## +21 ## Custom Views for the Rest of Us ## JACOB RICE 20 21 October 24-29 ## Custom Views for the Rest of Us Jacob Rice jacob.rice.cpp@gmail.com KEYSIGHT TECHNOLOGIES 1 using namespace std::views; t2) 3 { // Algorithm stuff... std::ranges::swap(t1, t2); // More algorithm ## Calls the custom version, if it exists (same for begin and end): 1 template 2 void AlgorithmThatSwaps(T& type: std::ranges::range_adaptor_closure. • Ensure all closure objects inherit from this. • Custom closure objects can then use it, too. namespace detail { template concept

## GPU Resource Management On JDOS 梁永清 liangyongqing1@jd.com ## 提供的服务 ## Experiment ## Training 1. 用于实验的 GPU 容器 2. 基于 Kubeflow 的机器学习训练服务 3. 模型管理和模型 Serving 服务 ## Serving 均基于容器，不对业务方直接提供 GPU 物理机

# 动态资源管理和容器技术 在金融行业的架构探索和明天 ## Resource Scheduling & Container Technology for Financial Service 余军 Gopher China 2015 ## 关于我 ☐ ~19y+ Linux & opensource experience ☐ Co-founder of Shanghai p14_2.jpg) ## Algorithm 1 DRF pseudo-code $R = \langle r_{1}, \cdots, r_{m} \rangle$ ▷ total resource capacities $C = \langle c_{1}, \cdots, c_{m} \rangle$ ▷ consumed resources, initially 0 $s_{i}$ \max_{j=1}^{m} \{ u_{i,j} / r_{j} \}$ else return ▷ the cluster is full end if ① Mesos 采用了DRF(Dominant Resource Fairness) 调度机制。YARN自带FIFO、Capacity Scheduler和Fair Scheduler(借鉴了Mesos的DRF)。 ② Mesos中的DRF调度算法过分的

## +21 ## Compile-Time Compression and Resource Generation with C++20 ## ASHLEY ROLL 20 21 October 24-29 ## I ntroduction Explore how C++20's constexper features can: • Generate data from code that take a user-supplied lambda to generate the data needed to render our desired compile-time resource! - These are effectively templated functions, but we will use the cleaner auto parameter syntax

[Image](/uploads/documents/a/f/0/b/af0ba3339ba8eb2fcdf2647d94c085d4/p9_9.jpg) ## Operator Pattern - Custom Resource & Controller apiVersion: pingcap.com/v1alpha1 kind: TidbCluster spec: version: v4 Kubernetes 的几种方式 ## Common ways to extend Kubernetes - Custom Resource - TidbCluster - Tidb Initializer - TidbMonitor - Backup/Restore - Custom Controller - Scheduler Extender (optional) - Admission Admission Webhook (optional) - ...Scheduler framework, Aggregated APIServer ## Custom Resource apiVersion: pingcap.com/v1alpha1 kind: TidbCluster spec: version: v4.0.3 pd: baseImage: pingcap/pd

flask_restful import Resource, Api app = Flask(__name__) api = Api(app) class HelloWorld(Resource): def get(self): return {'hello': 'world'} api.add_resource(HelloWorld, '//') defining methods on your resource. A basic CRUD resource for a todo application (of course) looks like this: from flask import Flask, request from flask_restful import Resource, Api app = Flask(__name__) Flask(__name__) api = Api(app) todos = {} class TodoSimple(Resource): def get(self, todo_id): return {todo_id: todos[todo_id]} def put(self, todo_id): todos[todo_id] = request.form['data']

user leveraging the status block of the Custom Resource Configuration of the workload • Operator provides configuration via the spec section of the Custom Resource - Operator reconciles configuration and performance metrics about the Operand Alerting and Events • Operand sends useful alerts - Custom Resources emit custom events ## Auto-scaling - Operator scales the Operand up under increased load based on /e/9/2/8/e9285922cb84ea7e2cf1f473a4f03572/p16_1.jpg) custom controller operator pattern built-in resources custom resources custom resource ## Kubebuilder 框架模型 ![Image](/uploads/documents/e/9/2

flask_restful import Resource, Api app = Flask(__name__) api = Api(app) class HelloWorld(Resource): def get(self): return {'hello': 'world'} api.add_resource(HelloWorld, '//') defining methods on your resource. A basic CRUD resource for a todo application (of course) looks like this: from flask import Flask, request from flask_restful import Resource, Api app = Flask(__name__) Flask(__name__) api = Api(app) todos = {} class TodoSimple(Resource): def get(self, todo_id): return {todo_id: todos[todo_id]} def put(self, todo_id): todos[todo_id] = request.form['data']

## Greenplum资源管理器 姚珂男/Pivotal kyao@pivotal.io ## Agenda • Greenplum数据库 • Resource Queue • Resource Group ## Greenplum数据库 • 基于PostgreSQL • 分布式 • OLAP • MPP(Massively Parallel Processing) ## Greenplum数据库 6d5e7818fe3eb6bc09e8312568f4d7a/p4_1.jpg) ## Resource Queue • SQL语句并发控制 • 基于cost的并发控制 • 基于priority的CPU控制 • 内存控制 ## Running Example • CREATE RESOURCE QUEUE rq WITH ( active_statements = 6, min_cost = 50000, priority = high, memory_limit = '1024MB' ); • CREATE ROLE r1 RESOURCE QUEUE rq; • SELECT * FROM gp_toolkit.gp_resqueue_status; ## 内存控制 • virtual memory note keeping

range accepts EHCache 2.x as well * added "contentLength()" method to Resource abstraction * URL-based Resource variants introspect "last-modified" and "content-length" response collections and nested collections (fixed regression) * ConversionService properly handles nested Resource arrays in Map values (fixed regression) * ConversionService does not accidentally use copy constructor any value type * SpringTemplateLoader for FreeMarker supports last-modified timestamp through Resource abstraction * HibernateJpaDialect correctly closes borrowed connections even for nested JDBC executions