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'''Entry on: November 30th 2016'''
==Introduction==
There are decisions to make regarding, if even you SHOULD parallelize your program, what method or libraries should you use? To compare two specific possibilties, is it better to use standardized C++ solutions, or Thread Building Blocks?
The main issue with parallelism as it exists currently in C++ is that it is very much a Wild West scenario where external companies are the ones flagshipping the parallel movement. That isn’t to say that there are no options for parallelization in C++ natively, but aside from experimental and non-finalized solutions there is not much more to work with other than std::thread to manually create threads, or the Boost library.
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==
BOOST provides free peer-reviewed portable C++ source libraries. Boost emphasizes libraries that work well with the C++ Standard Library. Boost libraries are intended to be widely useful, and usable across a broad spectrum of applications. Ten Boost libraries are included in the [http://www.open-std.org/jtc1/sc22/wg21/ C++ Standards Committee's] Library Technical Report ([http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2005/n1745.pdf TR1]) and in the new C++11 Standard. C++11 also includes several more Boost libraries in addition to those from TR1. More Boost libraries are proposed for standardization in C++17.
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==
==Resource Manager==
The role of the Resource Manager is to manage computing resources, such as processors and memory. The Resource Manager responds to workloads as they change at runtime by assigning resources to where they can be most effective.
The Resource Manager serves as an abstraction over computing resources and primarily interacts with the Task Scheduler. Although you can use the Resource Manager to fine-tune the performance of your libraries and applications, you typically use the functionality that is provided by the Parallel Patterns Library, the Agents Library, and the Task Scheduler. These libraries use the Resource Manager to dynamically rebalance resources as workloads change.
==Asynchronous Agents Library==
Not relevant to this comparison, .NET Framework.
==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 tiling are:· tile_static variables. Access to data in tile_static memory can be significantly faster than access to data in the global space (array or array_view objects).· [https://msdn.microsoft.com/en-ca/library/hh308384.aspx tile_barrier::wait Method]. 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.
==*A note on AMP and tiling== AMP does not properly compile on the visual studio 2015 platform, it must be run using libraries before VS2015. Tiling does not seem to be supported on the Intel Compiler as well. ==A simple for_Each Comparison== [[File:ForEachCode.PNG]] [[File:ForeachTable.PNG]] [[File:PBTResultsForEachChart.pngPNG]]
==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 TBBover 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 Parallel Patterns Library:https://msdn.microsoft.com/en-us/library/dd492418.aspx 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
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