2. August 23, 2006Talk at SASTRA1 By P.S.Dhekne, BARC dhekne@barc.gov.in Parallel, Cluster and Grid Computing

3. August 23, 2006Talk at SASTRA2 High Performance Computing Branch of computing that deals with extremely powerful computers and the applications that use them Supercomputers: Fastest computer at any given point of time HPC Applications: Applications that cannot be solved by conventional computers in a reasonable amount of time

4. August 23, 2006Talk at SASTRA3 Supercomputers Characterized by very high speed, very large memory Speed measured in terms of number of floating point operations per second (FLOPS) Fastest Computer in the world: “Earth Simulator” (NEC, Japan) – 35 Tera Flops Memory in the order of hundreds of gigabytes or terabytes

5. August 23, 2006Talk at SASTRA4 HPC Technologies Different approaches for building supercomputers –Traditional : Build faster CPUs Special Semiconductor technology for increasing clock speed Advanced CPU architecture: Pipelining, Vector Processing, Multiple functional units etc. –Parallel Processing : Harness large number of ordinary CPUs and divide the job between them

6. August 23, 2006Talk at SASTRA5 Traditional Supercomputers Eg: CRAY Very complex architecture Very high clock speed results in very high heat dissipation and advanced cooling techniques (Liquid Freon / Liquid Nitrogen) Custom built or produced as per order Extremely expensive Advantages: Program development is conventional and straight forward

7. August 23, 2006Talk at SASTRA6 Alternative to Supercomputer Parallel Computing: the use of multiple computers or processors working together on a single problem; harness large number of ordinary CPUs and divide the job between them –each processor works on its section of the problem –processors are allowed to exchange SequentialParallel 1 10000 1 2500 2501 5000 5001 7500 7501 10000 cpu 1 cpu 2 cpu 3 cpu 4 Big advantages of parallel computers: 1.total computing performance multiples of processors used 2.total very large amount of memory to fit very large programs 3.Much lower cost and can be developed in India information with other processors via fast interconnect path

8. August 23, 2006Talk at SASTRA7 Types of Parallel Computers The parallel computers are classified as – shared memory – distributed memory Both shared and distributed memory systems have: 1.processors: now generally commodity processors 2.memory: now general commodity DRAM/DDR 3.network/interconnect: between the processors or memory

9. August 23, 2006Talk at SASTRA8 Interconnect Method There is no single way to connect bunch of processors The manner in which the nodes are connected - Network & Topology Best choice would be a fully connected network (every processor to every other). Unfeasible for cost and scaling reasons : Instead, processors are arranged in some variation of a grid, torus, tree, bus, mesh or hypercube. 3-d hypercube 2-d mesh 2-d torus

10. August 23, 2006Talk at SASTRA9 Block Diagrams … Memory Interconnection Network P1P2P3P4P5 A Shared Memory Parallel Computer Processors

11. August 23, 2006Talk at SASTRA10 Block Diagrams … Interconnection Network P1P2P3P4P5 M1M2M3M4M5 A Distributed Memory Parallel Computer

12. August 23, 2006Talk at SASTRA11 Performance Measurements Speed of a supercomputer is generally denoted in FLOPS (Floating Point Operations per second) –MegaFlops (MFLOPS), Million (10 6 )FLOPS –GigaFLOPS (GFLOPS), Billion (10 9 )FLOPS –TeraFLOPS (TFLOPS), Trillion (10 12 ) FLOPS

13. August 23, 2006Talk at SASTRA12 Sequential vs. Parallel Programming Conventional programs are called sequential (or serial) programs since they run on one cpu only as in a conventional (or sequential) computer Parallel programs are written such that they get divided into multiple pieces, each running independently and concurrently on multiple cpus. Converting a sequential program to a parallel program is called parallelization.

