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8th IDGF Workshop

Hannover, August 17
th

2011

International Desktop

Grid Federation


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Experiences with the

University of Westminster

Desktop Grid


S C Winter, T Kiss, G Terstyanszky, D Farkas,
T Delaitre


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Contents

•
Introduction to Westminster Local
Desktop Grid (WLDG)

–
Architecture, deployment management

–
EDGeS Application Development
Methodology (EADM)

•
Application examples

•
Conclusions

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Introduction to Westminster
Local Desktop Grid (WLDG)

1

2

3

4

5

6

New Cavendish
St

576

Marylebone Road

559

Regent
Street

395

Wells Street


31

Little
Titchfield St


66

Harrow Campus

254

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WLDG Environment

•
DG Server on private University network

•
Over 1500 client nodes on 6 different
campuses

•
Most machines are dual core, all running
Windows

•
Running SZTAKI Local Desktop Grid package

•
Based on student laboratory PC’s

–
If not used by student

switch to DG mode

–
If no more work from DG server


shutdown
(Green policy)



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The DG Scenario

BOINC Server
BOINC workers
UoW Local Desktop Grid
Create graph
and concrete
workflow,
and submit
to DG
End
-
user
gUSE WS P
-
GRADE portal
WS P
-
GRADE
DG Submitter
submits jobs and
retrieve results
via 3G Bridge
Workers:
Download
executable and input
files
Upload
: result
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WLDG: ZENworks deployment

•
BOINC clients installed automatically and maintained
by specifically developed Novell ZENworks objects

–
MSI file has been created to generate a ZENworks object
that installs the client software.

–
BOINC Client Install Shield Executable converted into an MSI
package (/a switch on the BOINC Client executable)

–
BOINC client is part of the generic image installed on all lab
PC’s throughout the University

–
Guaranteed that any newly purchased and installed PC
automatically becomes part of the WLDG

•
All clients registered under same user account

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EDGeS Application Development
Methodology (EADM)

•
Generic methodology for DG application porting

•
Motivation: Special focus required when
porting/developing an application to a SG/DG
platform

•
Defines how the recommended software tools, eg.
developed by EDGeS, can aid this process

•
Supports iterative methods:

–
well
-
defined stages suggest a logical order

–
but (since in most cases process is non
-
linear) allows
revisiting and revising results of previous stages, at any
point

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EADM
–

Defined Stages

1.
Analysis of current
application

2.
Requirements analysis

3.
Systems design

4.
Detailed design

5.
Implementation

6.
Testing

7.
Validation

8.
Deployment

9.
User support,
maintenance & feedback

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Application Examples

•
Digital Alias
-
Free Signal Processing

•
AutoDock Molecular Modelling

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Digital Alias
-
Free Signal
Processing (DASP)

•
Users:

Centre for Systems Analysis
–

University of Westminster

•
Traditional DSP
based on
Uniform sampling

–
Suffers from aliasing

•
Aim
: Digital Alias
-
free Signal Processing (DASP)

–
One solution is Periodic Non
-
uniform Sampling (PNS)

•
The DASP application designs PNS sequences

•
Selection of optimal sampling sequence is computationally expensive
process

–
A linear equation has to be solved and a large number of solutions
(~10
10
) compared.

•
The analyses of the solutions are independent from each other


suitable for DG parallelisation


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DASP
-

Parallelisation

Solve
p
r
q
r
q
r
r







)
1
2
(
1
2

Find best
Permutation for
solution 1, 1+m,
1+2m
…
Find best
Permutation for
solution 2, 2+m,
2+2m
…
Find best
Permutation for
solution m, 2m,
3m,
…
…
q
r
,
q
r+1
,
…
,
q
2r
-
1
q
r
,
q
r+1
,
…
,
q
2r
-
1
q
r
,
q
r+1
,
…
,
q
2r
-
1
Find globally best solution
Locally best
solution
Locally best
solution
Locally best
solution
Computer 1
Computer 2
Computer m
Solve
p
r
q
r
q
r
r







)
1
2
(
1
2

Find best
Permutation for
solution 1, 1+m,
1+2m
…
Find best
Permutation for
solution 2, 2+m,
2+2m
…
Find best
Permutation for
solution m, 2m,
3m,
…
…
q
r
,
q
r+1
,
…
,
q
2r
-
1
q
r
,
q
r+1
,
…
,
q
2r
-
1
q
r
,
q
r+1
,
…
,
q
2r
-
1
Find globally best solution
Locally best
solution
Locally best
solution
Locally best
solution
Computer 1
Computer 2
Computer m
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DASP
–

Performance test results

Period T
(factor)

Sequential

DG
worst

DG
median

DG best

# of
work
units

Speedup
(best
case)

# of
nodes
involved
(median)

18

13 min

9 min

7 min

4 min

50

3.3

59

20

2 hr

29 min

111
min

43 min

20 min

100

7.5

97

22

26 hr

40 min

5h
1min

3 hr

24 min

2 hr 31
min

723

11

179

24

~820 hr

n/a

n/a

17 hr

54 min

980

46

372

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DASP
–

Addressing the
performance issues

•
Inefficient load balancing

–
solutions

of
the equation should

be

grouped based on the
execution time required to analyse individual solutions

•
Inefficient work

unit
generation

–
some

of
the solutions should
be
divided into subtasks
(more
work units
)

–
Limits to the possible speed
-
up



•
User
-
community/application developers to
consider redesigning the algorithm

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AutoDock Molecular Modelling

•
Users:

•
Dept of Molecular & Applied Biosciences, UoW

•
AutoDock:

•
a suite of automated docking tools

•
designed to predict how small molecules, such as
substrates or drug candidates, bind to a receptor
of known 3D structure

•
application components:

–
AutoDock performs the docking of the ligand to a set of grids
describing the target protein

–
AutoGrid pre
-
calculates these grids

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Need for Parallelisation

•
One run of
AutoDock

finishes in a reasonable
time

on a single PC

•
However, thousands of scenarios have to be
simulated and analysed to get stable and
meaningful results.

