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Dv: A toolkit for building remote interactive visualization services

David O’Hallaron

School of Computer Science and

Department of Electrical and Computer Engineering

Carnegie Mellon University

July, 1999

Martin Aeschlimann, Julio Lopez

Peter Dinda, Bruce Lowekamp

www.cs.cmu.edu/~droh

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Jacobo Bielak and Omar Ghattas (CMU CE) David O’Hallaron (CMU CS and ECE)Jonathan Shewchuk (UC-Berkeley)

Steven Day (SDSU Geology)www.cs.cmu.edu/~quake

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Teora, Italy1980

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San Fernando Valley

x

epicenter lat. 34.32 long. -118.48

lat. 34.08 long. -118.75

lat. 34.38 long. -118.16

San Fernando Valley

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San Fernando Valley (top view)

Soft soil

Hard rock

xepicenter

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San Fernando Valley (side view)

Soft soil

Hard rock

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Initial node distribution

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Partitioned unstructured finite element mesh of San Fernando

element

nodes

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Communication graph

Vertices: processorsEdges: communications

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Archimedeswww.cs.cmu.edu/~quake

Triangle/Pyramid

Slice

Parcel

Author

C compiler

Runtime library

ProblemGeometry (.poly)

Finite elementalgorithm (.arch)

MVPRODUCT(A,x,w);DOTPRODUCT(x,w,xw);r = r/xw;

.pack

.node, .ele

.c

a.out.part

parallel system

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Visualization of 1994 Northridge aftershock:

Shock wave propagation path

Generated by Greg Foss, Pittsburgh Supercomputing Center

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Visualization of 1994 Northridge aftershock:

Behavior of waves within basin

Generated by Greg Foss, Pittsburgh Supercomputing Center

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Animations of 1994 Northridge aftershock and 1995 Kobe

mainshock

• Data produced by the Quake group at Carnegie Mellon University

• Images rendered by Greg Foss, Pittsburgh Supercomputing Center.

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Motivation for Dv• Quake datasets are too large to store and

manipulate locally.– 40 GB - 6 TB depending on degree of downsampling

– Common problem now because of advances in hardware, software, and simulation methodology.

• Current visualization approach– Make request to supercomputing center (PSC) graphics

department.

– Receive an MPEGs, JPEG and/or videotapes in a couple of weeks/months.

• Desired visualization approach– Provide a remote visualization service that will allow us to

visualize Quake datasets interactively and in collaboration with colleagues around the world.

– Useful for qualitative debugging, demos, and solid engineering results

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Internet service models

• Traditional lightweight service model– Small to moderate amount of computation to satisfy requests

» e.g., serving web pages, stock quotes, online trading, current search engines

• Proposed heavyweight service model– Massive amount of computation to satisfy requests

» e.g., scientific visualization, data mining, future search engines

– Approach: provide heavyweight services on a computational grid of hosts.

clientserver

request

response

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Heavyweight grid service model

Remote compute hosts(allocated once per service

by the service provider)

Local compute hosts(allocated once per session

by the service user)

Besteffort

Internet

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Challenges to providing heavyweight Internet services

on a computational grid

• Grid resources are limited– We must find an easy way to grid-enable existing packages.

• Grid resources are heterogeneous– Programs should be performance-portable (at load time) in the

presence of heterogeneous resources.

• Grid resources are dynamic– Programs should be performance-portable (at run time) in the face of

dynamic resources.

• Applications that provide heavyweight grid services must be resource-aware.

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Motivating application: Earthquake ground motion

visualization

remotedatabase interpolationinterpolation isosurface

extraction

isosurfaceextraction

scenesynthesis

scenesynthesis

localdisplay

andinput

renderingrenderingreadingreading

FEM solverengine

materialsdatabase

ROI resolution contours scene

vtk routines

Decreasing amount of data

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Approaches for providing remote viz services

• Do everything on the remote server– Pros: very simple to grid enable existing packages

– Cons: high latency, eliminates possibility of proxying and caching at local site, can overuse remote site, not appropriate for smaller datasets.