14. August 23, 2006Talk at SASTRA13 Terms and Definitions Speedup of a parallel program: = Time taken on 1 cpus / Time taken on ‘n’ cpus Ideally Speedup should be ‘n’

15. August 23, 2006Talk at SASTRA14 Terms and Definitions Efficiency of a parallel program: = Speedup / No. of processors Ideally efficiency should be 1 (100 %)

16. August 23, 2006Talk at SASTRA15 Problem areas in parallel programs Practically, speedup is always less than ‘n’ and efficiency is always less than 100% Reason 1: Some portions of the program cannot be run in parallel (cannot be split) Reason 2: Data needs to be communicated among the cpus. This involves time for sending the data and time in waiting for the data The challenge in parallel programming is to suitably split the program into pieces such that speedup and efficiencies approach the maximum

17. August 23, 2006Talk at SASTRA16 Parallelism Property of an algorithm that lends itself amenable to parallelization Parts of the program that has inherent parallelism can be parallelized (divided into multiple independent pieces that can execute concurrently)

18. August 23, 2006Talk at SASTRA17 Types of parallelism Control parallelism (Algorithmic parallelism): –Different portions (or subroutines/functions) can execute independently and concurrently Data parallelism –Data can be split up into multiple chunks and processed independently and concurrently –Most scientific applications exhibit data parallelism

19. August 23, 2006Talk at SASTRA18 Parallel Programming Models Different approaches are used in the development of parallel programs Shared Variable Model: Best suited for shared memory parallel computers Message Passing Model: Best suited for distributed memory parallel computers

20. August 23, 2006Talk at SASTRA19 Message Passing Most commonly used method of parallel programming Processes in a parallel program use messages to transfer data between themselves Also used to synchronize the activities of processes Typically consists of send/receive operations

21. August 23, 2006Talk at SASTRA20 In the absence of any standardization initial parallel machines were designed with varied architectures having different network topologies BARC started Supercomputing development to meet computing demands of in-house users with the aim to provide inexpensive high-end computing since 1990-91 and have built several models How we started

22. August 23, 2006Talk at SASTRA21 Selection Of Main Components Architecture Simple Scalable Processor Independent Inter Connecting Network Scalable bandwidth Architecture independent Cost effective Parallel Software Environment User friendly Portable Comprehensive Debugging tools

23. August 23, 2006Talk at SASTRA22 SINGLE CLUSTER OF ANUPAM TC X25 Ethernet SCSI other systems Node 1 slave Node 15 slave Node 0 master Node 2 slave 860/XP,50MHZ 128 KB-512KB cache & 64-Mb- 256 Mb memory MULTIBUS II TERMINALS DISKSDISKS

24. August 23, 2006Talk at SASTRA23 0 115 wscsi 0 115 wscsi 0 1 15 wscsi 0 115 wscsi 64 NODE ANUPAM CONFIGURATION Y Y X X MB II BUS

25. August 23, 2006Talk at SASTRA24 8 NODE ANUPAM CONFIGURATION

26. August 23, 2006Talk at SASTRA25 ANUPAM APPLICATIONS Finite Element Analysis Pressure Contour in LCA Duct 64-Node ANUPAM Protein Structures 3-D Plasma Simulations

27. August 23, 2006Talk at SASTRA26 Problem Specification: Cylindrical geometry Radius = 8 km Height= 8 km No. of mesh points 80,000 No. of Energy groups 42 S N order 16 Conclusions: Use of 10 processors of the BARC computer system reduces the run time by 6 times. Simulations on BARC Computer System 2-D Atmospheric Transport Problem Estimation of Neutron-Gamma Dose up to 8 km from the source

28. August 23, 2006Talk at SASTRA27 OTHER APPLICATIONS OF ANUPAM SYSTEM * Protein Structure Optimization * AB Initio Electronic Structure Calculations * Neutron Transport Calculations * AB Initio Molecular Dynamics Simulations * Computational Structure Analysis * Computational Fluid Dynamics ( ADA, LCA) * Computational Turbulent flow * Simulation Studies in Gamma-Ray Astronomy * Finite Element Analysis of Structures * Weather Forecasting

