[PDF] GPU Parallel Program Development Using CUDA by Tolga Soyata

GPU Parallel Program Development Using CUDA. Tolga Soyata

GPU-Parallel-Program.pdf
ISBN: 9781498750752 | 476 pages | 12 Mb

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

Download GPU Parallel Program Development Using CUDA

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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.

CUDA Installation Guide for Microsoft Windows - NVIDIA Developer
With CUDA C/C++, programmers can focus on the task of parallelization of the algorithms rather than spending time on their implementation. Support heterogeneous computation where applications use both the CPU and GPU. Serial portions of applications are run on the CPU, and parallel portions are  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 Programming|NVIDIA - NVIDIA India
CUDA is NVIDIA's parallel computing platform that enables enthusiasts and scientists to dramatically improve computing performance by using the power of the GPU. Parallel Computing with CUDA | Pluralsight
An entry-level course on CUDA - a GPU programming technology from NVIDIA. 16m 52s. Tools Overview 5m 4s Using NSight 2m 59s Running CUDA Apps 3m 29s Debugging 2m 49s Profiling 2m 29s. Introduction to CUDA C. 30m 14s Dmitri is a developer, speaker, podcaster, technical evangelist and wannabe quant. 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. GPU vs CPU? What is GPU Computing?|NVIDIA
Dropping in GPU-optimized libraries; Adding compiler "hints" to auto-parallelize your code; Using extensions to standard languages like C and Fortran. Learning how to use GPUs with the CUDA parallel programming model is easy. For free online classes and developer resources visit CUDA zone. VISIT CUDA ZONE  A Review of CUDA, MapReduce, and Pthreads Parallel - arXiv
programming models to implement computational units, such as, multi-threads, on both CPUs and. GPUs efficiently. Yet still, with the advent of GPUs, additional . development was to create a programming model that was easy to use. The innate ability of MapReduce to do its parallel and distributed computation across.

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