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Grid Computing. Grid Computing With MPI Over Multiple Clusters. Presented by: Vasil Lalov James Murithi. Project Supervisor: Dr. Hassan Rajaei Dept. of Computer Science Bowling Green State University Bowling Green, OH. Presentation Overview. Introduction Clustering Concepts

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Grid Computing

Grid Computing With MPI Over Multiple Clusters

Presented by: Vasil Lalov

James Murithi

Project Supervisor: Dr. Hassan Rajaei

Dept. of Computer Science

Bowling Green State University

Bowling Green, OH


Presentation Overview

  • Introduction
  • Clustering Concepts
  • Grid Computing Concepts
  • Our Contribution
  • Demonstration
  • Q/A Time

Parallel Programming Concepts

Example of a typical computer program:







Parallel Programming Concepts

An Example of a primitive parallel program:











Clustering Concepts

Example of a small cluster:

Head Node

Network Switch

Compute Nodes


Grid Computing Concepts

Definition of a Grid:

  • 1998: “A computationalgrid is a hardware and software infrastructure that provides dependable, consistent, pervasive, and inexpensive access to high-end computational capabilities” - Carl Kesselman and Ian Foster
  • Grid computing is an emerging computing model that provides the ability to perform higher throughput computing by taking advantage of many networked computers to model a virtual computer architecture that is able to distribute process execution across a parallel infrastructure - from Wikipedia

Ian Foster

categories of grids
Categories of Grids
  • Computational grids (CPU scavenging) – monitor the network for idle resources and use these for high performance computing
  • Data grids – is a grid computing system that deals with data, the controlled sharing and management of large amounts of distributed data.
  • Equipment grids – have a primary piece of equipment like a telescope which the grid gets data from and analyses.

Grid Computing Concepts

Key Elements of a Computational Grid:

  • Coordination of resources that are subject to decentralized control
    • Resources from different domains (VO, company, department)‏
    • Users from different domains
    • Resources are often geographically separated
  • Use of standard, open general-purpose protocols and interfaces
    • Authentication/authorization
    • Resource discovery/access
  • Delivers non-trivial quality of service
    • Utility of combined system >> sum of parts

Grid Computing Concepts

Grid Types:

  • Global Grid
    • Includes resources located in multiple countries around the world
    • Used for solving problems of global importance
    • Rarely used for time sensitive applications
  • National Grid – e.g. Terra Grid
    • Includes resources located with in the boundaries of a single country
    • Often used for governmental purposes
  • Mini Grid
    • Includes resources owned and managed by a single organization (company, university, etc.)‏
    • Primarily used for research and education purposes
    • True commercial use is still in its infancy

Grid Computing Concepts

Grid Organization of Resources:

Cluster 2


Cluster 1

Data Warehouse


Grid Resource Managers

Definition and Examples:

  • Definition - A software package that is responsible for:
    • Detecting and managing available resources on the grid
    • Collecting, distributing and managing jobs that use the grid resources
    • Providing a simple user interface for submitting jobs to the grid
    • Enforcing security policies for protecting resources, data and users on the grid
  • Popular Grid Resource Managers:
    • Globus ToolKit
    • Condor

Grid Resource Managers

Problems with Globus ToolKit:

  • Complex Installation and Configuration
  • To run parallel jobs, MPICH-G2 is required
    • Very difficult installation
    • Requires 2 IP addresses per compute node
    • Requires recompiling existing MPI based software
  • Current Source Code is broken
  • Runs on Java (slow, problematic)‏

Grid Resource Managers

Condor Grid Manager:

  • Requires only MPI 1.2.x:
    • No need for second NIC card and external IP addresses
    • No need for recompiling existing MPI based software
  • Extremely versatile and scalable:
    • Can manage very small and very large grids
    • Manages multiple types of resources
    • Automatically finds, configures and uses resources
    • Works with many types of job schedulers (PBS, SGE, etc)
  • Easy to use once installed and configured
  • Standalone Application (faster)
  • Huge community support
  • Current version is 6.8.6

Grid Resource Managers

Details Condor Grid Manager:

  • Condor Universes – a universe is a run time environment
    • Standard – The standard universe allows a job running under Condor to handle system calls by returning them to the machine where the job was submitted
    • Vanilla – provides a way to run jobs that cannot be relinked, these jobs cannot be relocated, for batch ready jobs
    • MPI – Obsolete universe
    • Parallel – Parallel jobs including MPI
  • What is Condor good/used for?
    • “Hunting” for available resources
    • Maximizing the Grid throughput
    • Background Jobs (BOINC)‏
    • Interfacing with other job managers (Globus, SGE)‏


  • Grid Monitoring of Resources
  • Condor Job Submission Scripts
  • Condor Job Submission Process

Future Work

  • Improve on the current Condor Configuration on Protos Cluster
  • Research on interoperability of Globus and Condor
  • Install and configure Condor on BWP4 Cluster
  • Test the mini-grid
  • Scale up the current platform

In Conclusion

  • Grid computing is exponentially more complex than cluster computing
  • Grids are usually designed for wide range of applications
  • Execution of MPI jobs in Grid environment requires additional setup
  • Overall, Grids are more reliable than clusters but not as consistent

Q/A Time