29. August 23, 2006Talk at SASTRA28 Key Benefits Simple to use ANUPAM uses user familiar Unix environment with large memory & specially designed parallelizing tools No parallel language needed PSIM – parallel simulator runs on any Unix based system Scalable and processor independent

30. August 23, 2006Talk at SASTRA29 Bus based architecture Dynamic Interconnection network providing full connectivity and high speed and TCP/IP support Simple and general purpose industry back-plain bus Easily available off-the-shelf, low cost MultiBus, VME Bus, Futurebus … many solutions Disadvantages One communication at a time Limited scalability of applications in bus based systems Lengthy development cycle for specialized hardware i860, Multibus-II reaching end of line, so radical change in architecture was needed

31. August 23, 2006Talk at SASTRA30 Typical Computing, Memory & Device Attachment CPU MemoryBusMemoryBus Memory Device Card Input/Output Bus

32. August 23, 2006Talk at SASTRA31 Cache CPU Local Memory Remote Memory Memory Hierarchy

33. August 23, 2006Talk at SASTRA32 Ethernet: The Unibus of the 80s (UART of the 90s) compute server print server file server comm server Clients 2Km

34. August 23, 2006Talk at SASTRA33 Ethernet: The Unibus of the 80s Ethernet designed for –DEC: Interconnect VAXen, terminals –Xerox: enable distributed computing (SUN Micro) Ethernet evolved into a hodge podge of nets and boxes Distributed computing was very hard, evolving into –expensive, assymmetric, hard to maintain, –client server for a VendorIX –apps are bound to a configuration & VendorIX! –network is NOT the computer Internet model is less hierarchical, more democratic

35. August 23, 2006Talk at SASTRA34 Networks of workstations (NOW) New concept in parallel computing and parallel computers Nodes are full-fledged workstations having cpu, memory, disks, OS etc. Interconnection through commodity networks like Ethernet, ATM, FDDI etc. Reduced Development Cycle, mostly restricted to software Switched Network topology

36. NODE-01 NODE-02NODE-03NODE-04NODE-05NODE-06NODE-07NODE-08 NODE-09 NODE-10NODE-11NODE-12NODE-13NODE-14NODE-15NODE-16 FILE SERVER FAST ETHERNET SWITCH UPLINK Typical ANUPAM x86 Cluster CAT-5 CABLE

37. August 23, 2006Talk at SASTRA36 ANUPAM - Alpha Each node is a complete Alpha workstation with 21164 cpu, 256 MB memory, Digital UNIX OS etc. Interconnection thru ATM switch with fiber optic links @ 155 Mbps

38. August 23, 2006Talk at SASTRA37 PC Clusters : Multiple PCs Over the last few years, computing power of Intel PCs have gone up considerably (from 100 MHz to 3.2 GHz in 8 years) with fast, cheap network & disk (in built ) Intel processors beating conventional RISC chips in performance PCs are freely available from several vendors Emergence of free Linux as a robust, efficient OS with plenty of applications Linux clusters (use of multiple PCs) are now rapidly gaining popularity in academic/research institutions because of low cost, high performance and availability of source code

39. August 23, 2006Talk at SASTRA38 Trends in Clustering Clustering is not a new idea, it has become affordable, can be build easily (plug&play) now. Even Small colleges have it.

40. August 23, 2006Talk at SASTRA39  LB Cluster - Network load distribution and LB  HA Cluster - Increase the Availability of systems  HPC Cluster ( Scientific Cluster ) - Computation-intensive  Web farms - Increase HTTP/SEC  Rendering Cluster – Increase Graphics speed HPC : High Performance Computing HA : High Availability LB : Load Balancing Cluster based Systems Clustering is replacing all traditional Computing platforms and can be configured depending on the method and applied areas `

41. August 23, 2006Talk at SASTRA40 Computing Trends It is fully expected that the substantial and exponential increases in performance of IT will continue for the foreseeable future ( at least next 50 years) in terms of –CPU Power ( 2X – every 18 months) –Memory Capacity (2X – every 18 months) –LAN/WAN speed (2X – every 9 months) –Disk Capacity (2X – every 12 months) It is expected that all computing resources will continue to become cheaper and faster, though not necessarily faster than the computing problems we are trying to solve.

