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.
parallel computing experiences with cuda - Semantic Scholar
range of GPU devices. Because it provides a fairly simple, minimalist abstraction of parallelism and inherits all the well-known semantics of C, it lets programmersdevelop massively parallel programs with relative ease. In the year since its release, many developers have used CUDA to parallelize and accelerate GPU Parallel Program Development Using CUDA - Bokklubben
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Accelerate Your Applications Learn using step-by-step instructions, video tutorials and code samples. Features - Parallel Computing Toolbox - MATLAB - MathWorks
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WHAT IS CUDA? CUDA is NVIDIA's parallel computing architecture that enables dramatic increases in computing performance by harnessing the power of theGPU (graphics processing unit). With millions of CUDA-enabled GPUs sold to date, software developers, scientists and researchers are finding broad-ranginguses CS6963: Parallel Programming for GPUs (X units)
This course examines an important trend in high-performance computing, the use of special-purpose hardware originally designed for graphics and games to solve Students in the course will learn how to develop scalable parallel programs targeting the unique requirements for obtaining high performance on GPUs. Language Solutions | NVIDIA Developer
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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 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 GPU Parallel Program Development Using CUDA : Tolga Soyata
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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 GPU Accelerated Computing with C and C++ | NVIDIA Developer
Using the CUDA Toolkit you can accelerate your C or C++ applications by updating the computationally intensive portions of your code to run on GPUs. . established parallelization and optimization techniques and explainsprogramming approaches that can greatly simplify programming GPU- accelerated applications. 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