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996.icu

Repo for counting stars and contributing. Press F to pay respect to glorious developers.

angularjs-from-scripts

AngularJs examples from scratch (No NPM/Grunt)

bahir-flink

Mirror of Apache Bahir Flink

cpplearning

csshake

CSS classes to move your DOM!

csszengarden.com

The source of csszengarden.com

designpatterns

diary-list

eoeflappybird

一小时学会开发FlappyBird

flink-commodity-recommendation-system

:whale:基于 Flink 的商品实时推荐系统。使用了 redis 缓存热点数据。当用户产生评分行为时，数据由 kafka 发送到 flink，根据用户历史评分行为进行实时和离线推荐。实时推荐包括：基于行为和实时热门，离线推荐包括：历史热门、历史优质商品和 itemcf 。

flink-learning

flink learning blog. http://www.54tianzhisheng.cn/ 含 Flink 入门、概念、原理、实战、性能调优、源码解析等内容。涉及 Flink Connector、Metrics、Library、DataStream API、Table API & SQL 等内容的学习案例，还有 Flink 落地应用的大型项目案例（PVUV、日志存储、百亿数据实时去重、监控告警）分享。欢迎大家支持我的专栏《大数据实时计算引擎 Flink 实战与性能优化》

flink-recommandsystem-demo

:helicopter::rocket:基于Flink实现的商品实时推荐系统。flink统计商品热度，放入redis缓存，分析日志信息，将画像标签和实时记录放入Hbase。在用户发起推荐请求后，根据用户画像重排序热度榜，并结合协同过滤和标签两个推荐模块为新生成的榜单的每一个产品添加关联产品，最后返回新的用户列表。

git-pre

Git初探

hw2-table-sorter

homework2 table sorter

hw3-object-oriented

hw3-object-oriented

hw4-asynchronous-javascript

Asynchronous JavaScript

hw5-start-top

start-top

hw6-myhomework

MyHomework

image-resources

Images resources used by markdown

jianzhioffer

mostly-adequate-guide

Mostly adequate guide to FP (in javascript)

my-homework

SE-386 Lab 06

n-blog

使用 Express + MongoDB 搭建多人博客

nodeclub

:baby_chick:Nodeclub 是使用 Node.js 和 MongoDB 开发的社区系统

notes

onlinewrittenexamination

Online written examinations of different companies in the second half of 2018.

ring-menu

The live show of TOP at SE-386 class, the School of Software, SYSU

scms_static_resource

sharerender

ShareRender is a cloud gaming system that enables fine-grained resource sharing at the frame-level. Existing cloud gaming systems suffer from low GPU utilization in the virtualized environment. Moreover, GPU resources are scheduled in units of virtual machines (VMs) and this kind of coarse-grained scheduling at the VM-level fails to fully exploit GPU processing capacity. ShareRender offloads graphics workloads within VMs directly to GPUs, bypassing GPU virtualization. For each game running in a VM, ShareRender starts a graphics wrapper to intercept frame rendering requests and assign them to render agents responsible for frame rendering on GPUs. Thanks to the flexible workload assignment among multiple render agents, ShareRender enables fine-grained resource sharing at the frame-level to significantly improve GPU utilization. If you want to know more about ShareRender, please refer to our paper in Multimedia 2017. Wei Zhang, Xiaofei Liao, Peng Li, Hai Jin, Li Lin, "ShareRender: Bypassing GPU Virtualization to Enable Fine-grained Resource Sharing for Cloud Gaming". In Proceedings of ACM International Conference on Multimedia (MM'17), Mountain View, CA, 2017.

ssxg

a web app design for association to release notifications and for users to get infomation