42. August 23, 2006Talk at SASTRA41 Processor Speed Comparison 1356810Intel Itanium-2, 900 MHz (64 bit) 6 776621Alpha, 1GHz (64 bit)5 571511Alpha, 833 MHz (64 bit) 4 840852Pentium-IV, 2.4 GHz3 591574Pentium-IV, 1.7 GHz2 191231Pentium-III, 550 MHz1 SPECfp_base 2000 SPECint_base 2000 ProcessorSr. No.

43. August 23, 2006Talk at SASTRA42 Technology Gaps Sheer CPU speed is not enough Matching of Processing speed, compiler performance, cache size and speed, memory size and speed, disk size and speed, and network size and speed, interconnect & topology is also important Application and middleware software also adds to performance degradation if not good

44. August 23, 2006Talk at SASTRA43 Interconnect-Related Terms Most critical component of HPC still remains to be interconnect technology and network topology Latency: –Networks: How long does it take to start sending a "message"? Measured in microseconds- startup time –Processors: How long does it take to output results of some operations, such as floating point add, divide etc., which are pipelined?) Bandwidth: What data rate can be sustained once the message is started? Measured in Mbytes/sec or Gbytes/sec

45. August 23, 2006Talk at SASTRA44 High Speed Networking Network bandwidth is improving – LAN are having 10, 100, 1000, 10000 Mbps – WAN are based on ATM with 155, 622, 2500 Mbps With constant advances in Information & Communication technology - Processors and Networks are merging into one infrastructure - System Area or Storage Area Networks (Myrinet, Cray- link, Fiber channel, SCI etc): Low latency, High Bandwidth, Scalable to large numbers of nodes

46. August 23, 2006Talk at SASTRA45 Processor Interconnect Technology 6 2-102500Quadric Switch5 <400 nsec10,000InfiniBand4 1.5-142500SCI based Woulfkit3 1001000Gigabit Ethernet2 60100Fast Ethernet1 Latency time Microseconds Bandwidth MBits/sec Communication Technology Sr. No. 10-G Ethernet10,000<100

47. August 23, 2006Talk at SASTRA46 Interconnect Comparison FeatureFast EthernetGigabitSCI Latency88.07µs44.93µs (16.88 µs)5.55 µs (1.61 µs) Bandwidth11 Mbytes/Sec90 Mbytes/Sec250 Mbytes/Sec  Across Machines  Two processes in a given machine

48. August 23, 2006Talk at SASTRA47 Switched Network Topology Interconnection Networks such as ATM, Ethernet etc. are available as switched networks Switch implements a dynamic interconnection network providing all-to-all connectivity on demand Switch allows multiple independent communications simultaneously Full duplex mode of communication Disadvantages: Single point of failure, Finite capacity Disadvantages: Scalability, Cost for higher node count

49. August 23, 2006Talk at SASTRA48 Scalable Coherent Interface (SCI) High Bandwidth, Low latency SAN interconnect for clusters of workstations (IEEE 1596) Standard for point to point links between computers Various topologies possible: Ring, Tree, Switched Rings, Torus etc. Peak Bandwidth: 667 MB/s, Latency < 5 microseconds

50. August 23, 2006Talk at SASTRA49 Gigabit EthernetSCI Total latency (  s) Latency within node (  s) Total Latency (  s) Latency within node (  s) 44.9316.885.551.61 ANUPAM-Xeon Performance

