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![Page 1: Computer Science and Engineering Predicting Performance for Grid-Based Dataminingglimcher@cse.ohio-state.edu P. 1 IPDPS’07 A Performance Prediction Framework.](https://reader036.fdocuments.net/reader036/viewer/2022081519/56649f135503460f94c27458/html5/thumbnails/1.jpg)
Computer Science and Engineering
IPDPS’07 Predicting Performance for Grid-Based Datamining
A Performance Prediction Framework for Grid-Based Data Mining Applications
Leonid Glimcher
Gagan Agrawal
![Page 2: Computer Science and Engineering Predicting Performance for Grid-Based Dataminingglimcher@cse.ohio-state.edu P. 1 IPDPS’07 A Performance Prediction Framework.](https://reader036.fdocuments.net/reader036/viewer/2022081519/56649f135503460f94c27458/html5/thumbnails/2.jpg)
Computer Science and Engineering
IPDPS’07 Predicting Performance for Grid-Based Datamining
Motivating Scenario
Data Repository Clusters
Compute Clusters
User?
3 stages:•Disk i/o,•Network,•Compute.
![Page 3: Computer Science and Engineering Predicting Performance for Grid-Based Dataminingglimcher@cse.ohio-state.edu P. 1 IPDPS’07 A Performance Prediction Framework.](https://reader036.fdocuments.net/reader036/viewer/2022081519/56649f135503460f94c27458/html5/thumbnails/3.jpg)
Computer Science and Engineering
IPDPS’07 Predicting Performance for Grid-Based Datamining
Remote Data Analysis
• Remote data analysis– Grid is a good fit– Details can be very tedious
• Middleware abstracts away lots of development details
• Resource selection – crucial to performance• Performance prediction facilitates resource
selection
![Page 4: Computer Science and Engineering Predicting Performance for Grid-Based Dataminingglimcher@cse.ohio-state.edu P. 1 IPDPS’07 A Performance Prediction Framework.](https://reader036.fdocuments.net/reader036/viewer/2022081519/56649f135503460f94c27458/html5/thumbnails/4.jpg)
Computer Science and Engineering
IPDPS’07 Predicting Performance for Grid-Based Datamining
Presentation Road Map
• Problem statement and motivation• Middleware background• Our performance prediction approach• Experimental evaluation• Related work• Conclusions
![Page 5: Computer Science and Engineering Predicting Performance for Grid-Based Dataminingglimcher@cse.ohio-state.edu P. 1 IPDPS’07 A Performance Prediction Framework.](https://reader036.fdocuments.net/reader036/viewer/2022081519/56649f135503460f94c27458/html5/thumbnails/5.jpg)
Computer Science and Engineering
IPDPS’07 Predicting Performance for Grid-Based Datamining
Problem Statement
Given: Parallel data processing application Execution time break-down (profile) Configurations of available computing resources Dataset replicas in different size repositories
Predict application execution time in order to select right dataset replica and resource configuration
![Page 6: Computer Science and Engineering Predicting Performance for Grid-Based Dataminingglimcher@cse.ohio-state.edu P. 1 IPDPS’07 A Performance Prediction Framework.](https://reader036.fdocuments.net/reader036/viewer/2022081519/56649f135503460f94c27458/html5/thumbnails/6.jpg)
Computer Science and Engineering
IPDPS’07 Predicting Performance for Grid-Based Datamining
FREERIDE-G Design
![Page 7: Computer Science and Engineering Predicting Performance for Grid-Based Dataminingglimcher@cse.ohio-state.edu P. 1 IPDPS’07 A Performance Prediction Framework.](https://reader036.fdocuments.net/reader036/viewer/2022081519/56649f135503460f94c27458/html5/thumbnails/7.jpg)
Computer Science and Engineering
IPDPS’07 Predicting Performance for Grid-Based Datamining
FREERIDE-G Processing
KEY observation: most data mining algorithms follow canonical loop
Middleware API: • Subset of data to be
processed• Reduction object • Local and global reduction
operations • Iterator
While( ) {
forall( data instances d) {
I = process(d)
R(I) = R(I) op d
}
…….
