CUDA（Compute Unified Device Architecture），是显卡厂商NVIDIA推出的运算平台。 CUDA™是一种由NVIDIA推出的通用并行计算架构，该架构使GPU能够解决复杂的计算问题。 它包含了CUDA指令集架构（ISA）以及GPU内部的并行计算引擎。 开发人员现在可以使用C语言来为CUDA™架构编写程序，C语言是应用最广泛的一种高级编程语言。所编写出的程序可以在支持CUDA™的处理器上以超高性能运行。CUDA3.0已经开始支持C++和FORTRAN。

深度学习是解决难题（例如图像分类，分割和检测）的强大方法。 然而，使用深度学习进行推理是计算密集型的，消耗大量内存。 即使是规模很小的网络也需要大量的内存和硬件来执行这些算术运算。 这些限制可能会阻止将深度学习网络部署到计算能力低且内存资源较小的设备上。 在这种情况下，您可以将 Deep Learning Toolbox 与 Deep Learning Toolbox 模型量化库支持包结合使用，通过将卷积层的权重、偏差和激活量化为 8 位缩放整数来减少深度神经网络的内存占用数据类型。 然后您可以使用 GPU Coder 为量化网络生成优化的 CUDA 代码。 此示例展示了如何创建、训练和量化一个用于缺陷检测的简单卷积神经网络，然后演示如何为包括前/后图像处理和卷积神经网络在内的整个算法生成代码，以便您可以将其部署到 NVIDIA GPU 中，例如Jetson AGX Xavier、N

基于CUDA实现的卫星二体运动模型源码，采用Visual c++实现，与作者文章配套。

win7+keras2.0+cuda+gtx1070+gpu资源下载及安装过程记录 环境具体配置： 1、windows7专业版 sp1 2、显卡 GTXFORCE 1070 8G显存 3、Anaconda3 ,python 3.5 4、keras 2.0

此文件包含高清书（非扫描版）、源码以及课后答案，是学CUDA的利器

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.

cuDNN-Developer-Guide中文版，是cuDNN-Developer-Guide是中文版本。CUDNN是基于CUDA SDK开发的一套面向深度学习的基础库，其上是tensorRT。

基于NVIDIA TK1平台的安卓游戏机。可应用于虚拟现实，增强现实，智能服务型机器人！

用CUDA编写的中文分词项目，在没有GPU卡的平台下也可以直接运行

cmake opencvGPU版本时容易下载失败的文件，这里把3drpart整个文件夹放到编译输出目录文件夹下就行， opencv346，cuda9.1，cmake3.10.3