51. August 23, 2006Talk at SASTRA50 No. Of nodes : 128 Compute Node: Processor:Dual Intel Pentium Xeon @ 2.4 GHz Memory:2 GB per node File Server:Dual Intel based with RAID 5, 360 GB Interconnection networks: 64 bit Scalable Coherent Interface (2D Torus connectivity) Software:Linux, MPI, PVM, ANULIB Anulib Tools:PSIM, FFLOW, SYN, PRE, S_TRACE Benchmarked Performance:362 GFLOPS for High Performance Linpack ANUPAM P-Xeon Parallel Supercomputer

52. August 23, 2006Talk at SASTRA51 ANUPAM clusters Sustained speed on 84 P-III processors: 15 GFLOPS Year of introduction :- 2001 Year: 2002, Sustained speed on 64 P-IV cpus : 72 GFLOPS Sustained speed on 128 Xeon processors :- 365 GFLOPS Year of introduction :- 2003 ANU64 ASHVA ARUNA

53. August 23, 2006Talk at SASTRA52 ANUPAM-Pentium DateModelNode-MicroprocessorInter Comm. Mflops Oct/984-node PIIPentium PII/266MhzEthernet/100 248 Mar/99 16-node PIIPentium PII/333MhzEthernet/100 1300 Mar/0016 node PIIIPentium PIII/550 MhzGigabit Eth. 3500 May/0184 node PIIIPentium PIII/650 MhzGigabit Eth. 15000 June/02 64 Node PIV Pentium PIV/1.7GHzGiga & SCI72000 August/03 128 Node-Xeon Pentium/Xeon 2.4 Ghz Giga & SCI362000 ANUPAM series of super computers after 1997

54. August 23, 2006Talk at SASTRA53 Table of comparison ( with precise ( 64 bit ) computations ) Program name1 + 4 Anupam Alpha 1 + 8 Anupam Alpha Cray XMP 216 T-80 (24 Hr- forecast) 14 minutes11 minutes12.5 minutes All timings are Wall clock times

55. August 23, 2006Talk at SASTRA54 BARC’s New Super Computing Facility  BARC’s new Supercomputing facility was inaugurated by Honorable PM, Dr. Manmohan Singh on 15th November 2005.  A 512-node ANUPAM-AMEYA Supercomputer was developed with a speed of 1.7 Teraflop for HPC benchmark.  A 1024 node Supercomputer ( ~ 5 Tera flop) is planned during 2006-07  Being used by in-house users 512 Node ANUPAM-AMEYA External View of New Super Computing Facility

56. August 23, 2006Talk at SASTRA55 Support equipment Terminal servers –Connect serial consoles from 16 nodes onto a single ethernet link –Consoles of each node can be accessed using the terminal servers and management network Power Distribution Units –Network controlled 8 outlet power distribution unit –Facilities such as power sequencing, power cycling of each node possible –Current monitoring Racks –14 racks of 42U height, 1000 mm depth, 600 mm width

57. August 23, 2006Talk at SASTRA56 Software components Operating System on each node of the cluster is Scientific Linux 4.1 for 64 bit architecture –Fully compatible with Redhat Enterprise Linux –Kernel version 2.6 ANUPRO Parallel Programming Environment Load Sharing and Queuing System Cluster Management

58. August 23, 2006Talk at SASTRA57 ANUPRO Programming Environment ANUPAM supports following programming interfaces –MPI –PVM –Anulib –BSD Sockets Compilers –Intel Fortran Compiler –Portland Fortran Compiler Numerical Libraries –BLAS (ATLAS and MKL implementations) –LAPACK (Linear Algebra Package) –Scalapack (Parallel Lapack) Program development tools –MPI performance monitoring tools (Upshot, Nupshot, Jumpshot) –ANUSOFT tool suite (FFLOW, S_TRACE, ANU2MPI, SYN)

59. August 23, 2006Talk at SASTRA58 Load Sharing and Queuing System Torque based system resource manager Keep track of available nodes in the system Allot nodes to jobs Maintain job queues with job priority, reservations User level commands to submit jobs, delete jobs, find out job status, find out number of available nodes Administrator level commands to manage nodes, jobs and queues, priorities, reservations

