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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 pdf online books download GPU Parallel Program Development Using CUDA (English Edition)
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.
Teaching Accelerated CUDA Programming with GPUs | NVIDIA
This page is a “Getting Started” guide for educators looking to teach introductory massively parallel programming on GPUs with the CUDA Platform.
CUDA Toolkit | NVIDIA Developer
With the CUDA Toolkit, you can develop, optimize and deploy your applications on GPU-accelerated embedded systems, desktop workstations, enterprise data centers, The toolkit includes GPU-accelerated libraries, debugging and optimization tools, a C/C++ compiler and a runtime library to deploy yourapplication.
CUDA Spotlight: Developing Robots with CUDA - Nvidia
Martin: For my research, I use CUDA-enabled software called Aquila to develop complex artificial neural networks - inspired by those found in the brain - for the optimizations, the nice thing about CUDA is that simply by naive parallelization of the CPU code one can achieve massive speedups using GPU devices.
GPU Parallel Program Development Using CUDA - Amazon UK
Buy GPU Parallel Program Development Using CUDA (Chapman & Hall/CRC Computational Science) 1 by Tolga Soyata (ISBN: 9781498750752) from Amazon's Book Store. Everyday low prices and free delivery on eligible orders.
MATLAB Acceleration on Tesla and Quadro GPUs|NVIDIA
Available through the latest release of MATLAB 2010b, NVIDIA GPU acceleration enables faster results for users of the Parallel Computing Toolbox and MATLAB In addition to using MATLAB to develop GPU accelerated applications and models, it can also be used by CUDA programmers to prototype algorithms and
Raamat: 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 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
CUDA Code Samples | NVIDIA Developer
There are many CUDA code samples included as part of the CUDA Toolkit to help you get started on the path of writing software with CUDA C/C++. The code samples covers a wide range of applications and techniques, including: Simple techniques demonstrating image. Basic approaches to GPU Computing; Best
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