Read online: GPU Parallel Program Development Using CUDA

GPU Parallel Program Development Using CUDA by Tolga Soyata

• GPU Parallel Program Development Using CUDA
• Tolga Soyata
• Page: 476
• Format: pdf, ePub, mobi, fb2
• ISBN: 9781498750752
• Publisher: Taylor & Francis

GPU Parallel Program Development Using CUDA

Free audio motivational books download GPU Parallel Program Development Using CUDA 9781498750752 MOBI FB2 CHM by Tolga Soyata (English literature)

GPU Parallel Program Development Using CUDA by Tolga Soyata GPU Parallel Program Development using CUDA teaches GPU programming by showing the differences among different families of GPUs. This approach prepares the reader for the next generation and future generations of GPUs. The book emphasizes concepts that will remain relevant for a long time, rather than concepts that are platform-specific. At the same time, the book also provides platform-dependent explanations that are as valuable as generalized GPU concepts. The book consists of three separate parts; it starts by explaining parallelism using CPU multi-threading in Part I. A few simple programs are used to demonstrate the concept of dividing a large task into multiple parallel sub-tasks and mapping them to CPU threads. Multiple ways of parallelizing the same task are analyzed and their pros/cons are studied in terms of both core and memory operation. Part II of the book introduces GPU massive parallelism. The same programs are parallelized on multiple Nvidia GPU platforms and the same performance analysis is repeated. Because the core and memory structures of CPUs and GPUs are different, the results differ in interesting ways. The end goal is to make programmers aware of all the good ideas, as well as the bad ideas, so readers can apply the good ideas and avoid the bad ideas in their own programs. Part III of the book provides pointer for readers who want to expand their horizons. It provides a brief introduction to popular CUDA libraries (such as cuBLAS, cuFFT, NPP, and Thrust),the OpenCL programming language, an overview of GPU programming using other programming languages and API libraries (such as Python, OpenCV, OpenGL, and Apple’s Swift and Metal,) and the deep learning library cuDNN.

Using CUDA device functions from OpenACC - Applied Parallel
The performance power of GPUs could be exposed to applications using two principal kinds of programming interfaces: with manual parallel programming (CUDA or OpenCL), or with directive-based extensions relying on compiler's capabilities of semi-automatic parallelization (OpenACC and OpenMP4). Unlike for GPUs  Tutorial on GPU computing - Lorena A. Barba Group
GPU computing - key ideas: •Massively parallel. •Hundreds of cores. •Thousands of threads. •Cheap. •Highly available. •Programable: CUDA. Felipe A. Cruz • Hardware side: developing flexible GPUs. •Software side: •OpenCL is a low level specification, more complex to program with than CUDA C. •CUDA C is more  GPU Accelerated Computing with Python | NVIDIA Developer
Python is one of the most popular programming languages today for science, engineering, data analytics and deep learning applications. However, as an interpreted language, it has been considered too slow for high-performance computing. That has changed with CUDA Python from Continuum Analytics. MATLAB for CUDA Development | NVIDIA Developer
Using MATLAB, you can analyze data, develop algorithms, and create models in a variety of application areas such as image and video processing, signal processing and communications, computational finance Parallel Computing Toolbox is required to call GPU-enabled functions or integrate CUDA kernels in MATLAB. CUDA - Wikipedia
CUDA is a parallel computing platform and application programming interface ( API) model created by Nvidia. It allows software developers and software engineers to use a CUDA-enabled graphics processing unit (GPU) for general purpose processing – an approach termed GPGPU (General-Purpose computing on  Parallel and GPU Computing Tutorials, Part 9: GPU Computing with
Learn about using GPU-enabled MATLAB functions, executing NVIDIA ® CUDA ™ code from MATLAB ® , and performance considerations. GPU Parallel Program Development Using CUDA by - Waterstones
GPU Parallel Program Development using CUDA teaches GPU programming by showing the differences among different families of GPUs. This approach prepares the reader for the next generation and future generations of GPUs. The book emphasizes concepts that will remain relevant for a long time, rather than  Heterogeneous Parallel Programming: Dive into the World of
A previous article in this series titled 'Introducing NVIDIAs CUDA' covered the basics of the NVIDIA CUDA device architecture. This article covers parallelprogramming using CUDA C with sequential and parallel implementations of a vector addition program. Parallel programming and general-purpose GPU  MATLAB GPU Computing Support for NVIDIA CUDA-Enabled GPUs
You can use GPUs with MATLAB through Parallel Computing Toolbox, which supports: CUDA-enabled NVIDIA GPUs with compute capability 2.0 or higher. For releases 14a and earlier, compute capability 1.3 is sufficient. In a future release, support for GPU devices of compute capability 2.x will be removed. At that time, a 

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