++ template library for task parallelism

  • Rich set of components to efficiently implement higher-level, task-based parallelism
  • Future-proof applications to tap multicore and many-core power
  • Compatible with multiple compilers and portable to various operating systems

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Simplify Parallelism with a Scalable Parallel Model

Intel® Threading Building Blocks (Intel® TBB) 4.2 is a widely used, award-winning C and C++ library for creating high performance, scalable parallel applications.

  • Enhance Productivity and Reliability - Rich set of components to efficiently implement higher-level, task-based parallelism
  • Gain Performance Advantage Today and Tomorrow - Future-proof applications to tap multicore and many-core power
  • Fits Within Your Environment - Advanced threading library, compatible with multiple compilers and portable to various operating systems

"Intel® TBB provided us with optimized code that we did not have to develop or maintain for critical system services. I could assign my developers to code what we bring to the software table—crowd simulation software.”
Michaël Rouillé, CTO, Golaem


Also available as open source

Awards

Flow Graph

Flow Graph

The flow graph feature provides a flexible and convenient API for expressing static and dynamic dependencies between computations. It is customizable for a wide variety of problems. It also extends the applicability of Intel® TBB to event-driven/reactive programming models.

Intel® TBB delivers high performing and reliable code with less effort than hand-made threading. Pre-tested algorithms, concurrent containers, synchronization primitives, and a scalable memory allocator simplify parallel application development.


Dynamic Task Scheduler
Design For Scaling

Dynamic Task Scheduler

Application performance can automatically improve as processor core count increases by using abstract tasks. The sophisticated Intel® TBB task scheduler dynamically maps tasks to threads to balance the load among available cores, preserve cache locality, and maximize parallel performance. The implementation supports C++ exceptions, task/task group priorities, and cancellation which are essential for large and interactive parallel C++ applications.

Dynamic task scheduler and parallel algorithms support nested and recursive parallelism as well as running parallel constructs side-by-side. This is useful for introducing parallelism gradually and helps independent implementation of parallelism in different components of an application.


Interoperable
#Pragma Simd and Intel® TBB can be used together

Cross Platform Support and Composability

Organizations that require cross platform support today or anticipate needing it in the future should consider Intel® TBB. It is validated and commercially supported on Windows*, Linux*, and OS X* platforms, using multiple compilers. It is also available on FreeBSD*, IA-based Solaris*, and PowerPC*-based systems via the open source community. Intel® TBB is optimized for multicore architectures and Intel® Xeon Phi™ coprocessor.

Intel® TBB is designed to co-exist with other threading packages and technologies. Different components of Intel® TBB can be used independently and mixed with other threading technologies.


Portability

Organizations can expand their customer base by using a production-ready, open solution for parallelism that is available on a broad range of platforms. Intel® TBB is validated and commercially supported on Windows*, Linux*, and OS X* platforms, using multiple compilers. It is also available on FreeBSD*, IA-based Solaris*, and PowerPC*-based systems via the open source community.


Top Community Support
Order the Intel® Threading Building Blocks book online at amazon.com

Top Community Support

The broad support from an involved community provides developers access to additional platforms and OS’s. Intel® Premier Support services and Intel® Support Forums provide confidential support, technical notes, application notes, and the latest documentation.

A complete documentation package and code samples are readily available both as a part of Intel® TBB installation and online at http://threadingbuildingblocks.org. The User Guide provides an introduction into Intel® TBB. The Design Patterns chapter in the User Guide covers common parallel programming patterns and how to implement them using Intel® TBB. The Reference Manual contains formal descriptions of all classes and functions implemented in Intel® TBB.

What’s New in Intel® TBB 4.2

FeatureBenefit
Support for Latest Intel Architectures

Take advantage of the newest features in Intel’s latest processors including Transactional Synchronization Extensions (TSX). Adds support for Intel® Xeon Phi™ coprocessor for Windows and Intel® Xeon™ Processor (Ivy Bridge-EP).