}
![Page 8: Computer Science and Engineering Predicting Performance for Grid-Based Dataminingglimcher@cse.ohio-state.edu P. 1 IPDPS’07 A Performance Prediction Framework.](https://reader036.fdocuments.net/reader036/viewer/2022081519/56649f135503460f94c27458/html5/thumbnails/8.jpg)
Computer Science and Engineering
IPDPS’07 Predicting Performance for Grid-Based Datamining
Performance Prediction Approach
• 3 Phases of execution:– Retrieval at data server– Data delivery to compute node– Parallel processing at compute node
• Special processing structure:– Generalized reduction
Texec = Tdisk + Tnetwork + Tcompute
![Page 9: Computer Science and Engineering Predicting Performance for Grid-Based Dataminingglimcher@cse.ohio-state.edu P. 1 IPDPS’07 A Performance Prediction Framework.](https://reader036.fdocuments.net/reader036/viewer/2022081519/56649f135503460f94c27458/html5/thumbnails/9.jpg)
Computer Science and Engineering
IPDPS’07 Predicting Performance for Grid-Based Datamining
Needed profile information
Numbers of storage nodes (n) compute nodes (c)
Available bandwidth between these (b), in profile configuration
Execution time breakdown: data retrieval (td)
network communication (tn)
data processing (tc) components
Dataset size (s)
Reduction object information: maximum size communication time
Global reduction time
![Page 10: Computer Science and Engineering Predicting Performance for Grid-Based Dataminingglimcher@cse.ohio-state.edu P. 1 IPDPS’07 A Performance Prediction Framework.](https://reader036.fdocuments.net/reader036/viewer/2022081519/56649f135503460f94c27458/html5/thumbnails/10.jpg)
Computer Science and Engineering
IPDPS’07 Predicting Performance for Grid-Based Datamining
Data Retrieval and Communication Time
Data Retrieval:
Dataset size (s) and number of data hosts (n) for base profile and predicted configuration (s’ and n’).
Used to scale td.
Data Communication:
Also need dataset size and number of data hosts, as well as bandwidth (b and b’).
Used to scale tn.
tT nnetwtork b
b
n
n
s
s
''
'
![Page 11: Computer Science and Engineering Predicting Performance for Grid-Based Dataminingglimcher@cse.ohio-state.edu P. 1 IPDPS’07 A Performance Prediction Framework.](https://reader036.fdocuments.net/reader036/viewer/2022081519/56649f135503460f94c27458/html5/thumbnails/11.jpg)
Computer Science and Engineering
IPDPS’07 Predicting Performance for Grid-Based Datamining
Initial Data Processing Time Prediction
Dataset size (s) and number of compute nodes (c):
• base profile (s,c) • predicted profile (s’, c’)
Used to scale up tc.
Limitations – not modeling:• Inter-processor
communication time• Global reduction time
ccompute tc
c
s
sT
'
'
![Page 12: Computer Science and Engineering Predicting Performance for Grid-Based Dataminingglimcher@cse.ohio-state.edu P. 1 IPDPS’07 A Performance Prediction Framework.](https://reader036.fdocuments.net/reader036/viewer/2022081519/56649f135503460f94c27458/html5/thumbnails/12.jpg)
Computer Science and Engineering
IPDPS’07 Predicting Performance for Grid-Based Datamining
Modeling Interprocessor Communication
• Parallel computation involves communication of reduction object
• Communication time (Tro)• Reduction object size (r)• Interprocessor bandwidth (w)• Latency (l)• Reduction object size either
remains constant or scales linearly Tt roc
T '
lrwT ro
^
''
'TT rocompute
Tc
c
s
s
ccompute tc
c
s
sT
'
'
![Page 13: Computer Science and Engineering Predicting Performance for Grid-Based Dataminingglimcher@cse.ohio-state.edu P. 1 IPDPS’07 A Performance Prediction Framework.](https://reader036.fdocuments.net/reader036/viewer/2022081519/56649f135503460f94c27458/html5/thumbnails/13.jpg)
Computer Science and Engineering
IPDPS’07 Predicting Performance for Grid-Based Datamining
Modeling Global Reduction
• Global reduction time (Tg) is also serialized
• Depending on application, global reduction time:
– Scales linearly with number of nodes but is constant independent of size
– Stays constant independent of number of nodes, but scales linearly with data size
TTt grocT "
^^
"'
'TTT grocompute
Tc
c
s
s
^
''
'TT rocompute
Tc
c
s
s
![Page 14: Computer Science and Engineering Predicting Performance for Grid-Based Dataminingglimcher@cse.ohio-state.edu P. 1 IPDPS’07 A Performance Prediction Framework.](https://reader036.fdocuments.net/reader036/viewer/2022081519/56649f135503460f94c27458/html5/thumbnails/14.jpg)
Computer Science and Engineering
IPDPS’07 Predicting Performance for Grid-Based Datamining
Modeling Across Heterogeneous Clusters
Need scaling factors for all 3 stages of computation (from a set of representative applications).
