Examples of using Parallel algorithms in English and their translations into Portuguese
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However, very few parallel algorithms achieve optimal speed-up.
Understanding data dependencies is fundamental in implementing parallel algorithms.
Parallel algorithms are also known for finding strongly connected orientations of mixed graphs.
The cuda programming model is used in order to develop two different parallel algorithms.
Data analysis and visualization, numerical parallel algorithms, computational physics and chemistry, and engineering applications.
The students will acquire skills in programming parallel applications and designing effective parallel algorithms.
Parallel algorithms allow the load to be distributed among CPUs; an example is the spatial or force decomposition algorithm. .
Graduated from the Air Force Academy at 21 with an advanced degree in parallel algorithms and quantum electronics.
He did important early work on parallel algorithms for prefix sums and their application in the design of Boolean circuits for addition.
Works developed by von Neumann in the 1940s were already discussing the possibility of parallel algorithms for solving differential equations.
It is designed to cover computer architecture and networks, parallel algorithms, performance modeling,parallel applications, distributed memory, multithreading, and coprocessor and accelerator programming.
The introduction of the formal'P-RAM' model in Wyllie's 1979 thesis had the aim of quantifying analysis of parallel algorithms in a way analogous to the Turing Machine.
In this work we propose parallel algorithms for the following problems: the maximum subsequence sum, the maximum subarray sum, the maximum hyperrectangle sum and the selection of the k-th smallest element of an unsorted sequence.
The programming skills to build solutions using big data technologies such as MapReduce and scripting for NoSQL, andthe ability to write parallel algorithms for multi-processor execution.
His research interests include complexity theory, parallel algorithms, graph theory, cryptography, distributed computing, and neural networks.
This guide is designed to help developers programming for the CUDA architecture using C with CUDA extensions implement high performance parallel algorithms and understand best practices for GPU Computing.
Bearing this in mind, in this dissertation,we propose efficient parallel algorithms to solve the graph clustering problem, also known as the detection community problem in netwoks.
Integrated source code editors and debugging capabilities for OpenCL, GLSL, and Lua,as well as a toolbox of 2D user interface widgets Provide you with a framework to develop a wide range of parallel algorithms.
Courses are offered in the fields of internet system architectures and their related design problems,distributed and parallel algorithms, applications of artificial intelligence, platforms for collaborative work and ambient intelligence.
One way to reduce this long time of execution is to develop parallel algorithms, using the advantages of the recent computer architectures that allow high parallelism, i.e., graphics processor units(gpus) and multicore cpus.
The photorealistic rendering process for cinema and tv increasingly demands processing power,requiring fast parallel algorithms and effective task distribution systems.
Program description Courses are offered in the fields of internet system architectures and their related design problems,distributed and parallel algorithms, applications of artificial intelligence, platforms for collaborative work and ambient intelligence.
The guide will aid developers in the use of CUDA C. This guide is designed to help developers programming for the CUDA architecture using C with CUDA extensions implement high performance parallel algorithms and understand best practices for GPU Computing.
Lindcq the tool consists of: grammar in bnf form(backus-naur-form), the compiler verifies that the incidence of errors in the code to be executed,a graphical interface to facilitate the programming features that allow the construction of the circuit graphically and parallel algorithms jocl to perform operations that require greater computational cost in the gpu. at the end of an experiment is performed in order to assess th.
Deep blue intelligent parallel algorithm.
In this study, we used the hybrid parallel algorithm spike one preconditioner for a non-stationary iterative method combining the distributed and shared memory architectures, mpi and openmp.
The implementation of this proposed computational parallel algorithm was written with the language c and uses the mpi library.
This way, the communication phase of the parallel algorithm, where the local solutions are combined in order to generate the final set of solutions, is very efficient.
The goal of this work is to write a parallel algorithm in cuda and mpi, to attain the overall alignment of multiple biological sequences, specifically dna and proteins using heuristics that can provide a certain quality in a reasonable time.
Our implementation is based on a parallel algorithm for maximum subsequence that does not t into the simd model(single instruction multiple data), that is the model of almost all the algorithms implemented on gpus.