parallel computing


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Related to parallel computing: parallel processing

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 main part of parallel computing is to exploit or to create the concurrency in the program, share it to the other computers/processors using the parallel application programming interface (API), such as the MPI, so that other computers can act upon the data (Peterson, Arbenz 2004).
Parallel computing allows a computer code to use the resources of multiple computers simultaneously.
Parallel Computing Toolbox enables engineers and scientists in MATLAB to access all available computing resources available to them, from multicores and GPUs on local desktops to clusters and grids, with minimal programming effort.
Today tens of thousands of developers, scientists and researchers are writing CUDA-based parallel computing applications in areas such as seismic data analysis, financial modeling, medical imaging and weather forecasting, to name a few.
As hardware systems become more powerful, MATLAB users are increasingly presented with data-intensive problems that involve highly complex data sets," said Silvina Grad-Freilich, manager of parallel computing and application deployment marketing at The MathWorks.
NVIDIA's CUDA is a general purpose parallel computing architecture that takes advantage of the parallel compute engine in NVIDIA GPUs to solve many computational problems in a fraction of the time required on a general purpose CPU.
International Conference on Parallel Computing in Electrical Engineering (2006: Bialystok, Poland)
AMAX's parallel computing solutions utilize NVIDIA's latest Tesla10 series GPU architecture.
Parallel computing research areas include accelerating the mobile web browser and runtime environments; increasing mobile device performance while lowering power consumption in both active and idle modes; and achieving seamless ubiquitous wireless connectivity and cross-domain interoperability.
Parallel computing for bioinformatics and computational biology; models, enabling technologies, and case studies.
The MathWorks today announced that Argonne National Laboratory significantly reduced the simulation time of models built with the Powertrain System Analysis Toolkit (PSAT) by using Parallel Computing Toolbox and MATLAB Distributed Computing Server from The MathWorks.
NVIDIA's Revolutionary CUDA Parallel Computing Architecture to Support OpenCL

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