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Package Contents
Parallelizing Simple Loops
Parallelizing Complex Loops
Parallelizing Data Flow and Dependence Graphs
Work Isolation
Exceptions and Cancellation
Floating-point Settings
Containers
Mutual Exclusion
Timing
Memory Allocation
The Task Scheduler
Design Patterns
Migrating from Threading Building Blocks (TBB)
Constrained APIs
Appendix A Costs of Time Slicing
Appendix B Mixing With Other Threading Packages
References
parallel_for_each Body semantics and requirements
parallel_sort ranges interface extension
TBB_malloc_replacement_log Function
Type-specified message keys for join_node
Scalable Memory Pools
Helper Functions for Expressing Graphs
concurrent_lru_cache
task_group extensions
The customizing mutex type for concurrent_hash_map
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oneTBB Benefits
Intel® oneAPI Threading Building Blocks (oneTBB) is a library that helps you leverage multi-core performance without having to be a threading expert. Typically you can improve performance for multi-core processors by implementing the key points explained in the early sections of the Developer Guide. As your expertise grows, you may want to dive into more complex subjects that are covered in advanced sections.
There are a variety of approaches to parallel programming, ranging from using platform-dependent threading primitives to exotic new languages. The advantage of oneTBB 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++. The library differs from typical threading packages in the following ways:
oneTBB enables you to specify logical paralleism instead of threads. Most threading packages require you to specify threads. Programming directly in terms of threads can be tedious and lead to inefficient programs, because threads are low-level, heavy constructs that are close to the hardware. Direct programming with threads forces you to efficiently map logical tasks onto threads. In contrast, the oneTBB run-time library automatically maps logical parallelism onto threads in a way that makes efficient use of processor resources.
oneTBB 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, oneTBB focuses on the particular goal of parallelizing computationally intensive work, delivering higher-level, simpler solutions.
oneTBB is compatible with other threading packages. Because the library is not designed to address all threading problems, it can coexist seamlessly with other threading packages.
oneTBB emphasizes scalable, data parallel programming. Breaking a program up into separate functional blocks, and assigning a separate thread to each block is a solution that typically does not scale well since typically the number of functional blocks is fixed. In contrast, oneTBB emphasizes data-parallel programming, enabling multiple threads to work on different parts of a collection. Data-parallel programming scales well to larger numbers of processors by dividing the collection into smaller pieces. With data-parallel programming, program performance increases as you add processors.
oneTBB relies on generic programming. Traditional libraries specify interfaces in terms of specific types or base classes. Instead, oneAPI Threading Building Blocks uses generic programming. The essence of generic programming is writing 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 sort that sorts a sequence abstractly defined in terms of iterators on the sequence. The requirements on the iterators are:
Provide random access
The expression *i<*j is true if the item pointed to by iterator i should precede the item pointed to by iterator j, and false otherwise.
The expression swap(*i,*j) swaps two elements.
Specification in terms of requirements on types enables the template to sort many different representations of sequences, such as vectors and deques. Similarly, the oneTBB templates specify requirements on types, not particular types, and thus adapt to different data representations. Generic programming enables oneTBB to deliver high performance algorithms with broad applicability.