当前位置:网站首页>Differences between processes and threads
Differences between processes and threads
2022-07-28 06:14:00 【Ke Yulong】
process :
After the program saved on the hard disk runs , It will form an independent memory body in the memory space , This memory has its own address space , Has its own pile , The superior affiliated unit is the operating system . The operating system will be in process units , Allocate system resources (CPU Time slice 、 Memory and other resources ), A process is the smallest unit of resource allocation .
Threads :
Threads are also called lightweight processes , It's operating system scheduling , yes CPU Minimum unit of scheduling .
Thread is subordinate to process , It's the actual executor of the program . A process can have multiple threads , At least one thread , But a thread can only have one process .
coroutines :
coroutines , Also called tasklet , fibers ; It's a lighter existence than threads
Thread switching will be saved to CPU In my stack , It has its own register context and stack
The main function of a coroutine is to achieve concurrency under the condition of a single thread , But it's actually serial ( image yield equally )
A coroutine can have , The process is not managed by the operating system kernel , And it's completely controlled by the program .

边栏推荐
- Shutter webivew input evokes camera albums
- Quick look-up table to MD5
- What are the general wechat applet development languages?
- Cluster operation management system, to answer questions about the process
- Reinforcement learning -- SARS in value learning
- Various programming languages decimal | time | Base64 and other operations of the quick look-up table
- Centos7 installing MySQL
- Ssh/scp breakpoint resume Rsync
- NLP中基于Bert的数据预处理
- 《AdaFace: Quality Adaptive Margin for Face Recognition》用于人脸识别的图像质量自适应边缘损失
猜你喜欢

Reinforcement learning - incomplete observation problem, MCTs

Interpreting the knowledge in a neural network

Deep learning (self supervised: Moco V3): An Empirical Study of training self supervised vision transformers

2: Why read write separation

Deep learning (self supervision: Moco V2) -- improved bases with momentum contractual learning

Small program development solves the anxiety of retail industry

Improved knowledge distillation for training fast lr_fr for fast low resolution face recognition model training

深度学习(自监督:CPC v2)——Data-Efficient Image Recognition with Contrastive Predictive Coding

小程序开发如何提高效率?

深度学习(自监督:MoCo v2)——Improved Baselines with Momentum Contrastive Learning
随机推荐
Reinforcement learning - Basic Concepts
知识点21-泛型
Deep learning (self supervision: simpl) -- a simple framework for contractual learning of visual representations
Utils commonly used in NLP
神经网络学习
自动定时备份远程mysql脚本
pytorch深度学习单卡训练和多卡训练
Deep learning - patches are all you need
Deep learning pay attention to MLPs
How to use Bert
Digital collections "chaos", 100 billion market change is coming?
Deep learning (self supervision: simple Siam) -- Exploring simple Siamese representation learning
深度学习(增量学习)——ICCV2021:SS-IL: Separated Softmax for Incremental Learning
UNL class diagram
Paper reading notes of field low resolution face recognition based on selective knowledge extraction
First meet flask
小程序开发
uniapp webview监听页面加载后回调
Wechat applet development and production should pay attention to these key aspects
KubeSphere安装版本问题