Grid%20Computing

1
Grid Computing

• Outline
• Introduction
• Using the grid
• Ongoing research
• The presentation is based on the web, especially
  the work of Faisal N. Abu-Khzam Michael A.
  Langston (University of Tenessee)

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Introduction

• What is Grid Computing?
• Who Needs It?
• An Illustrative Example
• Grid Users
• Current Grids

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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

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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.

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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)

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Cousins of Grid Computing

• Parallel Computing
• Distributed Computing
• Peer-to-Peer Computing
• Many others Cluster Computing, Network
  Computing, Client/Server Computing, Internet
  Computing, etc...

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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.

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P2P 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.

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Methods of Grid Computing

• Distributed Supercomputing
• High-Throughput Computing
• On-Demand Computing
• Data-Intensive Computing
• Collaborative Computing
• Logistical Networking

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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.

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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.

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On-Demand Computing

• Uses grid capabilities to meet short-term
  requirements for resources that are not locally
  accessible.
• Models real-time computing demands.

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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.

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Collaborative Computing

• Concerned primarily with enabling and enhancing
  human-to-human interactions.
• Applications are often structured in terms of a
  virtual shared space.

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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.

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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.

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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.

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Example (continued)

• She sent the samples to San Diego and used
  NPACIs Telescience Grid and NASAs 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.
• The microscope produced a huge dataset of images.
• This dataset was stored using a storage resource
  broker on NASAs IPG.
• Moisan was able to run algorithms on this very
  dataset while watching the results in real time.

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Grid Users

• Grid developers
• Tool developers
• Application developers
• End Users
• System Administrators

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Grid Developers

• Very small group.
• Implementers of a grid protocol who provides
  the basic services required to construct a grid.

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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
• Also implement new (grid) services such as
• Resource locations
• Fault detection
• Security
• Electronic payment

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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.

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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.

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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.

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DTF

• Currently being built by NSFs 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.
• 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.

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Using the Grid

• Globus
• Condor
• Harness
• Legion
• IBP
• NetSolve
• Others

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Globus

• A collaboration of Argonne National Laboratorys
  Mathematics and Computer Science Division, the
  University of Southern Californias Information
  Sciences Institute, and the University of
  Chicago's Distributed Systems Laboratory.
• Started in 1996 and is gaining popularity year
  after year.
• 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
  resources, displays, special instruments and so
  forth.

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The Globus Toolkit

• The Globus Resource Allocation Manager (GRAM)
• Creates, monitors, and manages services.
• Maps requests to local schedulers and computers.
• The Grid Security Infrastructure (GSI)
• Provides authentication services.

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The Globus Toolkit

• The Monitoring and Discovery Service (MDS)
• Provides information about system status,
  including server configurations, network status,
  and locations of replicated datasets, etc.
• Nexus and globus_io
• provides communication services for heterogeneous
  environments.
• Global Access to Secondary Storage (GASS)
• Provides data movement and access mechanisms that
  enable remote programs to manipulate local data.
• Heartbeat Monitor (HBM)
• Used by both system administrators and ordinary
  users to detect failure of system components or
  processes.

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Condor

• The Condor project started in 1988 at the
  University of Wisconsin-Madison.
• The main goal is to develop tools to support High
  Throughput Computing on large collections of
  distributively owned computing resources.
• Runs on a cluster of workstations to glean wasted
  CPU cycles.
• A Condor pool consists of any number of
  machines, of possibly different architectures and
  operating systems, that are connected by a
  network.
• Condor pools can share resources by a feature of
  Condor called flocking.

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The Condor Pool Software

• Job management services
• Supports requests about the job queue .
• Puts a job on hold.
• Enables the submission of new jobs.
• Provides information about jobs that are already
  finished.
• A machine with job management installed is called
  a submit machine.

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The Condor Pool Software

• Resource management
• Keeps track of available machines.
• Performs resource allocation and scheduling.
• Machines with resource management installed are
  called execute machines.
• A machine could be a submit and an execute
  machine simultaneously.

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Condor-G

• A version of Condor that uses Globus to submit
  jobs to remote resources.
• Allows users to monitor jobs submitted through
  the Globus toolkit.
• Can be installed on a single machine. Thus no
  need to have a Condor pool installed.

