Framework Technologies and Progress

Huang Xingtao Zou jiaheng Li weidong Zhang xueyao

2013.07.05

Offline Tutorial II (X.T. Huang , SDU)

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What’s a Framework?

Framework Definition– A skeleton of an application into which developers plug in their

code and provides most of the common functionalities.– Provide and define standard interfaces between components.

Framework Benefits – Better specifications of what needs to be done– Better understanding of the system.– Low coupling between concurrent developments– Smooth integration and organization of the development. – Robustness, resilient to change (change-tolerant).– Fostering code re-use

Offline Tutorial II (X.T. Huang , SDU)

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Software Organization

Foundation Libraries

Framework

Trig

ger

Reco

nstr

uctio

n

Sim

ulati

on

Anal

ysis

Applications using framework components (Algorithms, Services, etc)

Provides basic services, common interfaces, data exchange and persistency mechanisms, interactivity

Basic libraries (such as Geant4, Root, Python, Openscientist, CLHEP, etc)

几个离线软件系统的构成

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Gaudi Object Diagram

Converter

Algorithm

Event DataService

PersistencyService

DataFiles

AlgorithmAlgorithm

Transient Event Store

Detec. DataService

PersistencyService

DataFiles

Transient Detector Store

MessageService

JobOptionsService

Particle Prop.Service

OtherServices

HistogramService

PersistencyService

DataFiles

TransientHistogram Store

ApplicationManager

ConverterConverter

Offline Tutorial II (X.T. Huang , SDU)

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Advantages of Gaudi

Clear separation between data and algorithms Clear separation between persistent data and transient

data Data Store-centered architectural style Encapsulated User code localized in few specific places

– Algorithms and Converters Run-time loading of components (dynamic libraries).

– addition of new components requires minimal recompilation All components with well defined interfaces and as

generic as possible

Beijing, CHINA for Review 7

Disadvantages of Gaudi

Multi-layers structure Too many third-party software or tools Relatively Slower Not suitable for non-accelerator experiments ,especially

events with time or space correlation

Light-weighted Framework (LAF) was designed and used for analysis.

13-1-6

New Experiments and New Framework

JUNO and LHAASO Experiment under R&D in China– JUNO performs very high precision measurement– LHAASO is very large scale experiment– Both are non-accelerator experiments– NuWa users’ experience shows that Gaudi is not suitable– Met the “similar” situation with BESIII in 2001 ,but huge

differenceFortran , C , C++belle (BASF), LHCb(Gaudi) , Babar or new one Finally Gaudi was chosen!!

– Lots of work has been done!

General Design Principle

JUNO

事例产生器

探测器模拟

数据刻度

事例重建

物理分析

AIDA CLHEP GCCXML ROOT Geant4 ……

SNiPER

LODESTAR

事例产生器

探测器模拟

数据刻度

事例重建

物理分析

…

SNiPER : Software for Non-collider Physics ExpeRimentsLODESTAR : LHAASO Offline Data Processing Software Framework

Beijing, CHINA for Review 10

Requirements for SNiPER

Learning from Gaudi– Data store centered architectural style

algorithms as data producers and consumers– Separation between data and algorithms

decease coupling and better for team development

– User code encapsulated in few specific places Algorithm, Service, DataObject

– Modularity Structure Run-time loading of components (dynamic libraries). Flexible execution control of algorithms

– Object I/O Capability to read/write C++ object. Independent of specific data models

– Programmable script parser for the control (Lin Tao , Xia xin)– Separation between “transient” and “persistent” representations of data???

based on the difference in different processing steps.

New requirements– Interface to distributed computing ( Zou Jiaheng )

Parallel processing Data file access over WAN (i.e. GRID)

13-1-6

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Kernel Structure of SNiPER

svcs

algsSniperMgr

OptionParser

AlgMgr

SvcMgr

IAlgorithm

IService

initialize()run()finilize()

setOption(name, value)

initialize()execute()finilize()

initialize()finilize()

initialize()execute()finilize()

initialize()finilize()

OptionParser/PropertyMgr: run time parameters configurationAlgorithms: data calculationsServices: other useful functionalities

PropertyMgr

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Algorithm and Service Management

Concrete Algorithm: 1. 模块化，可动态加载2. 仅通过接口与框架通信3. 每个特定类型算法可有一个或多个实例，通过

name 进行区分4. 可按配置顺序生成算法序列，支持嵌套、分支

等控制流程

IAlgorithm

ConcreteAlg

name()

IService

ConcreteSvc

name()

Concrete Service: • 具有与算法 1-3 相同特性• 可通过 name 在 SNiPER 内任意位置获取所需的

service 实例

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Algorithm and Data

DATAin

MEMORY

Algorithm 1

Algorithm 2

Algorithm 3

DISK

DISK

InputService

OutputService

数据与算法分离

• 便于不同算法间分享数据• 算法专注于数据的计算处理，与 I/O 解耦

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DataModel

DataObj

HeaderReadout

RawReadout McReadout RecReadout

setHeader()header

readout_map

header context

Header: 事例整体信息， id 、 detector 、 time 等Readout: 具体分支信息Header 中的 readout_map 对普通用户隐藏

