[C++ How to program C++大学教程 (第九版)]课后习题源代码，包含各章节习题源代码，帮助各位童鞋一起学习，一起进步！

基于单片机的无线红外防盗报警系统v1.1

Java How To Program Late Objects(10th) 英文无水印pdf 第10版 pdf所有页面使用FoxitReader和PDF-XChangeViewer测试都可以打开 本资源转载自网络，如有侵权，请联系上传者或csdn删除 本资源转载自网络，如有侵权，请联系上传者或csdn删除

stm32f10x 飞控 天地飞接收头WFLY program.zip

qt编写的扫雷程序，实现步骤在CSDN链接：https://blog.csdn.net/qq_34359028/article/details/85226747

微信小程序实例-微信小程序源码，无论学习还是二次开发必备，微信小程序（wei xin xiao cheng xu），简称小程序，英文名Mini Program，是一种不需要下载安装即可使用的应用，它实现了应用“触手可及”的梦想，用户扫一扫或搜一下即可打开应用。

Java大学教程 How to Program中文版(第四版)，java入门必选

基于stm32空气质量检测系统（毕设）

基于stm32水质监测系统（毕设）

I am from the days when computer engineers and scientists had to write assembly language on IBM mainframes to develop high-performance programs. Programs were written on punch cards and compilation was a one-day process; you dropped o your punch-code written program and picked up the results the next day. If there was an error, you did it again. In those days, a good programmer had to understand the underlying machine hardware to produce good code. I get a little nervous when I see computer science students being taught only at a high abstraction level and languages like Ruby. Although abstraction is a beautiful thing to develop things without getting bogged down with unnecessary details, it is a bad thing when you are trying to develop super high performance code. Since the introduction of the rst CPU, computer architects added incredible features into CPU hardware to \forgive" bad programming skills; while you had to order the sequence of machine code instructions by hand two decades ago, CPUs do that in hardware for you today (e.g., out of order processing). A similar trend is clearly visible in the GPU world. Most of the techniques that were taught asperformance improvement techniquesin GPU programming ve years ago (e.g., thread divergence, shared memory bank conicts, and reduced usage of atomics) are becoming less relevant with the improved GPU architectures because GPU architects are adding hardware features that are improving these previous ineciencies so much that it won’t even matter if a programmer is sloppy about it within another 5{10 years. However, this is just a guess. What GPU architects can do depends on their (i)transistor budget, as well as (ii) their customers’ demands. When I saytransistor budget, I am referring to how many transistors the GPU manufacturers can cram into an Integrated Circuit (IC), aka a \chip." When I saycustomer demands, I mean that even if they can implement a feature, the applications that their customers are using might not