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Legion

• An object-based metasystems software project
  designed at the University of Virginia to support
  millions of hosts and trillions of objects linked
  together with high-speed links.
• Allows groups of users to construct shared
  virtual work spaces, to collaborate research and
  exchange information.
• An open system designed to encourage third party
  development of new or updated applications,
  run-time library implementations, and core
  components.
• The key feature of Legion is its object-oriented
  approach.

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Harness

• A Heterogeneous Adaptable Reconfigurable
  Networked System
• A collaboration between Oak Ridge National Lab,
  the University of Tennessee, and Emory
  University.
• Conceived as a natural successor of the PVM
  project.

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Harness

• An experimental system based on a highly
  customizable, distributed virtual machine (DVM)
  that can run on anything from a Supercomputer to
  a PDA.
• Built on three key areas of research Parallel
  Plug-in Interface, Distributed Peer-to-Peer
  Control, and Multiple DVM Collaboration.

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IBP

• The Internet Backplane Protocol (IBP) is a
  middleware for managing and using remote storage.
  
• It was devised at the University of Tennessee to
  support Logistical Networking in large scale,
  distributed systems and applications.

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IBP

• Named because it was designed to enable
  applications to treat the Internet as if it were
  a processor backplane.
• On a processor backplane, the user has access to
  memory and peripherals, and can direct
  communication between them with DMA.

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IBP

• IBP gives the user access to remote storage and
  standard Internet resources (e.g. content servers
  implemented with standard sockets) and can direct
  communication between them with the IBP API.

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IBP

• By providing a uniform, application-independent
  interface to storage in the network, IBP makes it
  possible for applications of all kinds to use
  logistical networking to exploit data locality
  and more effectively manage buffer resources.

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NetSolve

• A client-server-agent model.
• Designed for solving complex scientific problems
  in a loosely-coupled heterogeneous environment.

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The NetSolve Agent

• A resource broker that represents the gateway
  to the NetSolve system
• Maintains an index of the available computational
  resources and their characteristics, in addition
  to usage statistics.

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The NetSolve Agent

• Accepts requests for computational services from
  the client API and dispatches them to the
  best-suited sever.
• Runs on Linux and UNIX.

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The NetSolve Client

• Provides access to remote resources through
  simple and intuitive APIs.
• Runs on a users local system.
• Contacts the NetSolve system through the agent,
  which in turn returns the server that can best
  service the request.
• Runs on Linux, UNIX, and Windows.

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The NetSolve Server

• The computational backbone of the system.
• A daemon process that awaits client requests.
• Runs on different platforms a single
  workstation, cluster of workstations, symmetric
  multiprocessors (SMPs), or massively parallel
  processors (MPPs).

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The NetSolve Server

• A key component of the server is the Problem
  Description File (PDF).
• With the PDF, routines local to a given server
  are made available to clients throughout the
  NetSolve system.

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The PDF Template

• PROBLEM Program Name
• LIB Supporting Library Information
• INPUT specifications
• OUTPUT specifications
• CODE

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Network Weather Service

• Supports grid technologies.
• Uses sensor processes to monitor cpu loads and
  network traffic.
• Uses statistical models on the collected data to
  generate a forecast of future behavior.
• NetSolve is currently integrating NWS into its
  agent.

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Gridware Collaboarations

• NetSolve is using Globus' "Heartbeat Monitor" to
  detect failed servers.
• A NetSolve client is now in testing that allows
  access to Globus.
• Legion has adopted NetSolves client-user
  interface to leverage its metacomputing
  resources.
• The NetSolve client uses Legions data-flow
  graphs to keep track of data dependencies.

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Gridware Collaboarations

• NetSolve can access Condor pools among its
  computational resources.
• IBP-enabled clients and servers allow NetSolve to
  allocate and schedule storage resources as part
  of its resource brokering. This improves fault
  tolerance.