DataBuffer DataBuffer

current()bigin()end()size()seek(int i)

head1 head2 head3 head4 head5 head6 head7 head8

rec1 rec2 rec5 rec6 rec8

MC2 MC5 MC7 MC8

只对头、尾进行增删操作，内部使用 deque

用户经由 header 间接访问，且有随机性（ lazy load ），使用 list

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考虑到 GenEvent 等类型与 Header 可能有一对多的对应关系，为降低内存数据维护难度， buffer 中可保存“智能指针”

• 对普通用户仅提供只读接口• 仅做为数据容器，与 I/O 解耦

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Event Buffer

0

1

2

3

4

5

6

7

EvtNum: 0 1 2 3 4 5 6 7Exe Num

Current event

Event buffer

Other events

专为对“时间关联事例”分析进行的设计

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运行时序示意图

每次事例循环开始时同步内存与磁盘数据（ I/O ）算法通过 BufferMgr 接口完成内存数据的存取

put

getexecute

read

writesyncData

SniperMgr BufferMgr DataWriter

DataReader

Algorithms

loop

Short Summary

Brief introduction to Framework Decide to design New Non-Gaudi based Framework One working version of SNiPER exists

– several main functionalities have been implmented SniperMgr Algorithm interface and management Service interface and management configuration interface one example is provided

– some need to be further optimized and discussed today In-memory data management Event data model persistency mechanism parallel processing python configuration Interface to Data base, Geometry and so on

Gaudi heavily relies on ROOT

FairRoot ： developed by GSI-IT

22.05.12 Florian Uhlig CHEP 2012, New York

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Start testing the VMC concept for CBM

First Release of CbmRoot

MPD (NICA) start also using FairRoot

ASYEOS joined(ASYEOSRoot)

GEM-TPC separated from PANDA branch (FOPIRoot)

Panda decided to join->FairRoot: same Base package for different experiments

R3B joined EIC (Electron Ion Collider BNL)EICRoot

2011201020062004

• Fully based on the ROOT

FairRoot for • Simulation ， reconstruction ， Data analysis

Planned Start in 2016 !

typedef std::map< std::string, TObject * > StoreObjMap typedef std::map< std::string, TClonesArray * > StoreArrayMap

Shuei YAMADA @ MEG review meeting, 2 July 2004

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ROMERoot based Object oriented Midas Extension

• Tool for Event based Data Analysis

• Fully Object Oriented

• Root based

• Full connection to the Midas Environment

• Online and Offline

• Based on Tasks, Containers and Folders for a good Data and Program Structure

• Experiment independent Base Classes

• Experiment dependent Classes are generated out of simple XML-Files

• The Users write only experiment specific code (physics)

• Administrative code is implemented in the generated code

• Self Documenting Code

• Self Linking Project

Shuei YAMADA @ MEG review meeting, 2 July 2004

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ROME Objects

Folders

•Objects, where data is stored in

•Stores the data of one detector (or sub detector) component

•Hierarchically arranged

•Data inside of Folders is structured

Tasks

•Tasks are objects, which provides actions

•They make calculations

•Store and read data in folders

•Fill trees and histograms

•Hierarchically arranged

•Task also own histograms

Trees

•Data Objects : only written, never read

•Used to write data on files

Histograms

•Graphical Data Objects : only written

•Belong to one Task

Steering Parameters

•Task steering

•Framework steering

Shuei YAMADA @ MEG review meeting, 2 July 2004

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Interconnections

Folders

TasksTasksTasksFill

Read

TreesTreesTreesRead

Flag

HistogramsHistogramsHistograms

HistogramsHistogramsHistograms

HistogramsHistogramsHistograms

Fill

Fill

Disk (Output)Write (ROOT)

Disk (Input)

Read (any Format)

Facts show us

ROOT becomes more and more popular and powerful From generator to analysis, most processes data can be

saved into ROOT It is better to have as same as possible event data

definition between transient and Persistency– easy implementation of I/O

Design of Event Data Model based on ROOT– Data Object inherits from Tobject

Navigation functions replyon Header Objects.

Pros : straightforwardcons: no general enough

Dyb uses RegistrationSequncewhich is separately from headers

Event Data Model based on ROOT

Tags for fast review

Purpose of tag is to support fast review events and decide whether or not to further read more data into Store.

Layout of Store

std::deque<Tobject* > frontStore std::map< std::string, std::list<Tobject* > > backStore

Front Store Back Store

Object Access and I/O

Data Store is used to manage the layout of TObjects– support access to Data Store with path or type

Data objects could be written to and read from Data Store with the streamers of these classes which will be automatically generated and included in the dictionary library.

Manipulation of this Data Store:– Filling– Reading– Trimming– WritingNeed further design and implement

Discussion

Further collect requirements of framework– in form of Use Case Study

Discuss and finalize the Data Model Design– data structure – data navigation – data storage – implementation

similar with GOD or coded

Optimize design of In-Memory Management– Buffer mechanism– Adopting ROOT– I/O

Other functionalities discussion– Job configuration ( python )– User interfaces (Algorithm, Services, Tools……)– structure of the whole offline softer system– ……

More inputsMore thinkingMore discussion

Thanks a lot!

Art

art is a generic C++-based modular analysis framework, for use from generator-level or DAQ event building through simulation, production and user analysis. – g-2 ， Mu2e ， NOνA ， LArSoft(μBooNE,ArgoNeuT, LBNE)

art grew out of and forked from CMS in 2010 Developer has been involved with frameworks:

– DØ, BTeV, CMS and MiniBooNE. art plans to support parallel processing of independent

events as well as to permit parallel processing within events

Art architecture