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Professional CUDA C Programming

multiple industrial sectors, Professional CUDA C Programming presents CUDA -- a parallel computing platform and programming model designed to ease;

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CUDA Programming

If you need to learn CUDA but don't have experience with parallel computing, CUDA Programming: A Developer's Introduction offers a;

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CUDA Application Design and Development

needs of working software developers who need to understand GPU programming with CUDA and increase efficiency in their projects. CUDA;

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Hands-On GPU Programming with Python and CUDA

Build real-world applications with Python 2.7, CUDA 9, and CUDA 10. We suggest the use of Python 2.7 over Python 3.x, since Python 2.7 has;

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Programming Massively Parallel Processors

students to think about problems in ways that are amenable to high-performance parallel computing. It utilizes CUDA (Compute Unified Device;

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Image Processing Using CUDA

. Initially shader based frameworks like GLSL, Cg etc have been used for GP-GPU. NVIDIA has developed CUDA to write scalable parallel programs in C;

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Programming Massively Parallel Processors

-point format, parallel patterns, and dynamic parallelism are covered in depth. This best-selling guide to CUDA and GPU parallel programming has;

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CUDA for Engineers

CUDA for Engineers gives you direct, hands-on engagement with personal, high-performance parallel computing, enabling you to do;

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Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA

Discover how CUDA allows OpenCV to handle complex and rapidly growing image data processing in computer and machine vision by accessing the;

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CUDA Fortran for Scientists and Engineers

CUDA Fortran for Scientists and Engineers shows how high-performance application developers can leverage the power of GPUs using;

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Deep Belief Nets in C++ and CUDA C: Volume 3

applications. At each step Deep Belief Nets in C++ and CUDA C: Volume 3 presents intuitive motivation, a summary of the most important equations;

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Deep Belief Nets in C++ and CUDA C: Volume 1

modern CPUs as well as massive parallel processing on computers with CUDA-capable video display cards. The first of three in a series on C;

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Programming Massively Parallel Processors

updated their coverage of CUDA, including coverage of newer libraries, such as CuDNN, moved content that has become less important to appendices;

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Introduction to Cuda and Gpu Programming

GPU Parallel Program Development using CUDA teaches GPU programming by showing the differences among different families of GPUs. This;

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Deep Belief Nets in C++ and Cuda C: Volume 2: Autoencoding in the Complex Domain

current usage by extending it to the complex domain for signal and image processing applications. Deep Belief Nets in C++ and CUDA C: Volume 2;

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GPU Parallel Program Development Using CUDA

GPU Parallel Program Development using CUDA teaches GPU programming by showing the differences among different families of GPUs. This;

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Multicore and GPU Programming

for multi-core CPU programming and many-core "massively parallel" computing. Using threads, OpenMP, MPI, CUDA and other state-of-the-art tools;

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Accelerating MATLAB with GPU Computing

> Starting with the basics, setting up MATLAB for CUDA (in Windows, Linux and Mac OS X) and profiling, it then guides users through advanced topics;

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Clases de hoy en escuelas de ayer

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GPU Computing Gems Emerald Edition

, allowing knowledge to cross-pollinate across the GPU spectrum. Many examples leverage NVIDIA's CUDA parallel computing architecture, the most;

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Bioinformatics

, proteomics, and evolutionary tree reconstruction Addresses GPGPU technology and the associated massively threaded CUDA programming model;

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GPU Programming in MATLAB

different computer systems. Finally, advanced material includes CUDA code in MATLAB and optimizing existing GPU applications. Throughout the book;

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Parallel Computing for Data Science

Parallel Computing for Data Science: With Examples in R, C++ and CUDA is one of the first parallel computing books to concentrate;

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Parallel Computing for Data Science

Parallel Computing for Data Science: With Examples in R, C++ and CUDA is one of the first parallel computing books to concentrate;

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Data Management on New Hardware

), and hybrid programming models like CUDA, OpenCL, and Open ACC. The papers also explore the interplay between overall system design, core;

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GPU Computing Gems Jade Edition

of CUDA programmers, this second edition contains 100% new material of interest across industry, including finance, medicine, imaging, engineering;

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Programming Massively Parallel Processors

updated coverage of CUDA, including the newer libraries such as CuDNN. New chapters on frequently used parallel patterns have been added, and case;

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