national grid computing initiative - garuda february 2006 c-dac / mohan ram n 1 garuda national grid...
TRANSCRIPT
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GARUDA National Grid Computing Initiative
N. Mohan RamChief Investigator – GARUDA
9th February 2006Kolkatta
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Presentation Outline
• Overview
• Technologies and Research Initiatives
• Communication Fabric
• Resources
• Partners
• Applications
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Project Overview
• Precursor to the National Grid Computing Initiative
– Test Bed for the grid technology/concepts and applications
– Provide inputs for the main grid proposal
• Major Deliverables– Technologies, Architectures, Standards & Research
Initiatives– Nation-wide high-speed communication fabric– Aggregation of Grid Resources– Deployment of Select applications of National Importance– Grid Strategic User Group
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Technologies, Architectures, Standards
and Research Initiatives
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Deliverables
• Technologies– Garuda Component Architecture & Deployment– Access Portal– Problem Solving Environments – Collaborative Environments– Program Development Environments– Management and Monitoring– Middleware and Security– Resource Management and Scheduling– Data Management– Clustering Technologies
• Research Initiatives– Integrated Development Environments– Resource Brokers & Meta Schedulers– Mobile Agent Framework– Semantic Grid Services (MIT Chennai)– Network Simulation
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Garuda Component Architecture
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C-DAC Development & Deployment CollaborationsResearch Initiatives OpenCommercial
Access GRIDAccess GRID Video Conferencing over IPVideo Conferencing over IP
Grid ApplicationsGrid Applications
MDSMDS
MPICH-G2MPICH-G2
Grid SchedulersGrid Schedulers
SUN Grid EngineSUN Grid EngineLoadlevelerLoadleveler
Storage Resource BrokerStorage Resource Broker Visualization SoftwareVisualization Software
PDE
Collaborative Environment
Benchmarks& Applications
Middleware & Security
Storage & Visualization
Monitoring &
Managem
ent
C-D
AC
GridM
onC
-DA
C G
ridMon
NM
SN
MS
Ganglia
Ganglia
DIViA for GridDIViA for Grid IDEIDE CactusCactusWorkflowWorkflow ProfilersProfilers
Grid ProbesGrid Probes
Integration & EngineeringIntegration & Engineering
GLOBUS 2.x/4.x
Certificate AuthorityCertificate Authority
Resource BrokerResource Broker
Grid SecurityGrid Security
Semantic Grid ServicesSemantic Grid Services
C-DAC Grid PortalC-DAC Grid Portal Problem Solving EnvironmentsProblem Solving EnvironmentsGrid AccessMethods
GARUDA Components
C-DAC Development & Deployment CollaborationsResearch Initiatives Open SourceCommercial
Access GRIDAccess GRID Video Conferencing over IPVideo Conferencing over IP
Grid ApplicationsGrid Applications
MDSMDS
MPICH-G2MPICH-G2
Grid SchedulersGrid Schedulers
SUN Grid EngineSUN Grid EngineLoadlevelerLoadleveler
Storage Resource BrokerStorage Resource Broker Visualization SoftwareVisualization Software
PDE
Collaborative Environment
Benchmarks& Applications
Middleware & Security
Storage & Visualization
Monitoring &
Managem
ent
C-D
AC
GridM
onC
-DA
C G
ridMon
NM
SN
MS
Ganglia
Ganglia
Monitoring &
Managem
ent
C-D
AC
GridM
onC
-DA
C G
ridMon
NM
SN
MS
Ganglia
Ganglia
DIViA for GridDIViA for Grid IDEIDE CactusCactusWorkflowWorkflow ProfilersProfilers
Grid ProbesGrid Probes
Integration & EngineeringIntegration & Engineering
GLOBUS 2.x/4.x
Certificate AuthorityCertificate Authority
Resource BrokerResource Broker
Grid SecurityGrid Security
Semantic Grid ServicesSemantic Grid Services
C-DAC Grid PortalC-DAC Grid Portal Problem Solving EnvironmentsProblem Solving EnvironmentsGrid AccessMethods
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Garuda Resource Deployment(at C-DAC centres)
AIXCluster
Shared User SpaceShared Data Space
Linux Cluster
Bangalore
Solaris Cluster
LinuxCluster
Shared User SpaceShared Data Space
Pune
Chennai HyderabadResource
Manager for Grids
Linux Cluster
ResourceManager for Grids
Garuda Access Portal
Other UsersPune Users
End Users access the grid through the Garuda Access Portal
HighAvailability
HighAvailability
Resource Manager for Grids
HighAvailability
HighAvailability
Resource Manager for Grids
Bangalore Users
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Garuda Access Portal
• Addresses the usability challenges of the Grid
• Supports submission of parallel and sequential jobs
• Support for Accounting
• Integration with Grid Scheduler under progress
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Collaborative Environments
• Enable Collaborative environment for the Grid developers, users/partners. Will facilitate development team meetings and collaborative project design/progress reviews
• IP based video conferencing over the high speed communication Fabric
• Initial Target : Enable all centres of C-DAC participating in the Garuda development & deployment to collaborate through video conferencing
• Also exploring Access Grid environment
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Program Development Environment
• Enable users to carry out entire program development life cycle for the Grid
• DIViA for the Grid
• Features– Supports MPICH-G2
debugging– Communication and
computational statistics in different graphical formats
– Identification of potential bottlenecks
– Unique method of tracing, leads to enhanced information with reduced log file size
• Debugger in Design Phase
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Management and Monitoring
• Monitors status & utilization of the Grid components : compute, network, softwares etc.
