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multiple industrial sectors, Professional CUDA C Programming presents CUDA -- a parallel computing platform and programming model designed to ease;
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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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needs of working software developers who need to understand GPU programming with CUDA and increase efficiency in their projects. CUDA;
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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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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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. 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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-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 gives you direct, hands-on engagement with personal, high-performance parallel computing, enabling you to do;
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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 shows how high-performance application developers can leverage the power of GPUs using;
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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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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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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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GPU Parallel Program Development using CUDA teaches GPU programming by showing the differences among different families of GPUs. This;
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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 teaches GPU programming by showing the differences among different families of GPUs. This;
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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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> 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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, allowing knowledge to cross-pollinate across the GPU spectrum. Many examples leverage NVIDIA's CUDA parallel computing architecture, the most;
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, proteomics, and evolutionary tree reconstruction Addresses GPGPU technology and the associated massively threaded CUDA programming model;
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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: 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: With Examples in R, C++ and CUDA is one of the first parallel computing books to concentrate;
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), 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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of CUDA programmers, this second edition contains 100% new material of interest across industry, including finance, medicine, imaging, engineering;
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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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