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Parallel Supercomputing in MIMD Architectures

Parallel Supercomputing in MIMD Architectures is devoted to supercomputing on a wide variety of Multiple-Instruction-Multiple-Data (MIMD;

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Data-Parallel Programming on MIMD Computers

Data-Parallel Programming demonstrates that architecture-independent parallel programming is possible by describing in detail how programs;

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Parallel Architectures for Artificial Neural Networks

architectures (ring, torus, etc.) built on different hardware platforms, ranging from large general purpose parallel computers to custom built MIMD;

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Parallel Programming

Parallel programming is designed for the use of parallel computer systems for solving time-consuming problems that cannot be solved on a;

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Practical Parallel Programming

computer theory and architecture, it describes four of the most popular parallel programming models in use today -- data parallelism, shared;

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Parallel MATLAB for Multicore and Multinode Computers

This is the first book on parallel MATLAB and the first parallel computing book focused on the design, code, debug, and test techniques;

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Parallel Programming

require access to a special multiprocessor system, concentrating instead only on parallel programs that can be executed on networked workstations;

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Parallel Programming

require access to a special multiprocessor system, concentrating instead only on parallel programs that can be executed on networked workstations;

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ScaLAPACK Users' Guide

for distributed memory message-passing MIMD computers and networks of workstations supporting parallel virtual machine (PVM) and/or message;

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Algorithms & Tools for Parallel Computing on Heterogeneous Clusters

performance computing on heterogeneous networks of computers.;

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Data Parallel C++

Book Is ForThose new data-parallel programming and computer programmers interested in data-parallel programming using C++.;

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PARLE '89 - Parallel Architectures and Languages Europe: Volume II

functional programming; MIMD, dataflow, inference and reduction machines; design and verification of parallel systems; VLSI, WSI and RISC;

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Parallel Programming

for inexpensive desktop computers. In only a few years, many standard software products will be based on concepts of parallel programming;

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Parallel Numerical Algorithms

methods which were originally developed for use with uniprocessors. Focusing on shared and local memory MIMD parallel computer systems, this;

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High Performance Computing

nodes or the multiple instruction, multiple data (MIMD) units on the accelerator Vectorization on the inner level After discussing;

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High Performance Computing

nodes or the multiple instruction, multiple data (MIMD) units on the accelerator Vectorization on the inner level After discussing;

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Parallel Programming With Microsoft Visual Studio 2010 Step

Your hands-on, step-by-step guide to the fundamentals of parallel programming Teach yourself how to help improve application performance by;

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The Massively Parallel Processor

of the Massively Parallel Processor (MPP), including discussions of hardware and software with special emphasis on applications, algorithms, and;

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Assembler Language Programming for IBM and IBM Compatible Computers [Formerly 370/360 Assembler Language Programming]

Presents a comprehensive business-oriented approach to teaching assembly language programming on IBM and IBM-compatible computers, geared;

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Programming Massively Parallel Processors

Programming Massively Parallel Processors: A Hands-on Approach shows both student and professional alike the basic concepts of parallel;

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Data Parallel C

code examples to illustrate key topics. Data parallelism in C++ enables access to parallel resources in a modern heterogeneous system;

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Advanced Topics in Dataflow Computing and Multithreading

pipelined design, and superpipelined data-driven VLSI processors. The next section, on language and programming issues, discusses stream data types;

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Structured Parallel Programming

Structured Parallel Programming offers the simplest way for developers to learn patterns for high-performance parallel programming;

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Parallel Computing for Data Science

exclusively on parallel data structures, algorithms, software tools, and applications in data science. It includes examples not only from the classic;

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Parallel Computing for Data Science

exclusively on parallel data structures, algorithms, software tools, and applications in data science. It includes examples not only from the classic;

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Programming Models for Parallel Computing

simultaneously computing and moving data. This book offers an overview of some of the most prominent parallel programming models used in high-performance;

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Parallel Processing, 1980 to 2020

This historical survey of parallel processing from 1980 to 2020 is a follow-up to the authors' 1981 Tutorial on Parallel Processing, which;

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