• Used by System Administrators and end users
• Being deployed at the Grid Monitoring and Management Centre(GMMC)
• User friendly interface
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Middleware & Security
• Deployed using Globus Toolkit, Commercial and C-DAC developed components
– GT2 for operational requirements– GT4 for research projects
• Resource Management and Scheduling– Moab from Cluster Resources for Grid Scheduling– Local Scheduling using Load Leveler for AIX Clusters
and Torque for Solaris and Linux Clusters
• Data Management– Storage Resource Broker from Nirvana for Data Grid
functionalities
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Resource Management and Scheduling
• Grid Scheduler from Cluster Resources– Industry Leading Scheduler– Components include Moab Workload Manager, Moab Grid
Scheduler and Moab Cluster Manager– Integrates with Globus
– Data Management through GASS and GridFTP– Job staging with GRAM/Gatekeeper services– User management through Globus user mapping files– Security through X509-based client authentication
• Grid Scheduler Features– Intelligent Data Staging– Co-Allocation & Multi-Sourcing– Service Monitoring and Management– Sovereignty (Local vs. Central Management Policies)– Virtual Private Cluster and Virtual Private Grid
• Local Resource Managers– Load Leveler on AIX– Torque on Solaris/Linux clusters
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Wide Area Grid
Administrator:Sets policies and manages via Cluster Manager for his or her own cluster and via Grid Resource Manager for
Grid policies
End Users: (In Multiple User Spaces)
Submit jobs via Garuda Grid Access Portal
GlobusGlobus
User Space 1User Space 1 User Space 2User Space 2 User Space NUser Space N
Grid FTPGrid FTP
Grid Resource Manager Interacts with Grid FTP to
Stage Data to each of the Clusters
Grid Resource Manager Leverages
the Security and Access Control
provided in Globus
Gri
d R
eso
urc
e M
anag
erG
rid
Res
ou
rce
Man
ager
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Local Area Grid (C-DAC Bangalore)
Administrators:Sets policies and
manages via Moab Cluster Manager
Moab Cluster ManagerMoab Cluster Manager
Moab Cluster Manager:Acts as the Interface, using
wizards and forms to improve ease of use and to unify interface to
Workload and Resource Managers
Garuda Access PortalGaruda Access Portal
End Users: (In a Single User Space)Submit jobs via web form Interface
Single User SpaceSingle User Space
Moab Workload ManagerMoab Workload Manager
Solaris Cluster Head Node
TorqueTorque
Moab Workload Manager: Enforces policies, monitors
workload and controls submissions through resource manager
Load LevelerLoad Leveler TorqueTorque
Linux Cluster Head Node AIX Cluster Head Node
…..
Unified Data Space
….. …..
OS and CommunicationOS and Communication OS and CommunicationOS and Communication OS and CommunicationOS and Communication
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Data Management• Enable data-oriented applications via an integrated
but distributed storage and data management infrastructure
• Requirements– Heterogeneous Data Access across Multiple Locations– Data Security– Reliability and Consistency of Data– Support for Unified Namespace and Multiple File Systems– Optimal turn-around for Data Access
– Parallel I/O– Bulk Operations– Intelligent Resource Selection and Data Routing– Latency Minimization
– Vertical and Horizontal Scalability
• Garuda Data Grid– Storage Resource Broker from Nirvana
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Clustering Technologies
• Software– High Performance Compilers– Message Passing Libraries– Performance and Debugging Tools– I/O Libraries, Parallel File System– Cluster Management Software– Available for AIX, Solaris and Linux
Clusters
• Hardware– 5Gbps SAN Technologies completed– Reconfigurable Computing Systems
for bioinformatics & cryptanalysis under progress
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Research Initiatives• Resource Broker
– Standards are yet to be formulated– Match the user requirements with the available resources– Address co-allocation of computation and communication– Forecasting the availability of resources
• Grid IDE– Writing and enabling applications to exploit the Grid– Compiling/cross-compiling across different platforms
– Seamless integration of complex functionalities – Support for multiple programming interfaces
• Semantic Grid Services (MIT, Chennai)– Publishing Grid Services– Intelligent discovery of Grid services– Integration with Garuda Portal
• Mobile Agent Framework– Monitoring of resources in the Grid– Grid software deployment and maintenance
• Network Simulation– Inputs for the next phase fabric architecture – To study impact of changes in traffic profile on the performance
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Garuda Communication Fabric
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Objectives & Deliverables
• Objective– Provide an ultra-high speed multi services
communication fabric connecting user organizations across 17 cities in the country
– Provide seamless & high speed access to the compute, data & other resources on the Grid
– In Collaboration with ERNET
• Deliverables– High-speed Communication Fabric connecting 17 cities– Grid Management & Monitoring Centre– IP based Collaborative Environment among select
centres
