Grid Computing
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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

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


Grid computing

Presentation Overview

  • Introduction

  • Clustering Concepts

  • Grid Computing Concepts

  • Our Contribution

  • Demonstration

  • Q/A Time


Grid computing

Parallel Programming Concepts

Example of a typical computer program:

Application

Application

Process

Data

Results


Grid computing

Parallel Programming Concepts

An Example of a primitive parallel program:

Application

Application

MasterProcess

Data

Processor

Processor

Data

MasterProcess

Results


Grid computing

Clustering Concepts

Example of a small cluster:

Head Node

Network Switch

Compute Nodes


Grid computing

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

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

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

Grid Computing Concepts

Grid Organization of Resources:

Cluster 2

VirtualOrganization

Cluster 1

Data Warehouse


Grid computing

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 computing

MPI Compilers for Grid Computing


Grid computing

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 computing

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 computing

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 computing

Demonstration

  • Grid Monitoring of Resources

  • Condor Job Submission Scripts

  • Condor Job Submission Process


Grid computing

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


Grid computing

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


Grid computing

Q/A Time

Thanks

Questions?


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