Selecting the best models for your application today will set a path for you to take full advantage of multicore and many-core performance without re-writing your code. Start today by implementing parallelism for today’s architecture and be ready for future architectures.

Lower memory overhead

Improved heuristics in the memory allocator reduce memory overhead by intelligently releasing unused or stale memory.

Improved handling of large memory requests

Improved handling of large (>8K-128MB) memory requests results in better performance when using frequent large memory allocations. Use of big memory pages can now be explicitly enabled via a function call or environment variable.

Better Fork Support

Fork safety through a user enabled API that ensures Intel® TBB worker threads are completed before executing a fork.

PPL* Compatibility

Improved compatibility with Parallel Patterns Library (PPL) by adding concurrent_unordered_multimap and concurrent_unordered_multiset API’s.

Windows* Store

Customers that use Intel® TBB in their applications can now submit and sell their app through the Windows Store.

Android* OS support

The Android OS is now supported as a target operating system for improved application performance and power efficiency. See Beacon Mountain for more Android developer tool details.

Rich set of components for Performance and Productivity

Rich set of components for Performance and Productivity
Intel® TBB 4.2 Pre-Tested Capabilities

Parallel Algorithms
Generic implementation of common parallel performance patterns

Generic implementations of parallel patterns such as parallel loops, flow graphs, and pipelines can be an easy way to achieve a scalable parallel implementation without developing a custom solution from scratch.

Concurrent Containers
Generic implementation of common idioms for concurrent access

Intel® TBB 4.2 concurrent containers are a concurrency-friendly alternative to serial data containers. Serial data structures (such as C++ STL containers) often require a global lock to protect them from concurrent access and modification; Intel® TBB concurrent containers allow multiple threads to concurrently access and update items in the container increasing allowed concurrency and improving an application’s scalability.

Synchronization Primitives
Exception-safe locks, condition variables, and atomic operations

Intel® TBB 4.2 provides a comprehensive set of synchronization primitives with different qualities that are applicable to common synchronization strategies. Exception-safe implementation of locks helps to avoid a dead-lock in programs which use C++ exceptions. Usage of Intel® TBB atomic variables instead of the C-style atomic API minimizes potential data races.

Scalable Memory Allocators
Scalable memory manager and false-sharing free memory allocator

The scalable memory allocator avoids scalability bottlenecks by minimizing access to a shared memory heap via per-thread memory pool management. Special management of large (>=8KB) blocks allows more efficient resource usage, while still offering scalability and competitive performance. The cache-aligned memory allocator avoids false-sharing by not allowing allocated memory blocks to split a cache line.

Create arbitrary task trees

When an algorithm cannot be expressed with high-level Intel® TBB 4.2 constructs, the user can choose to create arbitrary task trees. Tasks can be spawned for better locality and performance or en-queued to maintain FIFO-like order and ensure starvation-resistant execution.

Conditional Numerical Reproducibility

Ensure deterministic associativity for floating-point arithmetic results with the new Intel® TBB template function ‘parallel_deterministic_reduce’.

C++11 Support

Intel® TBB can be used with C++11 compilers and supports lambda expressions. For developers using parallel algorithms, lambda expressions reduce the time and code needed by removing the requirement for separate objects or classes.

Scalability with Future-proofing

  • Intel® TBB provides a simple and rapid way of developing robust parallel applications that abstracts platform details and threading mechanisms for performance that scales with increasing core counts
  • Intel® Threading Building Blocks yields linear scaling in these example applications

Select the right Intel® TBB license
Available Commercially and as open source

Select the right Intel® TBB license

  • Commercial Binary Distribution for customers who may require commercial support services. Attractive pricing available for academic, student and classroom usage.
  • Open Source Distribution can be used under GPLv2 with the runtime exception allowing usage in proprietary applications. Allows support for additional OSs and hardware platforms. Both source and binary forms are available for download from http://threadingbuildingblocks.org.
  • Custom license available if you require the ability to modify or distribute the commercial source code of Intel® TBB. Contact your Intel representative for more information.