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Ongoing Research

• Motivation
• Special Projects
• Ongoing work at Tennessee
• General Issues
• Open questions of interest to the entire research
  community

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Motivation

• Computer speed doubles every 18 months
• Network speed doubles every 9 months

Graph from Scientific American (Jan-2001) by Cleo
Vilett, source Vined Khoslan, Kleiner, Caufield
and Perkins
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Special Projects

• The SInRG Project.
• Grid Service Clusters (GSCs)
• Data Switches
• Incorporating Hardware Acceleration.
• Unbridled Parallelism
• SETI_at_home and Folding_at_home
• The Vertex Cover Solver
• Security.

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The SInRG Project
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The Grid Service Cluster

• The basic grid building block.
• Each GSC will use the same software
  infrastructure as is now being deployed on the
  national Grid, but tuned to take advantage of the
  highly structured and controlled design of the
  cluster.
• Some GSCs are general-purpose and some are
  special-purpose.

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The Grid Service Cluster
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An advanced data switch

• The components that make up a GSC must be able to
  access each other at very high speeds and with
  guaranteed Quality of Service (QoS).
• Links of at least1Gbps assure QoS in many
  circumstances simply by over provisioning.

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Computational Ecology GSC

• Collaboration between computer science and
  mathematical ecology.
• 8-processor Symmetric Multi-Processor (SMP).
• Initial in-core memory (RAM) is approximately 4
  gigabytes.
• Out-of-core data storage unit provides a minimum
  of 450 gigabytes.

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Medical Imaging GSC

• Collaboration between computer science and the
  medical school.
• High-end graphics workstations.
• Distinguished by the need to have these
  workstations attached as directly as possible to
  the switch to facilitate interactive manipulation
  of the reconstructed images.

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Molecular Design GSC

• Collaboration between computer science and
  chemical engineering.
• Data visualization laboratory
• 32 dual processors
• High performance switch

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Machine Design GSC

• Collaboration between computer science and
  electrical engineering.
• 12 Unix-based CAD workstations.
• 8 Linux boxes with Pilchard boards.
• Investigating the potential of reconfigurable
  computing in grid environments.

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Machine Design GSC
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Types of Hardware

• General purpose hardware can implement any
  function
• ASICs hardware that can implement only a
  specific application
• FPGAs reconfigurable hardware that can
  implement any function

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The FPGA

• FPGAs offer reprogrammability
• Allows optimal logic design of each function to
  be implemented
• Hardware implementations offer acceleration over
  software implementations which are run on general
  purpose processors

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The Pilchard Environment

• Developed at Chinese University in Hong Kong.
• Plugs into 133MHz RAM DIMM slot and is an example
  of programmable active memory.
• Pilchard is accessed through memory read/write
  operations.
• Higher bandwidth and lower latency than other
  environments.

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Objectives

• Evaluate utility of NetSolve gridware.
• Determine effectiveness of hardware acceleration
  in this environment.
• Provide an interface for the remote use of FPGAs.
• Allow users to experiment and gauge whether a
  given problem would benefit from hardware
  acceleration.

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Sample Implementations

• Fast Fourier Transform (FFT)
• Data Encryption Standard algorithm (DES)
• Image backprojection algorithm
• A variety of combinatorial algorithms

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Implementation Techniques

• Two types of functions are implemented
• Software version - runs on the PCs processor
• Hardware version - runs in the FPGA
• To implement the hardware version of the
  function, VHDL code is needed

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The Hardware Function

• Implemented in VHDL or some other hardware
  description language.
• The VHDL code is then mapped onto the FPGA
  (synthesis).
• CAD tools help make mapping decisions based on
  constraints such as chip area, I/O pin counts,
  routing resources and topologies, partitioning,
  resource usage minimization.

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The Hardware Function

• Result of synthesis is a configuration file (bit
  stream).
• This file defines how the FPGA is to be
  reprogrammed in order to implement the new
  desired functionality.
• To run, a copy of the configuration file must be
  loaded on the FPGA.

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Behind the Scenes
VHDL code
Configuration file
Software and Hardware functions
PDFs, Libraries
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Conclusions

• Hardware acceleration is offered to both local
  and remote users.
• Resources are available through an efficient and
  easy-to-use interface.
• A development environment is provided for
  devising and testing a wide variety of software,
  hardware and hybrid solutions.
• Unbridled parallelism
• Sometimes the overhead of gridware is unneeded
• Well known examples include SETI_at_home and
  Folding_at_home