parallel computing

(redirected from Parallelization)
Also found in: Dictionary, Thesaurus, Legal.

parallel computing

parallel computing

Solving a problem with multiple computers or computers made up of multiple processors. It is an umbrella term for a variety of architectures, including symmetric multiprocessing (SMP), clusters of SMP systems, massively parallel processors (MPPs) and grid computing. See SMP, MPP, clustering, pipeline processing, vector processor, hypercube and grid computing.
References in periodicals archive ?
The upgrades intelligently analyze the design of a customers software application built in Appian and automatically optimizes the code for parallelization across multiple CPU cores.
This kind of parallelization can improve the operational efficiency of both the CPU and the GPU because they can simultaneously perform their instructions for image frames.
The issue with this method is that the effects of parallelization become progressively harder to obtain as the time required to share data between computers increases when more than 10 computers are used at the same time.
Figure 2: Functional parallelization lets each subsystem take its own thread and core.
However, the use of the kind of bounded parallelization available in these architectures has not been closely studied for most AI applications.
Concepts from graph theory are often used in theoretical formalization of parallelization issues [7,8,9,10,11,12].
Readers are assumed to have at least some experience programming MATLAB, but not sufficient background in programming or computer architecture for parallelization.
He adds, "Falling memory prices and increased acceptance of cloud computing are both playing into the hands of Kognitio, and its parallelization capabilities could further its differentiation.
8220;We're excited to make our parallelization tools available to the OpenCL development community.
To minimize computational cost and maximize parallelization efficiency, basic idea throughout the dissertation is to keep the locality of the algorithm.
ADVANCED FDTD METHODS: PARALLELIZATION, ACCELERATION, AND ENGINEERING APPLICATIONS by Wenhua Yu, et.
In this podcast we talk to Mike Beunder, CEO of Vector Fabrics on optimization and parallelization of applications for multi-core processors.