GRID COMPUTING Faisal N Abu Khzam Michael A Langston University of Tennessee 1 Outline Hour 1 Introduction Break Hour 2 Using the Grid Break Hour 3 Ongoing Research Q A Session 2 Hour 1 Introduction What is Grid Computing Who Needs It An Illustrative Example Grid Users Current Grids 3 What is Grid Computing Computational Grids Homogeneous e g Clusters Heterogeneous e g with one of a kind instruments Cousins of Grid Computing Methods of Grid Computing 4 Computational Grids A network of geographically distributed resources including computers peripherals switches instruments and data Each user should have a single login account to access all resources Resources may be owned by diverse organizations 5 Computational Grids Grids are typically managed by gridware Gridware can be viewed as a special type of middleware that enable sharing and manage grid components based on user requirements and resource attributes e g capacity performance availability 6 Cousins of Grid Computing Parallel Computing Distributed Computing Peer to Peer Computing Many others Cluster Computing Network Computing Client Server Computing Internet Computing etc 7 Distributed Computing People often ask Is Grid Computing a fancy new name for the concept of distributed computing In general the answer is no Distributed Computing is most often concerned with distributing the load of a program across two or more processes 8 PEER2PEER Computing Sharing of computer resources and services by direct exchange between systems Computers can act as clients or servers depending on what role is most efficient for the network 9 Methods of Grid Computing Distributed Supercomputing High Throughput Computing On Demand Computing Data Intensive Computing Collaborative Computing Logistical Networking 10 Distributed Supercomputing Combining multiple high capacity resources on a computational grid into a single virtual distributed supercomputer Tackle problems that cannot be solved on a single system 11 High Throughput Computing Uses the grid to schedule large numbers of loosely coupled or independent tasks with the goal of putting unused processor cycles to work 12 On Demand Computing Uses grid capabilities to meet short term requirements for resources that are not locally accessible Models real time computing demands 13 Data Intensive Computing The focus is on synthesizing new information from data that is maintained in geographically distributed repositories digital libraries and databases Particularly useful for distributed data mining 14 Collaborative Computing Concerned primarily with enabling and enhancing human to human interactions Applications are often structured in terms of a virtual shared space 15 Logistical Networking Global scheduling and optimization of data movement Contrasts with traditional networking which does not explicitly model storage resources in the network Called logistical because of the analogy it bears with the systems of warehouses depots and distribution channels 16 Who Needs Grid Computing A chemist may utilize hundreds of processors to screen thousands of compounds per hour Teams of engineers worldwide pool resources to analyze terabytes of structural data Meteorologists seek to visualize and analyze petabytes of climate data with enormous computational demands 17 An Illustrative Example Tiffany Moisan a NASA research scientist collected microbiological samples in the tidewaters around Wallops Island Virginia She needed the high performance microscope located at the National Center for Microscopy and Imaging Research NCMIR University of California San Diego 18 Example continued She sent the samples to San Diego and used NPACI s Telescience Grid and NASA s Information Power Grid IPG to view and control the output of the microscope from her desk on Wallops Island Thus in addition to viewing the samples she could move the platform holding them and make adjustments to the microscope 19 Example continued The microscope produced a huge dataset of images This dataset was stored using a storage resource broker on NASA s IPG Moisan was able to run algorithms on this very dataset while watching the results in real time 20 Grid Users Grid developers Tool developers Application developers End Users System Administrators 21 Grid Developers Very small group Implementers of a grid protocol who provides the basic services required to construct a grid 22 Tool Developers Implement the programming models used by application developers Implement basic services similar to conventional computing services User authentication authorization Process management Data access and communication 23 Tool Developers Also implement new grid services such as Resource locations Fault detection Security Electronic payment 24 Application Developers Construct grid enabled applications for end users who should be able to use these applications without concern for the underlying grid Provide programming models that are appropriate for grid environments and services that programmers can rely on when developing higher level applications 25 System Administrators Balance local and global concerns Manage grid components and infrastructure Some tasks still not well delineated due to the high degree of sharing required 26 Some Highly Visible Grids The NSF PACI NCSA Alliance Grid The NSF PACI SDSC NPACI Grid The NASA Information Power Grid IPG The Distributed Terascale Facility DTF Project 27 DTF Currently being built by NSF s Partnerships for Advanced Computational Infrastructure PACI A collaboration NCSA SDSC Argonne and Caltech will work in conjunction with IBM Intel Quest Communications Myricom Sun Microsystems and Oracle 28 DTF Expectations A 40 billion bits per second optical network Called TeraGrid is to link computers visualization systems and data at four sites Performs 11 6 trillion calculations per second Stores more than 450 trillion bytes of data 29 GRID COMPUTING BREAK 30 Hour 2 Using the Grid Globus Condor Harness Legion IBP NetSolve Others 31 Globus A collaboration of Argonne National Laboratory s Mathematics and Computer Science Division the University of Southern California s Information Sciences Institute and the University of Chicago s Distributed Systems Laboratory Started in 1996 and is gaining popularity year after year 32 Globus A project to develop the underlying technologies needed for the construction of computational grids Focuses on execution environments for integrating widely distributed computational platforms data
View Full Document
Unlocking...