3/)(
3
3
2
2
1
1
TT
TT
TT
sdisk
disk
disk
disk
disk
disk
A
B
A
B
A
B
d
^^^^
TsTsTsT computenetworkdiskexec AcAnAdB
![Page 15: Computer Science and Engineering Predicting Performance for Grid-Based Dataminingglimcher@cse.ohio-state.edu P. 1 IPDPS’07 A Performance Prediction Framework.](https://reader036.fdocuments.net/reader036/viewer/2022081519/56649f135503460f94c27458/html5/thumbnails/15.jpg)
Computer Science and Engineering
IPDPS’07 Predicting Performance for Grid-Based Datamining
FREERIDE-G Applications
Data mining:• K-means clustering• KNN search• EM clustering
Scientific data processing:• Vortex extraction (right)• Molecular defect detection
and categorization
![Page 16: Computer Science and Engineering Predicting Performance for Grid-Based Dataminingglimcher@cse.ohio-state.edu P. 1 IPDPS’07 A Performance Prediction Framework.](https://reader036.fdocuments.net/reader036/viewer/2022081519/56649f135503460f94c27458/html5/thumbnails/16.jpg)
Computer Science and Engineering
IPDPS’07 Predicting Performance for Grid-Based Datamining
Experimental Setup
Base:700 MHz Pentiums connected through Myrinet LaNai 7.0
Heterogeneous prediction:2.4 GHz Opteron 250’s connected through Infiniband (1Gb)
Goal – to correctly model changes in:1. Parallel configuration2. Dataset size3. Network bandwidth4. Underlying resources
TTTexact
predictedexactError||
![Page 17: Computer Science and Engineering Predicting Performance for Grid-Based Dataminingglimcher@cse.ohio-state.edu P. 1 IPDPS’07 A Performance Prediction Framework.](https://reader036.fdocuments.net/reader036/viewer/2022081519/56649f135503460f94c27458/html5/thumbnails/17.jpg)
Computer Science and Engineering
IPDPS’07 Predicting Performance for Grid-Based Datamining
Modeling Parallel Performance
Errors for 3 approaches for:
1. Vortex detection, base:• 1-1 configuration• 710 MB dataset
2. Defect detection, base:• 1-1 configuration• 130 MB dataset
Results:• modeling reduction pays
off• accurate predictions
Vortex Detection (base: 1-1 configuration, 710MB dataset)
0.00%
0.50%
1.00%
1.50%
2.00%
2.50%
3.00%
3.50%
4.00%
4.50%
5.00%
1 cn 2 cn 4 cn 8 cn 16 cn 2 cn 4 cn 8 cn 16 cn 4 cn 8 cn 16 cn 8 cn 16 cn
1 2 4 8Number of data nodes
Re
lati
ve
pre
dic
tio
n e
rro
r %
no communicationreduction communicationglobal reduction
Molecular Defect Detection (base: 1-1 configuration, 130MB dataset)
0.00%
1.00%
2.00%
3.00%
4.00%
5.00%
6.00%
7.00%
8.00%
9.00%
10.00%
1 cn 2 cn 4 cn 8 cn 16 cn 2 cn 4 cn 8 cn 16 cn 4 cn 8 cn 16 cn 8 cn 16 cn
1 2 4 8Number of data nodes
Re
lati
ve
pre
dic
tio
n e
rro
r % no communication
reduction communicationglobal reduction
![Page 18: Computer Science and Engineering Predicting Performance for Grid-Based Dataminingglimcher@cse.ohio-state.edu P. 1 IPDPS’07 A Performance Prediction Framework.](https://reader036.fdocuments.net/reader036/viewer/2022081519/56649f135503460f94c27458/html5/thumbnails/18.jpg)
Computer Science and Engineering
IPDPS’07 Predicting Performance for Grid-Based Datamining
Modeling Dataset SizeEM clustering (base: 1-1 configuration/350 MB, predicted: 1.4 GB dataset)
0.00%
1.00%
2.00%
3.00%
1 cn 2 cn 4 cn 8 cn 16 cn 2 cn 4 cn 8 cn 16 cn 4 cn 8 cn 16 cn 8 cn 16 cn
1 2 4 8Number of data nodes
Re
lati
ve
pre
dic
tio
n e
rro
r %
global reduction
Molecular Defect Detection (base: 1-1 configuration/130MB dataset; predicting: 1.8 GB dataset)
0.00%
1.00%
2.00%
3.00%
4.00%
5.00%
6.00%
1 cn 2 cn 4 cn 8 cn 16 cn 2 cn 4 cn 8 cn 16 cn 4 cn 8 cn 16 cn 8 cn 16 cn
1 2 4 8Number of data nodes
Re
lati
ve
pre
dic
tio
n e
rro
r %
global reduction
Errors for 1 (best) approach for:1. EM clustering (1.4 GB) , base:
• 1-1 configuration• 350 MB dataset
2. Defect detection (1.8 GB), base:• 1-1 configuration• 130 MB dataset
Results:• biggest error when number of
data nodes is same as number of compute nodes
