B.S. JAGADEESH, COMPUTER DIVISION,

BARC, TROMBAY, MUMBAI – 400 085

First National Workshop of the National Knowledge Network

Indian Institute of Technology, Powai 31/October/2012

Bunches, each containing 100 billion protons, cross 40 million times a second in the centre of

each experiment 1 billion proton-proton interactions per second in ATLAS & CMS !

Large Numbers of collisions per event

~ 1000 tracks stream into the detector every 25 ns a large number of channels (~ 100 M ch) ~ 1 MB/25ns i.e. 40 TB/s !

LHC is a very large scientific instrument…

Lake Geneva

Large Hadron Collider27 km circumference

CMS

ATLAS

LHCb

ALICE

James Casey, CERN, IT Department

most rudimentary

Experiment CPU (MSi2k * year) Disk (TB) MSS (TB)

Alice 59.2 23903 17880

ATLAS 150 72,453 48398

CMS 108.2 34,403 46,800

LHCb 17.88 47,49 11632

2,00,000 Computers !!!!

Result

Same (as) Yet different (from)

Web (information) Allows collaboration too.

(many resources of which

information is one of them)

Cluster/distributed computing

(unifies resources)

Unifies resources belonging to

different administrative

domains

Virtualization (single resource) Allows virtualization of large

no. of resources like,

computation, Data, storage,

information etc

Ian.Bird@cern.ch 10

11

More than 140 computing centres

12 large centres for primary data management: CERN (Tier-0) and eleven Tier-1s

138 federations of smaller Tier-2 centres

India – BARC, TIFR, VECC

Relies on EGEE and OSG Grids

Tier2 Centre ~1 TIPS

Online System

Offline Processor Farm

~20 TIPS

CERN Computer Centre

FermiLab ~4 TIPS France Regional Centre

Italy Regional Centre

Germany Regional Centre

Institute Institute Institute Institute ~0.25TIPS

Physicist workstations

~622 Mbits/sec

~1 MBytes/sec

There is a “bunch crossing” every 25 nsecs.

There are 100 “triggers” per second

Each triggered event is ~1 MByte in size

Physicists work on analysis “channels”.

Each institute will have ~10 physicists working on one or more channels; data for these channels should be cached by the institute server

Physics data cache

~PBytes/sec

~622 Mbits/sec or Air Freight (deprecated)

Tier2 Centre ~1 TIPS

Tier2 Centre ~1 TIPS

Tier2 Centre ~1 TIPS

Caltech ~1 TIPS

~622 Mbits/sec

1 TIPS is approximately 25,000

SpecInt95 equivalents

Specific to WLCG– Gftp, LFC, GUID

MIDDLEWARE FUNCTIONALITIES ?

DAE-CERN Joint Co-ordination Meeting, Nov 2, 2011, CERN

Node Configuration Manager

NCM

CompA CompB CompC

ServiceA ServiceB

RPMs / PKGs

SW Package Manager

SPMA

Managed Nodes

SW server(s)

HT

TP

SW

Repository

RPMs

Install server

HT

TP

/ PX

E System

installer

Install

Manager

base OS

XML configuration profiles

HTTP

CDB

SQL backend

SQL

CLI

XML backend

SO

AP

GUI

SCRIPTS

ServiceC

The key monitoring areas in GridView

include

Service Availability Monitoring

File Transfer Monitoring

Job Monitoring

SAM

DB

GRIDVIEW

DB

Service Nodes

SAM tests SAM Test

Results

SAM

Framework

Publishing

Web Service

R-GMA

Archiver Module

Web Service

Archiver Module

SAM XSQL

Export Module

RBs SEs (gridftp)

WS Client

RB Job

Logs

Gridftp

Logs

Gridftp

Logs

Fabric Monitoring System

at Site (LEMON / Nagios)

WS Client

HTTP/XML

Availability Metrics

GOCDB GOCDB

Sync Module

Data Analysis &

Summarization Module

Visualization

Module

Graphs & Reports

Displays periodic Graphs and Reports for

Detailed SAM test results for tests run for services at

a particular site

Hourly, Daily, Weekly and Monthly basis

Full traceability from aggregate Availability to detailed

SAM test results

Provision for saving user preferences based on

certificates

Refer http://gridview.cern.ch/GRIDVIEW/

Gridview computes job statistics based on RB

job logs

Displays periodic Graphs and Reports for

Job Status (Total Number of Jobs in various States)

Job Success Rate

Job Resource Utilization (Elapsed time,CPU, Memory)

Average Job Turnaround time (RB Waiting, Site

Waiting, Execution Time)

Site, VO and RB-wise distribution

Hourly, Daily, Weekly and Monthly reports

Displays periodic Graphs and Reports for

Overall Summary

○ sites with high/low job execution rate

○ sites with high/low job success rate

○ VOs running more/less jobs etc

Possible to view job statistics for any user selected

combination of VO, Site and RB

Refer http://gridview.cern.ch/GRIDVIEW/

Please visit:

http://gridview.cern.ch/GRIDVIEW/

Most recent snap shot

Fully web based system providing Tracking : Tracking reported bugs, defects,

feature requests, etc.

Assignment : Automatic routing and notification to support staff to get issues resolved

Communication : Capturing discussion and sharing knowledge

Enforcement : Automatic reminders according to severity of the issues

Accountability : History and Logs

NKN is a state-of-the-art multi-gigabit pan-India network: www.nkn.in

►9th April 2009:

President of

India

Inaugurated the

NKN Project. ► 16 PoP

► 26 Backbone Links

► 57 Edge Links

► 100 Crores allocated

in 2008 budget

► Completed in ¾ year

Connect R & D,

educational,

health, agri,

labs institutes

etc..

NIC working as

the Project

Execution

Agency

Idea of setting

NKN was

finalized at the

Office of PSA &

NKC.

1500+ institutes

in final phase

GoI approved a

budget of INR 5990

crores for NKN in

March, 2010

►5th March,

2011: Launched

the Logo &

Website of NKN

► 27 PoP

► 76 Backbone Links

► 216 Edge Links

HAS ENABLED EVERYONE TO COME ON BOARD !!

0

National Grid

Computing

CDAC, Pune

WLCG Collaboration

Common Users

Group (CUG)

Anunet (DAE

units)

BARC – IGCAR

NKN Router

NKN-Internet

(Grenoble-France)

NKN-General

(National

Collaborations)

Intranet

segment of

BARC

Internet

segment of

BARC

Logical Communication

Domains Through NKN

Category Examples Characteristics

Distributed

Supercomputing

Ab-initio

Molecular Dyn

Large Cpu/

memory reqd

High Throughput Cryptography Harness Idle

cycles

On Demand Medical

instruments

Cost

effectiveness

Data Intensive CERN LHC Info from Large

Data sets

Collaborative Data

Exploration

Support

communication

KEEPING PROVENANCE INFORMATION TO MAKE

DATA DISCERNABLE TO NEXT GENERATION

MEETING PROVISIONING CHALLENGES

( CLOUDMAN PROJECT)

COMPLETE SWICTH OVER TO “CLOUDS” ?

(SECURITY OF DATA?)