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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

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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.

About CUDA | NVIDIA Developer
Drop in a GPU-accelerated library to replace or augment CPU-only libraries such as MKL BLAS, IPP, FFTW and other widely-used libraries; Automatically parallelize loops in Fortran or C code using OpenACC directives for accelerators;Develop custom parallel algorithms and libraries using a familiar programming  What's New in CUDA | NVIDIA Developer
With this release you can: Develop image augmentation algorithms for deep learning easily with new functions in NVIDIA Performance Primitives Run batched neural machine translations and sequence modeling Learn about the new CUDA parallel programming model for managing threads in scalable applications. 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  CUDA Zone | NVIDIA Developer
CUDA® is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs). With CUDA, developers are able to dramatically speed up computing applications by harnessing the power of GPUs. Applied Parallel Computing LLC | GPU/CUDA Training and
Over 60 trainings all over Europe for universities and industry On-site trainings on the whole range of GPU computing technologies Each lecture accompanied with a practical session on remote GPU cluster Best recipes of GPU code optimization , based on our 5-year development experience We have multiple training  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  Intro to Parallel Programming | Udacity
Learn the fundamentals of parallel computing with the GPU and the CUDAprogramming environment! In this class, you'll learn about parallel programming by coding a series of image processing algorithms, such as you might find in Photoshop or Instagram. You'll be able to program and run your assignments on high-end  NVIDIA CUDA Getting Started Guide for Microsoft Windows
CUDA® is a parallel computing platform and programming model invented by NVIDIA. development tools. 1.1. System Requirements. To use CUDA on your system, you will need the following installed: ‣ A CUDA-capable GPU. ‣ A supported . The CUDA Toolkit installation defaults to C:Program FilesNVIDIAGPU. 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  CUDA Toolkit Documentation - NVIDIA Developer Documentation
Maxwell Compatibility Guide: This application note is intended to help developers ensure that their NVIDIA CUDA applications will run properly onGPUs based on the NVIDIA Maxwell Architecture. This document provides guidance to ensure that your software applications are compatible with Maxwell. Technical preview: Native GPU programming with CUDAnative.jl
After 2 years of slow but steady development, we would like to announce the first preview release of native GPU programming capabilities for Julia. You can level of CUDA C. You should be interested if you know (or want to learn) how toprogram a parallel accelerator like a GPU, while dealing with tricky 

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