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Professional CUDA C Programming

the development of GPU programming -- fundamentals in an easy-to-follow format, and teaches readers how to think in parallel and implement;

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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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CUDA Fortran for Scientists and Engineers

experience, and cover the basics along with best practices for efficient GPU computing using CUDA Fortran. To help you add CUDA Fortran to;

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Programming Massively Parallel Processors

for constructing parallel programs are explored in detail. Case studies demonstrate the development process, which begins with computational;

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Programming Massively Parallel Processors

programming and GPU architecture. Various techniques for constructing parallel programs are explored in detail. Case studies demonstrate the;

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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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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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CUDA Programming

detailed guide to CUDA with a grounding in parallel fundamentals. It starts by introducing CUDA and bringing you up to speed on GPU parallelism and;

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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

stable support across all the libraries we use in this book. Key Features Expand your background in GPU programming-PyCUDA, scikit-cuda, and;

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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

access GPU device properties and capabilities from CUDA programs Learn how to accelerate searching and sorting algorithms Detect shapes such as;

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Hands-On GPU Computing with Python

Explore GPU-enabled programmable environment for machine learning, scientific applications, and gaming using PuCUDA, PyOpenGL, and Anaconda;

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GPU Programming in MATLAB

like to accelerate their codes using GPU programming without losing the many benefits of MATLAB. The book starts with coverage of the Parallel;

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Programming Massively Parallel Processors

concepts of parallel programming and GPU architecture, exploring, in detail, various techniques for constructing parallel programs. Case;

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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

such as CUDA libraries. The authors share their experience developing algorithms using MATLAB, C++ and GPUs for huge datasets, modifying MATLAB;

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GPU Computing Gems Emerald Edition

GPU Computing Gems Emerald Edition offers practical techniques in parallel computing using graphics processing units (GPUs) to enhance;

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Programming Massively Parallel Processors

development process, which begins with computational thinking and ends with effective and efficient parallel programs. The new edition includes;

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GPU Computing Gems Jade Edition

, gaming, environmental science, and green computing. It covers new tools and frameworks for productive GPU computing application development and;

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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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Efficient trademark retrieval using weighted image features

NVIDIA's Compute Unified Device Architecture (CUDA) enabled GPU. Our experimental results show that this new technique provides more robust;

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Real-time Knowledge-based Fuzzy Logic Model for Soft Tissue Deformation

Architecture (CUDA)-based GPU computing.;

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

computers with CUDA-capable video display cards. Source code for all routines presented in the book, and the executable CONVNET program which;

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Parallel Genetic Algorithms for Financial Pattern Discovery Using GPUs

of SAX/GA and genetic operators work to optimize possible solutions. This study is later used as the baseline for the development of parallel;

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Fast Medical Image Reconstruction using Graphics Processing Unit

to running CUDA code in MATLAB. This book exercises the acceleration of MRI reconstruction algorithm on NVIDIA's GeForce G 103M based GPU and;

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GPU-based Parallel Implementation of Swarm Intelligence Algorithms

GPU-based Parallel Implementation of Swarm Intelligence Algorithms combines and covers two emerging areas attracting increased attention;

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