parallel computing

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Related to Parallel computers: Parallel programming

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 ?
When the GPU is used as a parallel computer, it necessary taken into account the processing units number, its memory structure and own programming model.
The following experiments are stored in a distribution of the structure of the large-scale parallel computer. Now in two ways to achieve data distribution Gaussian elimination and to facilitate the analysis, there is not pivoting operation; while using constant efficiency scalable parallel system to analyze scalability.
The recursive doubling algorithm initially was developed to solve tridiagonal linear system of size n on a parallel computer with p processors using O(log p) parallel arithmetic steps [33].
It is defined as the ratio of the time taken to solve a problem on a single processing element to the time required to solve the same problem on a parallel computer with p identical processing elements; given by Equation (2):
This drive to use cutting-edge technology led to the development and operation of the first massively parallel computer in NOAA--an Intel Paragon installed in FSL in 1992.
The reasons for this reduction of the runtime instead of making it half then that of the sequential program are: the coarseness or finesse of the parallel data, communication time between the parallel computers and computational efforts.
Most of the serial and parallel computers are classified according to this taxonomy, thus, it is discussed in detail.
Programs developed using MPI can be reused in newer, faster parallel computers. The need for parallel processing is increasing with the necessity for high-speed processing.
Even on today's fastest parallel computers, it could take centuries to analyze genome rearrangements for large, complex organisms.
The information in this paper focuses particularly on: description the field of high performance scientific computing, parallel computing, scientific computing, parallel computers, and trends in the hpc field, presented here reveal important new directions toward the realization of a high performance computational society.
PETSc supports all commonly used computational environments, ranging from Microsoft Windows systems to the largest parallel computers from IBM and Cray.

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