Use Case Scenarios for Performance Control of Grid-based Metacomputing

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www.cs.man.ac.uk/cnc Use Case Scenarios for Performance Control of Grid- based Metacomputing John Gurd, Ken Mayes, Graham Riley 3rd Grid Performance Workshop, June 2005

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Use Case Scenarios for Performance Control of Grid-based Metacomputing. John Gurd, Ken Mayes, Graham Riley 3rd Grid Performance Workshop, June 2005. Overview. Preamble The case for Performance Control Context Malleable, component-based Grid applications - PowerPoint PPT Presentation

Transcript of Use Case Scenarios for Performance Control of Grid-based Metacomputing

Page 1: Use Case Scenarios for Performance Control of Grid-based Metacomputing

www.cs.man.ac.uk/cnc

Use Case Scenarios for Performance Control of Grid-based

Metacomputing

John Gurd, Ken Mayes, Graham Riley

3rd Grid Performance Workshop, June 2005

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Overview

Preamble• The case for Performance Control

Context• Malleable, component-based Grid applications

The PERCO (Performance Control) System• Design and implementation

Homogeneous Components• Simple performance control scenarios

More Complex Scenarios Conclusions

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Achieving Performance

Engineering for maximum performance:• coarse design, then fine tuning• requires high degree of repeatability• benefits from homogeneity, symmetry, etc.

Control to achieve (less than maximum) target:• use negative feedback control at run-time• necessary to approach dynamic

environment• helps to deal with heterogeneity

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How to Control Performance?

Requires (negative) feedback

• needs sensors, actuators and compensators• timers, control ‘handles’, predictive models

Whole system vs. piece-wise control• who is responsible for what?

Perception is that a hierarchy is needed• hence need hierarchical software structure

actuator

feedback function

error

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Controllable Components?

Several groups have suggested that control should be effected via a component-based software architecture• degenerates to singleton component• can reduce the complexity of control• can form a control hierarchy

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Overview of PERCO

Two-tier hierarchical performance control• CPS (Component Performance Steerer)

- one wrapped around each component- all attached to APS (see below)- maximises performance on deployed platform

• APS (Application Performance Steerer)- (re)deploys components on available resources- maximises performance on allocated platforms

Requires an external resource allocator (from which to obtain a set of resources in which to effect its deployments)

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Modus Operandi

Components progress via a sequence of progress points, at each of which a component calls out to its CPS for any component-specific performance control actions (local actuation; requires component to be malleable)

Certain progress points are also safe-points (i.e. the component is in a state that permits it to be redeployed) and, at these points, the CPS can call out to the APS for redeployment-based performance control actions (the APS means of actuation)

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Progress Points

Assume that the execution of components and application proceeds through phases, and that the phase boundaries are marked by progress points.

Can take decisions about performance and (possibly) actuate at the progress points

0 1 2 3 4 5 6 7

Ph 1 Ph 2 Ph 3 Ph 4 Ph 5 Ph 6 Ph 7

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Application vs. Component Progress Points

Application progress points need to be safe points

Application progress points

Component progress points

APS

CPS

Component

Time

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PERCO System Overview

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PERCO Infrastructure

Each component is attached to a local loader which is capable of moving the component safely around the distributed Grid hardware according to the APS commands

The local loaders act in concert with the APS to form a virtual loader layer for the application

Each CPS communicates with the local loader on behalf of its component

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PERCO System for 2 Components, C1 & C2

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Controllable Components?

Several groups have suggested that control should be effected via a component-based software architecture• degenerates to singleton component• can reduce the complexity of control• can form a control hierarchy

But where do the components come from?• a knotty problem (cf. RealityGrid LB3D)

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One Answer . . .

Homogeneous components• each component a copy of the same model• used e.g. for parameter search• e.g. LB3D from RealityGrid

Performance control scenarios• N instances of LB3D, finish as fast as

possible- equates to keeping them in (approximate)

timestep with each other (see next slides)

• execute N instances of LB3D at specified rates relative to one another- e.g. N=2, one instance executes twice as many

timesteps per unit of time as the other

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With No Control

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With Control Exerted

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Slightly More Complex Answer . . .

“Almost homogeneous” components• each component a copy of a similar model,

but ...• ... with different driving parameters

- e.g. LB3D with different resolutions

Performance control scenarios• TeraGyroid experiment (from RealityGrid;

conducted during SC’2003; see next slide)• IntBioSim “beading” method• Hurricane “tracking”

Embedded high resolution subdomains• when does extra resolution become new

physics?

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TeraGyroid Use Case Scenario

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Even More Complex Answer: Coupled Models

Many scientific modellers are finding a need to link together multiple models:• climate/envt. models (ocean + atmosphere + ...)• multi-scale phenomena (CFD + MD = HybridMD)• aircraft lightning strike (CEM + a/f structure)+ others, all needing high performance & ‘Grid’

The individual models seem to constitute ready-made components:• can these be used for performance control?

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Summary

We are investigating the practicalities of component-based performance control in Grid execution environments

A prototype performance control system is being developed and we have shown that it can be used to achieve a scientifically meaningful high-level performance objective

We are ready to apply it to realistic scientific coupled model applicationsK.R. Mayes, M. Luján, G.D. Riley, J. Chin, P.V. Coveney, J.R. Gurd, Towards performance control on the Grid, Philosophical Transactions of the Royal Society of London: Series A, to appear, August 2005.

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Related Projects at Manchester

FLUME - design of next generation Unified Model software• funded by The Met Office (led by Mick Carter)

RealityGrid – condensed matter modelling• EPSRC-funded e-Science (led by Peter Coveney at UCL)

SoftIAM - climate impact, integrated assessment modelling• funded by the Tyndall Centre (led by Rachel Warren)

IntBioSim – integrated biological simulation• BBSRC-funded e-Science (led by Mark Sansom at

Oxford)

GENIEfy – Earth system modelling• NERC-funded e-Science (led by Tim Lenton + Tyndall C)

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Weblinks

For more information check:

http://www.cs.man.ac.uk/cnc

http://www.realitygrid.org

http://www.intbiosim.org (under construction)