Rich set of components for Performance and Productivity

Rich set of components for Performance and Productivity
Intel® TBB 4.2 Pre-Tested Capabilities

Parallel Algorithms
Generic implementation of common parallel performance patterns

Generic implementations of parallel patterns such as parallel loops, flow graphs, and pipelines can be an easy way to achieve a scalable parallel implementation without developing a custom solution from scratch.

Concurrent Containers
Generic implementation of common idioms for concurrent access

Intel® TBB 4.2 concurrent containers are a concurrency-friendly alternative to serial data containers. Serial data structures (such as C++ STL containers) often require a global lock to protect them from concurrent access and modification; Intel® TBB concurrent containers allow multiple threads to concurrently access and update items in the container increasing allowed concurrency and improving an application’s scalability.

Synchronization Primitives
Exception-safe locks, condition variables, and atomic operations

Intel® TBB 4.2 provides a comprehensive set of synchronization primitives with different qualities that are applicable to common synchronization strategies. Exception-safe implementation of locks helps to avoid a dead-lock in programs which use C++ exceptions. Usage of Intel® TBB atomic variables instead of the C-style atomic API minimizes potential data races.

Scalable Memory Allocators
Scalable memory manager and false-sharing free memory allocator

The scalable memory allocator avoids scalability bottlenecks by minimizing access to a shared memory heap via per-thread memory pool management. Special management of large (>=8KB) blocks allows more efficient resource usage, while still offering scalability and competitive performance. The cache-aligned memory allocator avoids false-sharing by not allowing allocated memory blocks to split a cache line.

Create arbitrary task trees

When an algorithm cannot be expressed with high-level Intel® TBB 4.2 constructs, the user can choose to create arbitrary task trees. Tasks can be spawned for better locality and performance or en-queued to maintain FIFO-like order and ensure starvation-resistant execution.

Conditional Numerical Reproducibility

Ensure deterministic associativity for floating-point arithmetic results with the new Intel® TBB template function ‘parallel_deterministic_reduce’.

C++11 Support

Intel® TBB can be used with C++11 compilers and supports lambda expressions. For developers using parallel algorithms, lambda expressions reduce the time and code needed by removing the requirement for separate objects or classes.

Scalability with Future-proofing

  • Intel® TBB provides a simple and rapid way of developing robust parallel applications that abstracts platform details and threading mechanisms for performance that scales with increasing core counts
  • Intel® Threading Building Blocks yields linear scaling in these example applications

Select the right Intel® TBB license
Available Commercially and as open source

Select the right Intel® TBB license

  • Commercial Binary Distribution for customers who may require commercial support services. Attractive pricing available for academic, student and classroom usage.
  • Open Source Distribution can be used under GPLv2 with the runtime exception allowing usage in proprietary applications. Allows support for additional OSs and hardware platforms. Both source and binary forms are available for download from http://threadingbuildingblocks.org.
  • Custom license available if you require the ability to modify or distribute the commercial source code of Intel® TBB. Contact your Intel representative for more information.

Videos to get you started:

  • Introduction to Intel® Threading Building Blocks

Register for future Webinars


Previously recorded Webinars:

  • Creating parallel reactive and streaming applications with the Intel® Threading Building Blocks (Intel® TBB) flow graph