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Fabric Connectivity
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Features
• Ethernet based High Bandwidth capacity
• Scalable over entire geographic area
• High levels of reliability
• Fault tolerance and redundancy
• Interference resilience
• High security
• Effective Network Management
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Grid Management & Monitoring Centre(GMMC)
• To provide an integrated Grid Resource Management & Monitoring Framework
• Network Traffic Analysis and Congestion Management
• Change and Configuration Management
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Grid Resources
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Objective and Deliverables
• Objective– Provide heterogeneous
resources in the Grid including Compute, Data, Software and Scientific Instruments
– Deploy Test facilitates for Grid related research and development activities
• Deliverables– Grid enablement of C-DAC resources
at Bangalore and Pune
– Aggregation of Partner Resources
– Setting up of PoC Test Bed and Grid Labs at Bangalore, Pune, Hyderabad and Chennai
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Resources
• HPC Clusters & Storage from C-DAC– Bangalore : 128 CPU AIX Cluster,5 TB Storage– Pune : 64 CPU Solaris Cluster
: 16 CPU Linux Cluster, 4 TB Storage– Chennai : 16 CPU Linux Cluster, 2 TB Storage– Hyderabad : 16 CPU Linux Cluster, 2 TB Storage– The proposed 5 TF system to be part of the Grid
• Satellite Terminals from SAC Ahmedabad
• 2 TF Computing Cycles from IGIB Delhi
• 32 way SMP from Univ. of Hyderabad
• 64 CPU cluster from MIT, Chennai
• 64 CPU cluster from PRL, Ahmedabad
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Grid Partners
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Motivation and Status
• Motivation– Setup a User Group to Collaborate on
Research and Engineering of Technologies, Architectures, Standards and Applications in HPC and Grid Computing
– To Contribute to the aggregation of resources in the Grid
• Current Status– 37 research & academic institutions in the
17 cities have agreed in principle to participate
– ERNET-HQ in Delhi– 7 centres of C-DAC– Total of 45 institutions
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Partner Participation Institute of Plasma Research, Ahmedabad Physical Research Laboratory, Ahmedabad Space Applications Centre, Ahmedabad Harish Chandra Research Institute, Allahabad Motilal Nehru National Institute of Technology, Allahabad Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore Indian Institute of Astrophysics, Bangalore Indian Institute of Science, Bangalore Institute of Microbial Technology, Chandigarh Punjab Engineering College, Chandigarh Madras Institute of Technology, Chennai Indian Institute of Technology, Chennai Institute of Mathematical Sciences, Chennai
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Partner Participation (Contd.) Indian Institute of Technology, Delhi Jawaharlal Nehru University, Delhi Institute for Genomics and Integrative Biology, Delhi Indian Institute of Technology, Guwahati Guwahati University, Guwahati University of Hyderabad, Hyderabad Centre for DNA Fingerprinting and Diagnostics, Hyderabad Jawaharlal Nehru Technological University, Hyderabad Indian Institute of Technology, Kanpur Indian Institute of Technology, Kharagpur Saha Institute of Nuclear Physics, Kolkatta Central Drug Research Institute, Lucknow Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow
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Partner Participation (Contd.) Bhabha Atomic Research Centre, Mumbai Indian Institute of Technology, Mumbai Tata Institute of Fundamental Research, Mumbai IUCCA, Pune National Centre for Radio Astrophysics, Pune National Chemical Laboratory, Pune Pune University, Pune Indian Institute of Technology, Roorkee Regional Cancer Centre, Thiruvananthapuram Vikram Sarabhai Space Centre, Thiruvananthapuram Institute of Technology, Banaras Hindu University, Varanasi
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Applications of Importance for PoC Garuda
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Objectives and Deliverables
• Objectives– Enable applications of
national importance requiring aggregation of geographically distributed resources
• Deliverables– Grid enablement of
illustrative applications and some demonstrations such as
– Bioinformatics– Disaster Management
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Bioinformatics
• Bioinformatics Resources & Applications Facility (BRAF) on PARAM Padma
• Supports highly optimized Bioinformatics codes on the PARAM Padma
• Web computing portal providing all computational facility to solve related problems
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Disaster Management
PARAM Padma
at Bangalore
Flight data transmission from
nearby Airport
Grid Partner Resource
at Pune
User Agencies
GRID Communication
Fabric
High Speed Communicati
on
UserAgenci
es
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Disaster Management (contd..)• Requirements
– Timely dissemination of disaster information to user agencies
– Organize logistics around automated and secure work flow and data base
• Challenges– Widely spread application resources and
types : disaster sensors, compute, application experts– Turn around time for the work flow