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A concurrent programming language is defined as one which uses the concept of simultaneously executing processes or threads of execution as a means of structuring a program. A parallel language is able to express programs that are executable on more than one processor.
"Embarrassingly" is used here to refer to parallelization problems which are "embarrassingly easy". [4] The term may imply embarrassment on the part of developers or compilers: "Because so many important problems remain unsolved mainly due to their intrinsic computational complexity, it would be embarrassing not to develop parallel implementations of polynomial homotopy continuation methods."
A task-parallel model focuses on processes, or threads of execution. These processes will often be behaviourally distinct, which emphasises the need for communication. Task parallelism is a natural way to express message-passing communication. In Flynn's taxonomy, task parallelism is usually classified as MIMD/MPMD or MISD.
The speculative thread will need to be discarded or re-run if its presumptions on the input state prove to be invalid. It is a dynamic (runtime) parallelization technique that can uncover parallelism that static (compile-time) parallelization techniques may fail to exploit because at compile time thread independence cannot be guaranteed.
An example is a computer program that processes files. A part of that program may scan the directory of the disk and create a list of files internally in memory. After that, another part of the program passes each file to a separate thread for processing. The part that scans the directory and creates the file list cannot be sped up on a ...
The other thread is pushed onto the bottom of the deque, but the processor continues execution of its current thread. Initially, a computation consists of a single thread and is assigned to some processor, while the other processors start off idle. Any processor that becomes idle starts the actual process of work stealing, which means the ...
Reinders, James (July 2007), Intel Threading Building Blocks: Outfitting C++ for Multi-core Processor Parallelism (Paperback ed.), Sebastopol: O'Reilly Media, ISBN 978-0-596-51480-8 Voss, M. (October 2006), Demystify Scalable Parallelism with Intel Threading Building Blocks' Generic Parallel Algorithms , archived from the original on 2012-02-05 ...
Minimizing the depth/span is important in designing parallel algorithms, because the depth/span determines the shortest possible execution time. [8] Alternatively, the span can be defined as the time T ∞ spent computing using an idealized machine with an infinite number of processors. [9] The cost of the computation is the quantity pT p. This ...