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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."
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 ...
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 ...
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 ...
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 ...
OpenMP is an implementation of multithreading, a method of parallelizing whereby a primary thread (a series of instructions executed consecutively) forks a specified number of sub-threads and the system divides a task among them. The threads then run concurrently, with the runtime environment allocating threads to different processors.
One benefit of a thread pool over creating a new thread for each task is that thread creation and destruction overhead is restricted to the initial creation of the pool, which may result in better performance and better system stability. Creating and destroying a thread and its associated resources can be an expensive process in terms of time.