–
AutoDock

has to be run multiple times with the same
input files but with random factors

–
Simulations runs are independent from each other
–

suitable for DG


•
AutoGrid

does not require Grid resources

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AutoDock component workflow

gpf

file

pdb

file (ligand)

pdb

file
(receptor)

prepare_ligand4.py

prepare_receptor4.py

pdbqt

file

pdbqt

file

AUTOGRID

AUTODOCK

map files

Pamela

dpf

file

AUTODOCK

AUTODOCK

AUTODOCK

AUTODOCK

dlg

files

SCRIPT1

SCRIPT2

best
dlg

files


pdb

file

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Computational workflow in

P
-
GRADE

receptor.pdb

ligand.pdb

Autogrid

executables,

Scripts (uploaded by the

developer , don’t change it)

gpf

descriptor file

dpf

descriptor file

output
pdb

file

Number of
work units

1.
The Generator job creates specified numbered of
AutoDock jobs.

2.
The AutoGrid job creates
pdbqt

files from the
pdb

files, runs the
autogrid

application and generates
the map files. Zips them into an archive file. This
archive will be the input of all AutoDock jobs.

3.
The AutoDock jobs are running on the Desktop Grid.
As output they provide
dlg

files.

4.
The Collector job collects the
dlg

files. Takes the
best results and concatenates them into a
pdb

file.

dlg

files

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AutoDock
–

Performance test
results

Speedup
0
20
40
60
80
100
120
140
160
180
200
10
100
1000
3000
# of work units
Speedup
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DG Drawbacks: The “Tail”
Problem

Jobs >> Nodes

Jobs ≈ Nodes

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Tackling the Tail Problem

•
Augment the DG infrastructure with
more reliable nodes, eg. service grid or
cloud resources

•
Redesign scheduler to detect tail and
resubmit tardy tasks to SG or cloud

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Cloudbursting: Indicative Results

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AutoDock
-

Conclusions

•
CygWin

on Windows implementation
inhibited performance

–
can be improved using (eg.)

•
DG to EGEE bridge

•
Cloudbursting

•
AutoDock

is black
-
box legacy application

–
source code not available

–
code
-
based improvement not possible


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Further Applications

•
Ultrasound Computer Tomography
-

Forschungszentrum

Karlsruhe


•
EMMIL
–

E
-
marketplace optimization
-

SZTAKI

•
Anti
-
Cancer Drug Research (CancerGrid)
-

SZTAKI

•
Integrator of Stochastic Differential Equations in Plasmas
-

BIFI

•
Distributed Audio Retrieval
-

Cardiff University

•
Cellular Automata based Laser Dynamics
-

University of
Sevilla

•
Radio Network Design
–

University of
Extramadura


•
An X
-
ray diffraction spectrum analysis
-

University of
Extramadura

•
DNA Sequence Comparison and Pattern Discovery
-

Erasmus
Medical
Center

•
PLINK
-

Analysis of genotype/phenotype data

-

Atos

Origin

•
3D video rendering
-

University of Westminster


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Conclusions
–

Performance Issues

•
Performance enhancements

–
accrue from cyclical enterprise level hardware
and software upgrades

•
Are countered by
performance
degradation

–
arising from shared nature of resources

•
Need to focus on
robust performance
measures

–
in face of random unpredictable run
-
time
behaviours

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Conclusions
–

Load Balancing
Strategies

•
Heterogranular workflows

–
Tasks can differ widely in size and run times

–
Performance prediction, based eg. on previous runs, can inform
mapping (up to a point) ..

–
.. but after this, may need to re
-
engineer code (white box only)

–
.. or consider offloading bottleneck tasks to reliable resources

•
Homogranular workflows

–
Classic example: parameter sweep problem

–
Fine grain problems (#Tasks >> #Nodes) help smooth out the overall
performance, but ..

–
.. tail problem can be significant (especially if #Tasks
≈

#Nodes)

–
Smart detection of delayed tasks coupled with speculative
duplication

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Conclusions
–

Deployment Issues

•
Integration within enterprise desktop management
environment has many advantages, eg.

–
PC’s and applications are continually upgraded

–
Hosts and licenses are “free” on the DG

•
… but, also some drawbacks:

–
No direct control

•
Typical environments can be slack and dirty

•
Corporate objectives can override DG service objectives

•
Examples: current UoW Win7 deployment, green agenda

–
Service relationship, based on trust

•
DG bugs can easily damage trust relationship, if not caught quickly

•
Example: recent GenWrapper bug

–
Non
-
dedicated resource

•
Must give way to priority users, eg. students




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The End

Any questions?