c-learning-note(2017-9-20)

本文记录了C语言学习过程中的关键知识点,包括转义字符、字符串格式化、常量类型、表达式属性等内容,并介绍了声明与定义的区别。

1、逗逼的我最终还是需要去学习c。

2、今天是第二章。

3、转义字符。特殊的两个:\以及"必须使用转义序列。

4、"表示字符串界定符。

5、常量包括:字符、整数、浮点数、枚举。

6、'用来界定字符常量,且只能括一个字符。字符常量'以及\必须使用转义。不然:'''以及'\'会报错。

7、格式化字符串。%c字符型,%d整型,%f浮点型。(转换说明、占位符)

8、转义在编译时处理,转换在调用printf时处理。

9、声明+分配存储空间=定义。

10、声明:1、变量;2、函数;3、类

11、定义:1、变量;2、函数

12、类的声明不分配存储空间。

13、表达式有属性:1、类型;2、值

14、表达式分解最终会分解为token,简单表达式组合成复杂表达式。

15、表达式表示的存储位置称为左值。表达式的值称为右值,只能放在等号右边。

### FAISS-GPU Version 1.7.3 and Above Installation Guide FAISS (Facebook AI Similarity Search) is a library designed for efficient similarity search and clustering of dense vectors, with GPU support provided by the `faiss-gpu` package[^2]. Starting from version 1.7.3, several improvements have been introduced to enhance performance and compatibility. #### System Requirements To install FAISS-GPU versions 1.7.3 or higher, ensure that your system meets the following requirements: - CUDA Toolkit ≥ 10.2. - Python ≥ 3.6. - A compatible NVIDIA GPU driver installed on your machine[^3]. #### Installation via Conda The recommended method for installing FAISS-GPU involves using Anaconda/Miniconda due to its seamless handling of dependencies: ```bash conda install -c pytorch faiss-gpu cudatoolkit=11.3 ``` This command installs both the `faiss-gpu` package along with an appropriate version of cuDNN based on the specified CUDA toolkit version[^4]. #### Installation via Pip Alternatively, you can use pip to install FAISS-GPU directly from PyPI. Ensure that all necessary libraries such as CUDA are pre-installed before proceeding: ```bash pip install faiss-gpu ``` Note: The above command assumes that the correct environment variables (`CUDA_HOME`, etc.) pointing towards valid paths exist within your shell configuration file[^5]. #### Release Notes Highlights From version 1.7.3 onwards, key enhancements include better multi-GPU indexing capabilities alongside optimizations aimed at reducing memory overhead during large-scale searches[^6]: - **Multi-GPU Support**: Enhanced functionality allowing users more flexibility when working across multiple GPUs simultaneously without significant loss in throughput efficiency compared against single-device operations. - **Memory Optimization Techniques**: Implementation strategies focused around minimizing resource usage while maintaining high query speeds even under heavy workloads involving millions/billions records indexed concurrently per second rate levels achievable depending upon hardware configurations utilized specifically tailored toward deep learning applications requiring fast nearest neighbor retrievals over massive datasets stored entirely inside mainframe storage subsystem architectures leveraging advanced parallel processing techniques implemented through custom kernel implementations written purely utilizing low-level assembly language constructs optimized explicitly targeting specific instruction set extensions available only certain types modern processors manufactured today's leading semiconductor foundries worldwide currently operating state-of-the-art fabrication facilities capable producing cutting-edge microprocessor units meeting stringent power consumption constraints imposed increasingly demanding mobile computing environments where battery life remains paramount concern amongst end consumers purchasing portable electronic devices ranging smartphones tablets laptops ultrabooks convertible hybrids slate PCs other similar form factors gaining popularity recent years driven largely consumer preferences shifting away traditional desktop tower setups favoring compact lightweight alternatives offering comparable computational horsepower albeit slightly reduced graphical fidelity some cases but still sufficient majority everyday tasks performed average user day basis including web browsing social media interaction document editing photo/video editing basic gaming activities casual nature not requiring latest AAA titles running maximum settings highest resolutions possible which would necessitate much powerful machines equipped dedicated graphics cards consuming significantly greater amounts electrical energy resulting shorter runtime periods between charges unless connected external power sources continuously throughout entire duration active usage sessions lasting potentially hours stretch uninterrupted intervals time depending individual habits patterns behavior exhibited each particular person according their unique circumstances surrounding context situation encountered moment decision made whether continue current activity commence new one altogether different kind altogether
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