Examples of using Parallel computing in English and their translations into Indonesian
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Parallel computing on GPU.
Also called parallel computing.
The combination of technologies used includes Open GL, Core Image for Mac's graphics card,Open CL for parallel computing, and 64-bit architecture.
OpenCL provides parallel computing using task-based and data-based parallelism.
They're requesting parallel computing.
Nvidia's invention of the GPU in 1999 sparked the growth of the PCgaming market, redefined modern computer graphics, and revolutionized parallel computing.
All modern computing is parallel computing.
CUDA is NVIDIA's parallel computing architecture that enables dramatic increases in computing performance by harnessing the power of the GPU(graphics processing unit).
It takes better advantage of Parallel Computing.
Some of the new features in. NET 4 include MEF, parallel computing, a new API and a few improvisations to Web Forms, core services, MVC, deployment, WPF and WCF components.
This sub-network is likely to be available with high-speed computing nodes andbandwidth links during the time required for parallel computing tasks.
Cloud computing is the development of parallel computing, distributed computing and grid computing. .
Recently, ant colonies also are being studied and modeled in machine learning, complex interactive networks,stochasticity of encounter and interaction networks, parallel computing, and other computing fields.[1].
CUDA(compute unified device architecture) is a parallel computing platform and programming model that was created by NVIDIA and implemented by the GPU(graphics processing unit).
Recently, ant colonies are also studied and modeled for their relevance in machine learning, complex interactive networks,stochasticity of encounter and interaction networks, parallel computing, and other computing fields.
CUDA: CUDA(aka Compute Unified Device Architecture) is a parallel computing platform and programming model created by NVIDIA and implemented by the graphics processing units(GPUs) that they produce.
The considerable number of partnerships between public and private institutions, where the Dutch government collaborates with the private and educational sector, leads to active development in the diverse fields of embedded systems, modeling, multimedia technologies,virtual laboratories and parallel computing.
P2P systems can be used to provide anonymized routing of network traffic,massive parallel computing environments, distributed storage and other functions.
Grid computing-"A form of distributed and parallel computing, whereby a'super and virtual computer' is composed of a cluster of networked, loosely coupled computers acting in concert to perform very large tasks.".
As power consumption(and consequently heat generation)by computers has become a concern in recent years,[3] parallel computing has become the dominant paradigm in computer architecture, mainly in the form of multi-core processors.
Parallel computing is a form of computation in which many calculations are carried out simultaneously,[33] operating on the principle that large problems can often be divided into smaller ones, which are then solved"in parallel".
To enable this new computing paradigm,NVIDIA invented the CUDA parallel computing architecture that is now shipping in GeForce, ION, Quadro, and Tesla GPUs, representing a significant installed base for application developers.
This track covers all aspects of parallel computing systems, from hardware to software, and the entire range of scale from laptops to compute servers, GPU accelerators, heterogeneous systems and large-scale, high-performance compute infrastructures.
Furthermore,“distributed” or“grid” computing, in general, is a special type of parallel computing that relies on complete computers(with onboard CPUs, storage, power supplies, network interfaces, etc.) connected to a network(private, public or the Internet) by a conventional network interface, such as Ethernet.
However, quasi-opportunistic distributed execution of demanding parallel computing software in grids should be achieved through implementation of grid-wise allocation agreements, co-allocation subsystems, communication topology-aware allocation mechanisms, fault tolerant message passing libraries and data pre-conditioning.