60. August 23, 2006Talk at SASTRA59 ANUNETRA : Cluster Management System Management and Monitoring of one or more clusters from a single interface Monitoring functions: –Status of each node and different metrics (load, memory, disk space, processes, processors, traffic, temperature and so on) –Jobs running on the system –Alerts to the administrators in case of malfunctions or anomalies –Archival of monitored data for future use Management functions: –Manage each node or groups of nodes (reboots, power cycling, online/offline, queuing and so on) –Job management

61. August 23, 2006Talk at SASTRA60 Metric View on Ameya Node View on Ameya

62. August 23, 2006Talk at SASTRA61 SMART: Self Monitoring And Rectifying Tool Service running on each node which keeps track of things happening in the system –Hanging jobs –Services terminated abnormally SMART takes corrective action to remedy the situation and improve availability of the system

63. August 23, 2006Talk at SASTRA62 Accounting System Maintains database entries for each and every job run on the system –Job ID, user name, number of nodes, –Queue name, API (mpi, pvm, anulib) –Submit time, start and end time –End status (finished, cancelled, terminated) Computes system utilization User wise, node wise statistics for different periods of time

64. August 23, 2006Talk at SASTRA63 Accounting system – Utilization plot

65. August 23, 2006Talk at SASTRA64 Other tools Console logger –Logs all console messages of each node into a database for diagnostics purposes Sync tool –Synchronizes important files across nodes Automated backup –Scripts for taking periodic backups of user areas onto the tape libraries Automated installation service –Non-interactive installation of node and all required software

66. August 23, 2006Talk at SASTRA65  PFS gives a different view of I/O system with its unique architecture and hence provides an alternative platform for development of I/O intensive applications  Data(File) striping in a distributed environment.  Supports collective I/O operations.  Interface as close to a standard LINUX interface as possible.  Fast access to file data in parallel environment irrespective of how and where file is distributed.  Parallel file scatter and gather operations Parallel File System (PFS)

67. August 23, 2006Talk at SASTRA66 Architecture of PFS MIT SERVER SERVER M I T D I T PFS DAEMON I/O DAEMON LI T I/O DAEMON LI T I/O DAEMON LI T I/O Manager Request Data NODE 1 NODE 2 NODE N PFSManager

68. August 23, 2006Talk at SASTRA67  Processing ( parallelization of computation)  I/O ( parallel file system)  Visualization (parallelized graphic pipeline/ Tile Display Unit) Complete solution to scientific problems by exploiting parallelism for

69. August 23, 2006Talk at SASTRA68 Domain-specific Automatic Parallelization (DAP)  Domain: a class of applications such as FEM applications  Experts use domain-specific knowledge  DAP is a combination of expert system and parallelizing compiler  Key features: interactive process, experience- based heuristic techniques, and visual environment

70. August 23, 2006Talk at SASTRA69 Operation and Management Tools Manual installation of all nodes with O.S., compilers, Libraries etc is not only time consuming it is tedious and error prone Constant monitoring of hardware/networks and software is essential to report healthiness of the system while running 24/7 operation Debugging and communication measurement tools are needed Tools are also needed to measure load, free CPU, predict load, checkpoint restart, replace failed node etc. We have developed all these tools to enrich ANUPAM software environment

71. August 23, 2006Talk at SASTRA70 Programming so many nodes concurrently remains a major barrier for most applications - Source code should be known & parallisable - Scalable algorithm development is not an easy task - All resources are allotted for a single job - User has to worry about message passing, synchronization and scheduling of his job - 15% users only require these solutions, rest can manage with normal PCs Fortunately lot of free MPI codes and even parallel solvers are now available Still there is large gap between technology & usage as parallel tools are not so user friendly Limitations of Parallel Computing