More Tech Articles

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Supplemental Documentation

Threading Building Blocks book
By James Reinders (Intel)Posted 05/22/20080
Intel® Threading Building Blocks: Outfitting C++ for Multi-core Processor Parallelism, by James Reinders, © 2007      Sample Code: Download the sample code for the book here. Sample Chapter: Download the sample chapter from the book here. Discussion Forum: You can discuss your experiences u...
General Questions about TBB
By adminPosted 10/25/20070
Main Page Threading Building Blocks FAQ Home Page General Questions about TBB Using TBB Reporting Issues with TBB Contributing to TBB Contents 1Introductory FAQ 1.1What did Intel announce on July 24 2007? 1.2Why is this significant? 1.3Why did you not open source originally? 1.4Why open sourc...
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Libraries identified with wrong c++ library, std::runtime_error crash results
By Russell W.4
Heads up: With Composer XE 2013 sp1, Intel shipped two versions of the tbb libraries: one set linked to libstdc++ (the older C++0x gnu library), another linked to libc++ (the newer, C++11-compliant, LLVM library). They're incorrectly identified by the directory in which they're installed, and if you use the wrong one, the result is that any use of std::runtime_error may crash in the class destructor — whether or not you even throw the exception. The tbb problem: /opt/intel/tbb/lib/*.dylib contains the libraries linked to libc++ /opt/intel/tbb/lib/libc++/*.dylib contains the libraries linked to libstdc++  But it's an Apple bug that it matters. Per https://developer.apple.com/library/mac/technotes/tn2185/_index.html#//a... in the section titled "Which std C++ symbols are set in stone, and which aren't (ABI issues)", this issue is specifically addressed, and std::runtime_error is supposed to be ABI compatible across runtime changes. The list of C++ classes that are supposed to be ABI c...
Graph nodes and core affinity
By Alex S.9
Hi, I'm considering to use TBB for its graph functionality. I need to strictly bind every graph node to specific CPU core/thread (for example, there could be 10 graph nodes and 3 cores/threads). It seems to me that TBB does not provide such a level of control over core/thread affinity for graph nodes, am I wrong? Thanks a lot.
Can C++11 thread_local be used with TBB
By Gennadiy Rishkin1
Can I expect that C++11 thread_local will have the expected effect. I assume that using thread_local will not cause any surprises since TBB is designed to be a library-based solution that uses the platform's threading. So, for example:  // Forward declaration of class Class Test; // Thread local for thread safety thread_local *p_global; ... ... ... int main() { // Create several instances of Test // Assign pointer to each instance to p_global; }Will every thread have its own copy of p_global?
Multiple pre-emptive thread pools in TBB
By stephen d.1
I have a requirement to create a number of real time processing chains one running at n Hz and others running at x, y and z Hz in a single process. Where x, y and z are some multiple (not necessarily a simple multiple) of n. For example one chain running at 1Hz and others running at 3Hz and 4Hz. Each processing chain needs to make use of TBB to parallelize some of their computations etc. and so needs to have a pool of TBB worker threads that match the number of hardware processors, but the higher frequency chains need to pre-empt the lower frequency chains in order for the system to work. How can this be achieved using TBB? It seems from the documentation that it is not possible to create competing pools of TBB workers that will pre-empt each other, since TBBs task groups etc. seem to share a single pool of real threads and there does not appear to be anyway to set the real system priority of a TBB worker thread, or am I missing something?
Flow Graph - 1 Input -> 0..* Output
By ronag892
I have a scenario where I need a flow graph node that a single input token, performs some processing on that token and the output none or several output tokens. What would be the best way to achieve this? on input data       while data decode item           output item  
Sort two arrays simultaneously using TBB sort
By Pavanakumar M.6
Hi The problem I am having is as follows, 1) I have arrays A and B of size N. 2) I have to sort A and also make B in the same sorted permutation as that of A. This is how currently I have implemented in my code using TBB sort. But I find that the in-place re-ordering algorithm is a serial implementation and is a major bottleneck in my code. How can I do this more efficiently using only TBB sort and possibly avoid the use of permutation array ? std::vector<unsigned> order(N); std::vector<double> A(N), B(N);   for( unsigned i = 0; i < N; ++i ) order[i] = i;   CompFunctor compare(A); tbb::parallel_sort( order.begin(), order.end(), compare ); /// Runs in parallel InPlacePermute( &A[0], &B[0], &order[0], N ); /// Runs in serial   /// Comparison functor definition struct CompFunctor {   CompFunctor( std::vector<double> &a )   : _data(a) {  }     inline bool operator()( uintT i, uintT j ) const {     return _data[i] < _data[j];   }     private:   std...
tbb concurrent_hash_map in shared memory
By yaron y.3
I am using dpdk shared memory. So it "map the hugepage memory to the same virtual addresses in client and server applications"(exact memory mapping). I need a hash between processes. I can see from your documenatation that concurrent_hash_map  is good between threads. If i use dpdk shared memory, and initialize concurrent_hash_map  in shared memory, will i be able to use concurrent_hash_map in shared memory same way as between threads?
tbb:task has its own context?
By Oliver K.27
Does a tbb::task have its own context, e.g. its own stack like coroutines or fibers? I try to evaluate if it is possible to write code similiar to GO-routines with TBB, e.g. each task has it's own stack, can be suspended iit's current stackframe and resumed later (maybe while waiting for some external event).  

