Achieving Application Performance on the Computational Grid

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Achieving Application Performanceon the
Computational Grid

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• Francine Berman

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The Computing Landscape
Wireless
MPPs
clusters
PCs
Workstations
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Computing Platforms

• Combining resources in the box
• focus is on new hardware, software support
• Combining resources as a virtual box
• focus is on software infrastructure

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The Computational Grid

• Computational Grid ensemble of distributed and
  heterogeneous resources
• Metaphor Electric Power Grid
• for users, power is ubiquitous
• you can plug in anywhere
• you dont need to know where the power is coming
  from

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Better Toast

• On the electric power grid, power is either
  adequate or its not
• On the computational grid, application
  performance depends on the underlying system
  state
• Major Grid research and development thrusts
• Building the Grid
• Achieving performance on the Grid

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Scheduling for Performance

• How much work should each processor be given?
• Jacobi2D AppLeS solves equations for Area

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Adaptive Scheduling can leverage deliverable
resource performance.

• Jacobi2D experiments

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AppLeS

• Joint project with Rich Wolski at U. Tenn

• AppLeS Application-Level Scheduler
• agent-based approach
• each application integrated with its own AppLeS
• each AppLeS develops and implements a custom
  application schedule

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A Bushel of AppLeS almost

• During the first phase of the project, weve
  focused on developing AppLeS applications
• Jacobi2D
• DOT
• SRB
• Simple SARA
• magnetohydrodynamics
• CompLib
• INS2D
• Tomography, ...
• Each of these AppLeS constitutes a point
  solution for application scheduling

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New Directions

• AppLeS Templates
• distributed data applications
• parameter sweeps
• master/slave applications
• data parallel stencil applications

Network Weather Service
dynamic benchmarking
suite selection
API
AppLeS Template Retargeting Engineering
Environment
API
API
Application Module
Performance Module
Scheduling Module
Deployment Module
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New Directions

• Expanding AppLeS target execution sites
• interactive clusters
• linux, NT
• Globus, Legion
• batch systems
• high-throughput clusters (Condor)
• all of the above

AppLeS
SCHED
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New Directions

• Real World Scheduling
• scheduling with
• partial information
• poor information
• dynamically changing information
• Multischeduling
• resource economies
• scheduling social structure

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The Brave New World

• Design, development, and execution of grid-aware
  applications

Grid Application Development System
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The AppLeS Project

• Thanks to NSF, NASA, NPACI, DARPA, DoD
• AppLeS Home Page
• http//www-cse.ucsd.edu/groups/hpcl/apples.htm
  l

• AppLeS Corps
• Fran Berman, UCSD
• Rich Wolski, U. Tenn
• Henri Casanova
• Walfredo Cirne
• Marcio Faerman
• Jaime Frey

• Jim Hayes
• Graziano Obertelli
• Gary Shao
• Shava Smallen
• Alan Su
• Dmitrii Zagorodnov