72. August 23, 2006Talk at SASTRA71 Evolution in Hardware Compute Nodes: –Intel i860 –Alpha 21x64 –Intel x86 Interconnection Network: –Bus : MultiBus-II, Wide SCSI –Switched Network: ATM, Fast Ethernet, Gigabit Ethernet –SAN: Scalable Coherent Interface

73. August 23, 2006Talk at SASTRA72 Evolution in Software Parallel Program Development API: –ANULIB (Proprietary) to MPI (Standard) Runtime environment –I/O restricted to master only (860) to Full I/O (Alpha and x86) –One program at a time (860) to Multiple Programs to Batch operations Applications –In-house parallel to Ready made parallel applications –Commercially available parallel software

74. August 23, 2006Talk at SASTRA73 Issues in building large clusters Scalability of interconnection network Scalability of software components –Communication Libraries –I/O Subsystem –Cluster Management Tools –Applications Installation and Management Procedures Troubleshooting Procedures

75. August 23, 2006Talk at SASTRA74 Other Issues in operating large clusters Space Management –Node form factor –Layout of the nodes –Cable routing and weight Power Management Cooling arrangements

76. August 23, 2006Talk at SASTRA75 The P2P Computing Computing based on P2P architecture allows to share distributed resources with each other with or without the support from a server. How do you manage under utilized resources? - It is seen that utilization of desktop PC is typically <10 %, and this percentage is decreasing even further as PCs are becoming more powerful - Large organizations must be having more than thousand PCs, each delivering > 20 MFlops and this power is growing with every passing day ……Trick is to use Cycle Stealing mode - Each PC now has about 20Gbyte disc capacity 80Gb X 1000 = 80 Terabyte storage space is available ; Very large File storage –How do you harness power of so many PCs in a large organization? …….. Issue of “Owership” hurdle, to be resolved – Latency & bandwidth of LAN environment is quite adequate for P2P computing………Space management no problem; use PCs wherever they are!!

77. August 23, 2006Talk at SASTRA76 INTERNET COMPUTING Today you can’t run your jobs on the Internet Internet Computing using idle PC’s, is becoming an important computing platform (Seti@home,Napster,Gnutella,Freenet, KaZak)Seti@home –www is now a promising candidate for core component of wide area distributed computing environment. –Efficient Client/server models & protocols –Transparent networking, navigation & GUI with multimedia access & dissemination for data visualization –Mechanism for distributed computing such as CGI.Java With improved performance (price/performance) & the availability of Linux, Web Services ( SOAP, WSDL, UDDI,WSFL), COM technology it is easy to develop loosely coupled distributed applications

78. August 23, 2006Talk at SASTRA77 Difficulties in present systems –As technology is constantly changing there is a need for regular upgrade/enhancement –Cluster/Servers are not fail safe and fault tolerant. –Many systems are dedicated to a single application, thus idle when application has no load –Many clusters in the organization remain idle –For operating a computer centre 75 % cost come from environment upkeep, staffing, operation and maintenance. –Computers, Networks, Clusters, Parallel Machines and Visual systems are not tightly coupled by software; difficult for users to use it

79. August 23, 2006Talk at SASTRA78 Computer Assisted Science & Engineering CASE High Speed Network Disks PCs, SMPs Clusters Problem Solving Environment RAID Visual Data Server Analysis – a very general model Can we tie all components tightly by software? Menu -Template - Solver - Pre & Post - Mesh

80. August 23, 2006Talk at SASTRA79 User Access Point Resource Broker Grid Resources Result GRID CONCEPT

81. August 23, 2006Talk at SASTRA80 Are Grids a Solution? Goals of Grid Computing Reduce computing costs Increase computing resources Reduce job turnaround time Enable parametric analyses Reduce Complexity to Users Increase Productivity Technology Issues Clusters Internet infrastructure MPP solver adoption Administration of desktop Use middleware to automate Virtual Computing Centre “Dependable, consistent, pervasive access to resources” “Grid Computing” means different things to different people.

