## 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

Zstandard Compression Algorithm 215 Relaxed Rules for Specifying Table Distribution Columns 215 Resource Groups Features 216 PL/pgSQL Procedural Language Enhancements 216 Replicated Table Data 217 Concurrency Column Oriented Storage 319 Compression 320 Distributions 320 Resource Queue Memory Management 320 Partitioning 322 Indexes 322 Resource Queues 323 Monitoring and Maintenance 323 ANALYZE 323 Vacuum Settings 329 Number of Segments per Host 329 Resource Queue Segment Memory Configuration 329 Resource Queue Statement Memory Configuration 330 Resource Queue Spill File Configuration 331 **Schema Design**

## 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

Docker |可配额/可度量 Control Groups (cgroups)| |---| |cgroups 实现了对资源的配额和度量。cgroups 提供类似文件的接口，在 /cgroup 目录下新建一个文件夹即可新建一个 group，在此文件夹中新建 task 文件，并将 pid 写入该文件，即可实现对该进程的资源控制。groups 可以限制 blkio、cpu、cpuact、cpus namespace: enabled Network namespace: enabled Multiple /dev/pts instances: enabled --- Control groups --- Cgroup: enabled Cgroup clone_children flag: enabled Cgroup device: enabled Cgroup sched:

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

1.8.5 Resizing the Machine's Window 1.8.6 Saving the State of the Machine 1.9 Using VM Groups 1.10 Snapshots 1.10.1 Taking, Restoring, and Deleting Snapshots 1.10.2 Snapshot Contents running. • VM groups. Oracle VM VirtualBox provides a groups feature that enables the user to organize and control virtual machines collectively, as well as individually. In addition to basic groups, it is also in more than one group, and for groups to be nested in a hierarchy. This means you can have groups of groups. In general, the operations that can be performed on groups are the same as those that can be

Extending Flask-RESTful • Content Negotiation • Custom Fields & Inputs ○ Response Formats ☐ Resource Method Decorators Custom Error Handlers • Intermediate Usage ○ Project Structure Use With Blueprints 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__)

• Co-location @ Alibaba • Kubernetes vs YARN • Kubernetes & YARN: a hybrid architecture • Resource Isolation • Future ## What/Why co-location ## Data center Avg utilization ![Image](/uploads/d in co-location • Resource sharing: Make services and jobs share a single cluster and have them run on the same hardware • Resource Isolation: Rely on scheduling efficiency and resource isolation to guarantee guarantee the SLA. ## Why co-location • Two separated cluster cause huge resource wastage. Increase resource utilization. • 1 hour peak traffic on double 11 requires a lot of extra resources. However,