GPU Parallel Program Development Using CUDA. Tolga Soyata

GPU Parallel Program Development Using CUDA

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

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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.

Introduction to Parallel Programming using GPGPU and CUDA
Learn the fundamentals of GPU & CUDA programming, use your knowledge in Machine Learning, Data Mining and Deep Learning. The first course on the Udemy platform to introduce the NVIDIA's CUDA parallel architecture andprogramming model. CUDA is a . It takes time to develop content mate. 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 GPU Programming|NVIDIA UK
CUDA is NVIDIA's parallel computing platform that enables enthusiasts and scientists to dramatically improve computing performance by using the power of the GPU. Development and performance analysis of a parallel Monte Carlo
In this work a hybrid parallel Monte Carlo based neutron transport simulationprogram has been developed using Message-passing Interface (MPI) and Compute Unified Device Architecture (CUDA) technologies. Such program is aimed to run on a GPU-Cluster, that means, a computer cluster in which the nodes are Languages, APIs and Development Tools for GPU Computing - Nvidia
350+ Universities teaching GPU Computing on the CUDA Architecture. NVIDIAGPU with the CUDA Parallel Computing Architecture. CUDA. C/C++ CUDA Architecture. Application Acceleration Engines (AXEs). Middleware, Modules & Plug-ins. Foundation Libraries. Low-level Functional Libraries.