82. August 23, 2006Talk at SASTRA81 What is needed? Reply Choice Computational Resources Clusters MPP Workstations MPI, PVM,Condor... Request Broker Scheduler Database Client - RPC like Matlab Mathematica C, Fortran Java, Perl Java GUI GatekeeperGatekeeper ISP

83. August 23, 2006Talk at SASTRA82 Why Migrate Processes ?  LOAD BALANCING  Reduce average response time  Speed up individual jobs  Gain higher throughput  MOVE PROCESS CLOSER TO ITS RESOURCES  Use resources effectively  Reduce network traffic  INCREASE SYSTEMS RELIABILITY  MOVE PROCESS TO A MACHINE HOLDING / CONFIDENTIAL DATA

84. August 23, 2006Talk at SASTRA83 Process FILE-SERVER Process PR-JOB3 PR-JOB1 PR-JOB2 Process PR-JOB1 PR-JOB2 Process PR-JOB2 PR-PARL PR-JOB3 Process

85. August 23, 2006Talk at SASTRA84 What does the Grid do for you? You submit your work And the Grid –Finds convenient places for it to be run –Organises efficient access to your data Caching, migration, replication –Deals with authentication to the different sites that you will be using –Interfaces to local site resource allocation mechanisms, policies –Runs your jobs, Monitors progress, Recovers from problems,Tells you when your work is complete If there is scope for parallelism, it can also decompose your work into convenient execution units based on the available resources, data distribution

86. August 23, 2006Talk at SASTRA85 User Interface (UI) User Interface (UI):The place where users logon to the Grid Computing Element (CE) Computing Element (CE): A batch queue on a site’s computers where the user’s job is executed Storage Element (SE) Storage Element (SE): provides (large-scale) storage for files Resource Broker (RB) Resource Broker (RB): Matches the user requirements with the available resources on the Grid Main components Information System Information System: Characteristics and status of CE and SE (Uses “GLUE schema”)

87. August 23, 2006Talk at SASTRA86 INTERNET Virtual organisations negotiate with sites to agree access to resources Grid middleware runs on each shared resource to provide –Data services –Computation services –Single sign-on Distributed services (both people and middleware) enable the grid Typical current grid E-infrastructure is the key !!!

88. August 23, 2006Talk at SASTRA87 Biomedical applications

89. EGEE tutorial, Seoul88 Earth sciences applications Earth Observations by Satellite –Ozone profiles Solid Earth Physics –Fast Determination of mechanisms of important earthquakes Hydrology –Management of water resources in Mediterranean area (SWIMED) Geology –Geocluster: R&D initiative of the Compagnie Générale de Géophysique  A large variety of applications is the key !!!

90. August 23, 2006Talk at SASTRA89 Grid initiatives CroGrid

91. August 23, 2006Talk at SASTRA90 GARUDA Department of Information Technology (DIT), Govt. of India, has funded CDAC to deploy computational grid named GARUDA as Proof of Concept project. It will connect 45 institutes in 17 cities in the country at 10/100 Mbps bandwidth.

92. August 23, 2006Talk at SASTRA91 Other Grids in India EU-IndiaGrid (ERNET, C-DAC, BARC,TIFR,SINP,PUNE UNIV, NBCS) Coordination with Geant for Education Research DAE/DST/ERNET MOU for Tier II LHC Grid (10 Univ) BARC MOU with INFN, Italy to setup Grid research Hub C-DAC’s GARUDA Grid Talk about Bio-Grid and Weather-Grid

93. August 23, 2006Talk at SASTRA92 Summary There have been three generations of ANUPAM, all with different architectures, hardware and software Usage of ANUPAM has increased due to standardization in programming models and availability of parallel software Parallel processing awareness has increased among users Building parallel computers is a learning experience Development of Grid Computing is equally challenging

94. August 23, 2006Talk at SASTRA93 THANK YOU