CENTRE FOR PARALLEL COMPUTING
8th IDGF Workshop
Hannover, August 17
th
2011
International Desktop
Grid Federation
CENTRE FOR PARALLEL COMPUTING
Experiences with the
University of Westminster
Desktop Grid
S C Winter, T Kiss, G Terstyanszky, D Farkas,
T Delaitre
CENTRE FOR PARALLEL COMPUTING
Contents
•
Introduction to Westminster Local
Desktop Grid (WLDG)
–
Architecture, deployment management
–
EDGeS Application Development
Methodology (EADM)
•
Application examples
•
Conclusions
CENTRE FOR PARALLEL COMPUTING
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
CENTRE FOR PARALLEL COMPUTING
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)
CENTRE FOR PARALLEL COMPUTING
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
CENTRE FOR PARALLEL COMPUTING
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
CENTRE FOR PARALLEL COMPUTING
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
CENTRE FOR PARALLEL COMPUTING
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
CENTRE FOR PARALLEL COMPUTING
Application Examples
•
Digital Alias
-
Free Signal Processing
•
AutoDock Molecular Modelling
CENTRE FOR PARALLEL COMPUTING
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
CENTRE FOR PARALLEL COMPUTING
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
CENTRE FOR PARALLEL COMPUTING
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
CENTRE FOR PARALLEL COMPUTING
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
CENTRE FOR PARALLEL COMPUTING
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
CENTRE FOR PARALLEL COMPUTING
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
CENTRE FOR PARALLEL COMPUTING
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
CENTRE FOR PARALLEL COMPUTING
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
CENTRE FOR PARALLEL COMPUTING
AutoDock
–
Performance test
results
Speedup
0
20
40
60
80
100
120
140
160
180
200
10
100
1000
3000
# of work units
Speedup
CENTRE FOR PARALLEL COMPUTING
DG Drawbacks: The “Tail”
Problem
Jobs >> Nodes
Jobs ≈ Nodes
CENTRE FOR PARALLEL COMPUTING
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
CENTRE FOR PARALLEL COMPUTING
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
CENTRE FOR PARALLEL COMPUTING
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
CENTRE FOR PARALLEL COMPUTING
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
CENTRE FOR PARALLEL COMPUTING
The End
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