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  • What is parallelism?
  • By way of analogy, if you’re trying to cook a multi-course meal and all you have is a single-burner stove, you can only cook one part of the meal at a time. If you have a stove with four burners, you can cook four things at once, bringing them to the table all at the same time. Software parallelism is similar. There are different techniques used to achieve parallelism—threading is one of them. The idea is to take an application and, where it is possible, split up the program so different parts can run simultaneously on different processors in a multicore configuration. Then, part of the application brings all the parts together to present the application results.

    Task-based parallelism is a mechanism to execute several work items (tasks) in parallel.

  • What is threading?
  • Threading is a kind of parallelism. It’s a technique used by software developers to decompose applications into parts that can be run simultaneously on a computer with multiple processors or multiple cores. Threaded applications run on a single computer, again with multiple cores, under the management of a single operating system.

  • Why does C++ need Intel® TBB?
  • C++, like other popularly used languages, was not designed to express parallelism. Fortunately, C++ is extensible using templates. Developers liked the OpenMP concept, whereby they could get scalable performance without adding much new code, yet needed something that was more conducive to the object oriented/template based programming style of C++. Developers wanted us to do something about parallel containers, and algorithms – so templates were a perfect fit. The ‘generic programming’ style which STL uses – which allows components to be easily composed without giving up performance appealed a great deal to us. We settled on extending C++ in a fashion similar to how STL extended C++.

    Abstraction is important to developers. Using native threads, doing your own explicit thread management, is like assembly language for parallelism. TBB is the abstraction we need for many reasons. Programming for parallelism using native threads is tedious, error prone and not portable. It also is seldom as scalable as it could be, because high levels of scalability are more difficult to program.

  • Does Intel charge run-time fees or royalties for its libraries?
  • No.

  • Are there analysis tools that understand the semantics of Intel® TBB?
  • Yes. Applications threaded with Threading Building Blocks can be analyzed with Intel® VTune™ Amplifier XE 2013 and Intel® Inspector XE 2013. Intel® Advisor XE 2013, available in any Intel® Studio XE product, can help find regions with the greatest performance potential from parallelism.

  • Where can I get an evaluation copy of Intel® TBB?
  • 30 day evaluation versions of Intel® Software Development Products are available for free download. You can get free support during the evaluation period by creating an Intel® Premier Support account after requesting the evaluation license. Click here for Windows, Linux or OS X*

  • Are there any books to help developers better understand how to use Intel® TBB?
  • A book on Intel Threading Building Blocks, by James Reinders has been published by O’Reilly Media. The tutorial, examples and other documentation which come with the download are excellent resources.

  • How do the commercial and open source versions differ?
  • Currently there are no product differences between the commercial and open source version. We maintain one source base and do builds for both from the same source base. The versions differ in the level support.

    Intel® TBB is offered commercially for Windows, Linux or OS X* customers who want additional support or cannot follow the open source GPLv2 with the runtime exception license. With a purchase, customers will receive one year of product updates available at the Intel® Registration Center and one year of technical support from Intel® Premier Support, our interactive issue management and communication website. This premier support service allows you to submit questions to Intel engineers. In addition to Intel® Premier Support, we have user forums, technical notes, application notes, and documentation available on our website.

