30
edits
Changes
→Fighting Mongooses
That said, while the C++11 standard solutions are considered ‘experimental’, they are largely functional and comparable to TBB functionality in most cases in terms of efficiency.
==STL==
The [https://en.wikipedia.org/wiki/Standard_Template_Library Standard Template Library (STL)] is a software library for the C++ programming language that influenced many parts of the C++ Standard Library. It provides four components called algorithms, containers, functional, and iterators. As early as 2006, parallelization has been being pushed for inclusion in the STL for C++, to some success (more on this later).
==TBB==
TBB (Threading Building Blocks) is a high-level, general purpose, feature-rich library for implementing parametric polymorphism using threads. It includes a variety of containers and algorithms that execute in parallel and has been designed to work without requiring any change to the compiler. Uses task parallelism, Vectorization not supported.
==BOOST==
Since 2006 an intimate week long annual conference related to Boost called [http://cppnow.org/ C++ Now] has been held in Aspen, Colorado each May. Boost has been a participant in the annual [https://developers.google.com/open-source/soc/?csw=1 Google Summer of Code] since 2007.
==STD(PPL) – since Visual Studio 2015==
==Auto-Parallelizer==
Multiple example loops [https://msdn.microsoft.com/en-ca/library/hh872235.aspx here]
==C++ AMP (C++ Accelerated Massive Parallelism)==
C++ AMP accelerates the execution of your C++ code by taking advantage of the data-parallel hardware that's commonly present as a graphics processing unit (GPU) on a discrete graphics card. The C++ AMP programming model includes support for multidimensional arrays, indexing, memory transfer, and tiling. It also includes a mathematical function library. You can use C++ AMP language extensions to control how data is moved from the CPU to the GPU and back.
==AMP Tiling==
Tiling divides threads into equal rectangular subsets or tiles. If you use an appropriate tile size and tiled algorithm, you can get even more acceleration from your C++ AMP code. The basic components of ==*A note on AMP and tiling are:== · tile_static variablesAMP does not properly compile on the visual studio 2015 platform, it must be run using libraries before VS2015. Access Tiling does not seem to data in tile_static memory can be significantly faster than access to data in supported on the global space (array or array_view objects)Intel Compiler as well.· ==A simple for_Each Comparison== [https[File://msdnForEachCode.microsoftPNG]] [[File:ForEachTable.com/en-ca/library/hh308384.aspx tile_barrier::wait MethodPNG]]. A call to tile_barrier::wait suspends execution of the current thread until all of the threads in the same tile reach the call to tile_barrier::wait· Local and global indexing. You have access to the index of the thread relative to the entire array_view or array object and the index relative to the tile.· tiled_extent Class and tiled_index Class. You use a tiled_extent object instead of an extent object in the parallel_for_each call. You use a tiled_index object instead of an index object in the parallel_for_each call[[File:ForEachChart.PNG]]
==Comparing STL/PPL to TBB: Sorting Algorithm==
The clear differentiation in the code is that TBB does not have to operate using random access iterators, while STL’s parallel solution to sorting (and serial solution) does. If TBB sort is run using a vector instead of a simple array, you will see more even times.
==Conclusion==
The conclusion to draw when comparing TBB to STL, in their current states, is that you ideally should use TBB over STL. STL parellelism is still very experimental and unrefined, and will likely remain that way until we see the release of C++17. However, following C++17’s release, using the native parallel library solution will likely be the ideal road to follow.
==References==
http://www.boost.org/
https://scs.senecac.on.ca/~gpu621/pages/content/tbb__.html
Auto-Parallelization and Auto-Vectorization: https://msdn.microsoft.com/en-ca/library/hh872235.aspx
Concurrency Runtime:
https://msdn.microsoft.com/en-ca/library/dd504870.aspx
Accelerated Massive Parallelism (AMP): https://msdn.microsoft.com/en-ca/library/hh265137.aspx
Using Lambdas, Function objects and Restricted functions:
https://msdn.microsoft.com/en-ca/library/hh873133.aspx
Using Tiles:
https://msdn.microsoft.com/en-ca/library/hh873135.aspx
Concurrency Runtime Overview:
https://msdn.microsoft.com/en-us/library/ee207192.aspx
----
