【学习资料】2000+本书:芯片、驱动、系统、应用、程序、人生、理财等

博主分享了包含2000+本书的学习资料库,尤其重点推荐了三本关于CUDA编程的电子书,包括《CUDA并行程序设计》、《Learn CUDA Programming》和《CUDA 编程:基础与实践》。资源仅供临时学习使用,鼓励支持正版。

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重磅资料库!!!老读者知道小杨我是一个喜欢读书来学东西的人,因为书籍的知识是系统的。

在这十多年的学习过程中,购买了很多纸质书,也查找、购买、收藏了很多的电子书。于是在年前整理出来,大概有2000+本书,包含了芯片、驱动、系统、应用、程序人生各个方面。

电子书分享链接仅作临时学习使用,请购买正版书籍,支持原创作者。让良币驱逐劣币

获取链接在文末!如侵权请联系删除!

整理不易,使劲学习!

CUDA programming: a developer's guide to parallel computing with GPUs. by Shane Cook. Over the past five years there has been a revolution in computing brought about by a company that for successive years has emerged as one of the premier gaming hardware manufacturersdNVIDIA. With the introduction of the CUDA (Compute Unified Device Architecture) programming language, for the first time these hugely powerful graphics coprocessors could be used by everyday C programmers to offload computationally expensive work. From the embedded device industry, to home users, to supercomputers, everything has changed as a result of this. One of the major changes in the computer software industry has been the move from serial programming to parallel programming. Here, CUDA has produced great advances. The graphics processor unit (GPU) by its very nature is designed for high-speed graphics, which are inherently parallel. CUDA takes a simple model of data parallelism and incorporates it into a programming model without the need for graphics primitives. In fact, CUDA, unlike its predecessors, does not require any understanding or knowledge of graphics or graphics primitives. You do not have to be a games programmer either. The CUDA language makes the GPU look just like another programmable device. Throughout this book I will assume readers have no prior knowledge of CUDA, or of parallel programming. I assume they have only an existing knowledge of the C/C++ programming language. As we progress and you become more competent with CUDA, we’ll cover more advanced topics, taking you from a parallel unaware programmer to one who can exploit the full potential of CUDA. For programmers already familiar with parallel programming concepts and CUDA, we’ll be discussing in detail the architecture of the GPUs and how to get the most from each, including the latest Fermi and Kepler hardware. Literally anyone who can program in C or C++ can program with CUDA in a few hours given a little training. Getting from novice CUDA programmer, with a several times speedup to 10 times–plus speedup is what you should be capable of by the end of this book. The book is very much aimed at learning CUDA, but with a focus on performance, having first achieved correctness. Your level of skill and understanding of writing high-performance code, especially for GPUs, will hugely benefit from this text. This book is a practical guide to using CUDA in real applications, by real practitioners. At the same time, however, we cover the necessary theory and background so everyone, no matter what their background, can follow along and learn how to program in CUDA, making this book ideal for both professionals and those studying GPUs or parallel programming.
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