    The open source support includes user forums, documentation and additional resources available on our website, threadingbuildingblocks.org.

  • Where can I get more information on the TBB open source offering?
  • For more information, visit threadingbuildingblocks.org. The site includes TBB source code, documentation, user forums, blogs, podcasts, articles, white papers and support areas.

  • What is parallelism?
  • By way of analogy, if you’re trying to cook a multi-course meal and all you have is a single-burner stove, you can only cook one part of the meal at a time. If you have a stove with four burners, you can cook four things at once, bringing them to the table all at the same time. Software parallelism is similar. There are different techniques used to achieve parallelism—threading is one of them. The idea is to take an application and, where it is possible, split up the program so different parts can run simultaneously on different processors in a multicore configuration. Then, part of the application brings all the parts together to present the application results.

    Task-based parallelism is a mechanism to execute several work items (tasks) in parallel.

  • What is threading?
  • Threading is a kind of parallelism. It’s a technique used by software developers to decompose applications into parts that can be run simultaneously on a computer with multiple processors or multiple cores. Threaded applications run on a single computer, again with multiple cores, under the management of a single operating system.

  • Why does C++ need Intel® TBB?
  • C++, like other popularly used languages, was not designed to express parallelism. Fortunately, C++ is extensible using templates. Developers liked the OpenMP concept, whereby they could get scalable performance without adding much new code, yet needed something that was more conducive to the object oriented/template based programming style of C++. Developers wanted us to do something about parallel containers, and algorithms – so templates were a perfect fit. The ‘generic programming’ style which STL uses – which allows components to be easily composed without giving up performance appealed a great deal to us. We settled on extending C++ in a fashion similar to how STL extended C++.

    Abstraction is important to developers. Using native threads, doing your own explicit thread management, is like assembly language for parallelism. TBB is the abstraction we need for many reasons. Programming for parallelism using native threads is tedious, error prone and not portable. It also is seldom as scalable as it could be, because high levels of scalability are more difficult to program.

  • Does Intel charge run-time fees or royalties for its libraries?
  • No.

  • Are there analysis tools that understand the semantics of Intel® TBB?
  • Yes. Applications threaded with Threading Building Blocks can be analyzed with Intel® VTune™ Amplifier XE 2013 and Intel® Inspector XE 2013. Intel® Advisor XE 2013, available in any Intel® Studio XE product, can help find regions with the greatest performance potential from parallelism.

  • Where can I get an evaluation copy of Intel® TBB?
  • 30 day evaluation versions of Intel® Software Development Products are available for free download. You can get free support during the evaluation period by creating an Intel® Premier Support account after requesting the evaluation license. Click here for Windows, Linux or OS X*

  • Are there any books to help developers better understand how to use Intel® TBB?
  • A book on Intel Threading Building Blocks, by James Reinders has been published by O’Reilly Media. The tutorial, examples and other documentation which come with the download are excellent resources.

  • How do the commercial and open source versions differ?
  • Currently there are no product differences between the commercial and open source version. We maintain one source base and do builds for both from the same source base. The versions differ in the level support.

    Intel® TBB is offered commercially for Windows, Linux or OS X* customers who want additional support or cannot follow the open source GPLv2 with the runtime exception license. With a purchase, customers will receive one year of product updates available at the Intel® Registration Center and one year of technical support from Intel® Premier Support, our interactive issue management and communication website. This premier support service allows you to submit questions to Intel engineers. In addition to Intel® Premier Support, we have user forums, technical notes, application notes, and documentation available on our website.

    The open source support includes user forums, documentation and additional resources available on our website, threadingbuildingblocks.org.

  • Where can I get more information on the TBB open source offering?
  • For more information, visit threadingbuildingblocks.org. The site includes TBB source code, documentation, user forums, blogs, podcasts, articles, white papers and support areas.

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