ModerateBandwidth

Link

Veryhigh-endremote server

Localmachine

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Approaches for providing remote viz services

• Do everything but the rendering on the remote server

– Pros: fairly simply to grid-enable existing packages, removes some load from the remote site.

– Cons: requires every local site to have good rendering power.

high-endremoteserver

Machine with good rendering

power

Moderatebandwidth

Link

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Approaches for providing remote viz services

Highbandwidth

link

high-endremoteserver

low-endlocal

PC or PDA

Moderatebandwidth

link

powerfullocalproxyserver

• Use a local proxy for the rendering– Pros: offloads work from the remote site, allows local sites

to contribute additional resources.

– Cons: local sites may not have sufficiently powerful proxy resources, application is more complex, requires high bandwidth between local and remote sites.

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Approaches for providing remote viz services

Ultrahigh Bandwidth

Link

low-endremoteserver

powerfullocal

server

• Do everything at the local site.– Pros: low latency, easy to grid-enable existing packages.– Cons: requires high-bandwidth link between sites, requires powerful compute

and graphics resources at the local site.

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Providing remote viz services

• Claim: static application partitions are not appropriate for heavyweight Internet services on computational grids

• Thus, the goal with Dv is to find some flexible framework that allows us to schedule and partition heavyweight services.

• The approach is based on the notion of an active frame.

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Active frames

Framedata

Activeframe

interpreter

Applicationlibrariese.g, vtk

Framedata

Frameprogram

Active Frame Server

Input Active Frame Output Active Frame

Host

Frameprogram

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Overview of a Dv visualization service

Dv Server

Remote DV Active Frame Servers Local DV Active Frame Servers

Responseframes

DvServer

DvServerResponse

frames

...

Request frames

Responseframes

Userinputs/Display

Responseframes

DvServer

LocalDv

client

Remotedatasets

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Grid-enabling vtk with Dv

reader

localDv

client

response frames (to other Dv servers)

[appl. data, scheduler, flowgraph,control ]

request frame[request server, scheduler, flowgraph, data reader ]

request server

remote machine

... local Dv

serverscheduler

result

...

local machine(Dv client)

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Scheduling Dv programs

• Scheduling at request frame creation time– all response frames use same schedule

– can be performance portable at load time

– can not be performance portable at run time

• Scheduling at response frame creation time– performance portable at load time and partially at run

time.

• Scheduling at response frame delivery time– can be performance portable at both load and run time.

– per-frame scheduling overhead a potential disadvantage.

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Current Dv Issues• Flowgraphs with multiple inputs/outputs

– currently only support chains

• Caching– Static data such as meshes needs to be cached on

intermediate servers.

• Scheduling interface– Must support a wide range of scheduling strategies, from

completely static (once per session) to completely dynamic (each time a frame is sent by each frame server)

• Network and host resource monitoring– network queries and topology discovery (Lowekamp,

O’Hallaron, Gross, HPDC99)

– host load prediction (Dinda and O’Hallaron, HPDC99)

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Issues (cont)

• Managing the Dv grid– Resource discovery

» where are the Dv servers?

» which of them are running?

– Resource allocation

» which Dv servers are available to use?

– Collective operations

» Broadcast?

» Global synchronization of servers

• Client model– one generic client that runs in a browser

– config files that personalize client interface for each new service (dataset)

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Related work

• Active messages (Berkeley)

• Active networks (MIT)

• Globus (Argonne and USC)

• Legion (UVA)

• Harness (ORNL)

• Cumulvs (ORNL)

• PVM and MPI (MSU, ORNL, Argonne)

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Conclusions

• Heavyweight Internet services on computational grids are emerging.

• Static partitioning is not appropriate for heavyweight grid services

• Actives frames provides a uniform framework for grid-enabling and partitioning heavyweight services such as remote visualization.

• Dv is a toolkit based on active frames that we have used to grid-enable vtk.

• Dv provides a flexible framework for experimenting with grid scheduling techniques.