• accurate predictions
![Page 19: Computer Science and Engineering Predicting Performance for Grid-Based Dataminingglimcher@cse.ohio-state.edu P. 1 IPDPS’07 A Performance Prediction Framework.](https://reader036.fdocuments.net/reader036/viewer/2022081519/56649f135503460f94c27458/html5/thumbnails/19.jpg)
Computer Science and Engineering
IPDPS’07 Predicting Performance for Grid-Based Datamining
Impact of Network Bandwidth
Errors for 1 (best) approach for:1. EM clustering (250 Kbps) ,
base:• 1-1 configuration• 500 Kbps
2. Defect detection (250 Kbps), base:• 1-1 configuration• 500 Kbps
Results:• biggest error when number of
data nodes is same as number of compute nodes
• Modeling reduction is most accurate
EM clustering (base: 4-4 configuration/1.4GB dataset; predicting: 130 MB dataset)
0.00%
0.50%
1.00%
1.50%
2.00%
1 cn 2 cn 4 cn 8 cn 16 cn 2 cn 4 cn 8 cn 16 cn 4 cn 8 cn 16 cn 8 cn 16 cn
1 2 4 8Number of data nodes
Re
lati
ve
pre
dic
tio
n e
rro
r %
global reduction
Molecular Defect Detection (base: 4-4 configuration/1.8 GB dataset; predicting: 350 dataset)
0.00%
0.50%
1.00%
1.50%
1 cn 2 cn 4 cn 8 cn 16 cn 2 cn 4 cn 8 cn 16 cn 4 cn 8 cn 16 cn 8 cn 16 cn
1 2 4 8Number of data nodes
Re
lati
ve
pre
dic
tio
n e
rro
r %
global reduction
![Page 20: Computer Science and Engineering Predicting Performance for Grid-Based Dataminingglimcher@cse.ohio-state.edu P. 1 IPDPS’07 A Performance Prediction Framework.](https://reader036.fdocuments.net/reader036/viewer/2022081519/56649f135503460f94c27458/html5/thumbnails/20.jpg)
Computer Science and Engineering
IPDPS’07 Predicting Performance for Grid-Based Datamining
Predictions for different type of cluster
Errors for 1 (best) approach for:1. Defect detection (1.8 GB) ,
base:• 1-1 configuration• 710 MB dataset
2. EM clustering (700 MB), base:• 8-8 configuration• 350 MB dataset
Results:• Scaling factors different• Largest error when predicted
configuration has same number of compute nodes as base
Molecular Defect Detection (base: 4-4 configuration, 130MB dataset;prediction: 1.8 GB dataset)
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
1 cn 2 cn 4 cn 8 cn 16 cn 2 cn 4 cn 8 cn 16 cn 4 cn 8 cn 16 cn 8 cn 16 cn
1 2 4 8Number of data nodes
Re
lati
ve
pre
dic
tio
n e
rro
r %
global reduction
EM clustering (base: 8-8 configuration, 350 MB dataset; prediction: 700 MB dataset)
0.00%
1.00%
2.00%
3.00%
4.00%
5.00%
6.00%
7.00%
8.00%
9.00%
10.00%
1 cn 2 cn 4 cn 8 cn 16 cn 2 cn 4 cn 8 cn 16 cn 4 cn 8 cn 16 cn 8 cn 16 cn
1 2 4 8Number of data nodes
Re
lati
ve
pre
dic
tio
n e
rro
r %
global reduction
![Page 21: Computer Science and Engineering Predicting Performance for Grid-Based Dataminingglimcher@cse.ohio-state.edu P. 1 IPDPS’07 A Performance Prediction Framework.](https://reader036.fdocuments.net/reader036/viewer/2022081519/56649f135503460f94c27458/html5/thumbnails/21.jpg)
Computer Science and Engineering
IPDPS’07 Predicting Performance for Grid-Based Datamining
Existing Work
3 broad categories for resource allocation: Heuristic approach to mapping Prediction through modeling:
Statistical estimation/predictionAnalytical modeling of parallel
application Simulation based performance prediction
![Page 22: Computer Science and Engineering Predicting Performance for Grid-Based Dataminingglimcher@cse.ohio-state.edu P. 1 IPDPS’07 A Performance Prediction Framework.](https://reader036.fdocuments.net/reader036/viewer/2022081519/56649f135503460f94c27458/html5/thumbnails/22.jpg)
Computer Science and Engineering
IPDPS’07 Predicting Performance for Grid-Based Datamining
Summary
• Performance prediction approach • Exploits similarities in application processing
structure to come up with very accurate results• Approach accurately models changes in:
– Computing configuration– Dataset size– Network bandwidth– Underlying compute resources