Приклади вживання Parallel computing Англійська мовою та їх переклад на Українською
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Technologies of distributed systems and parallel computing.
The Landscape of Parallel Computing Research: A View from Berkeley.
Дияк Іван Modeling physical and technical processes based on parallel computing.
We promised parallel computing benchmarks lately. Here we go….
The analysis of influence of element base on choice of the architecture of the parallel computing system.
Programming standards for parallel computing include OpenCL(vendor-independent), OpenACC, and OpenHMPP.
Research interests: Mathematical modeling of dynamic systems using parallel computing;
Parallel Computing, also called Multitasking is when a software executes more then 1 task at a time.
Cilk, Cilk++ and Cilk Plusare general-purpose programming languages designed for multithreaded parallel computing.
Parallel computing has become the main model in computer architecture, mainly in the form of Multi-core processors.
The ARCnet and VAXcluster products not only supported parallel computing, but also shared file systems and peripheral devices.
Parallel computing becomes the dominant paradigm in computer architecture, mainly in the form of multi-core processors.
Global Arrays, or GA, is the library developed byscientists at Pacific Northwest National Laboratory for parallel computing.
Image processors often employ parallel computing even with SIMD or MIMD technologies to increase speed and efficiency.
Creating an effective means of automating the design ofparallel programs based on high-level descriptions of the parallel computing processes;
Floating point parallel computing is not necessarily deterministic, which is to say it does not automatically yield identical results every single time.
This method sees use, for example,in load balancing for parallel computing in order to minimize communication between processor nodes.
Parallel computing system for the rapid and high-precision analysis and forecasting of regional natural and anthropogenic processes in the atmosphere;
In article known methods of an estimation of heterogeneity of parallel computing systems, in particular systems of distributed data processing(DDPS) are considered.
Parallel computing is used in a wide range of fields, from bioinformatics(protein folding and sequence analysis) to economics(mathematical finance).
High Performance Fortran(HPF)is an extension of Fortran 90 with constructs that support parallel computing, published by the High Performance Fortran Forum(HPFF).
In these studies, he laid the foundation for the mathematical modeling and created the most important principles for the design andvalidation of difference schemes and parallel computing.
Parallel computing can also be applied to the design of fault-tolerant computer systems, particularly via lockstep systems performing the same operation in parallel. .
Data parallelism(also known as loop-level parallelism)is a form of parallelization of computing across multiple processors in parallel computing environments.
As part of the research work, we did a number of experiments on parallel computing and parallel simulation of complex dynamic systems in the computer laboratories of the University.
Task parallelism(also known as function parallelism and control parallelism)is a form of parallelization of computer code across multiple processors in parallel computing environments.
This figure is exceeded in many industrial crash models demanding optimized crash solvers with High-Performance Computing(HPC) features,such as vectorization and parallel computing.
Although commercial applications may define the architecture of most future parallel computers,traditional scientific applications will remain important users of parallel computing technology.
We can say with no doubt that commercial applications will define future parallel computers architecture butscientific applications will remain important users of parallel computing technology.
AMC Bridge takes an active role in platform development for scientific, engineering andfinancial computing systems that process mass data using parallel computing architectures.