Download ipdps04 - Computer Science and Engineering

Computer Science and Engineering
Scaling and Parallelizing a Scientific Feature
Mining Application Using a Cluster
Middleware.
Leonid Glimcher
Xuan Zhang
Gagan Agrawal
ipdps’04
Parallelizing Feature Mining Using FREERIDE
Leonid Glimcher
P. 1
Computer Science and Engineering
Presentation Road Map
• Motivation.
• Description of middleware and functionality.
• Description of sequential vortex detection
algorithm.
• Parallelization challenges and solution
• Experimental results.
• Conclusions and future work.
ipdps’04
Parallelizing Feature Mining Using FREERIDE
Leonid Glimcher
P. 2
Computer Science and Engineering
Motivation for Middleware.
• Problem:
– Data is growing in size exponentially
– Extracting knowledge out of data is
increasingly difficult.
• Solution:
– Parallelizing data mining algorithms to make
extracting knowledge out of data more
efficient.
• But developing parallel datamining applications
is no routine task (tedious and time consuming).
ipdps’04
Parallelizing Feature Mining Using FREERIDE
Leonid Glimcher
P. 3
Computer Science and Engineering
FREERIDE
• Framework for Rapid
Implementation of
Datamining Engines
(created by Jin-Agrawal et
al.)
• Distributed and shared
memory parallelization
functionality.
• Support for efficient
processing of diskresident datasets.
• Based on a key
observation…
ipdps’04
While( ) {
forall( data instances d) {
I = process(d)
R(I) = R(I) op d
}
…….
}
Parallelizing Feature Mining Using FREERIDE
Leonid Glimcher
P. 4
Computer Science and Engineering
Parallelization in FREERIDE
Distributed Memory Setting:
– Data is divided b/w
processors
– Reduction object is
replicated
– Each node performs
local reduction on its
data
– Master node performs
global reduction
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Shared Memory Setting:
– Different data items are
assigned to different
threads
– Synchronization
techniques are used to
avoid race conditions
in accessing the
reduction object.
– Synchronization
involves replication
and/or locking.
Parallelizing Feature Mining Using FREERIDE
Leonid Glimcher
P. 5
Computer Science and Engineering
Application Specific Functionality
• To be specified by the developer using this
interface:
– Subset of data to be processed
– Local reductions
– Global reductions
– Iterator
• In addition application specific reduction object
needs to be defined
ipdps’04
Parallelizing Feature Mining Using FREERIDE
Leonid Glimcher
P. 6
Computer Science and Engineering
Previously on FREERIDE
• FREERIDE has been used for efficient shared and
distributed memory parallelization of:
– Decision tree construction,
– Apriori and FP-tree frequent item set mining,
– K-nearest neighbor classification,
– K-means clustering,
– EM clustering
• Conclusion: FREERIDE can be used to efficiently
and quickly parallelize well known data mining
algorithms
ipdps’04
Parallelizing Feature Mining Using FREERIDE
Leonid Glimcher
P. 7
Computer Science and Engineering
Vortex Detection
• Sequential version was
implemented by Machiraju
et al.
• Classify-aggregate
paradigm:
– Detection,
– Binary Classification,
– Aggregation,
– De-noising,
– Ranking
ipdps’04
Parallelizing Feature Mining Using FREERIDE
Leonid Glimcher
P. 8
Computer Science and Engineering
Mapping vortex detection to FREERIDE
•
•
•
Detection and classification are performed as a part of local
processing
Aggregation is performed as a combination of local processing
and node processing and global combination steps.
De-noising and ranking are performed in the post processing
step.
Detect
Classify
Local processing
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Denoise
Aggregate
Node
processing
Global
Combine
Rank
Post-processing
Parallelizing Feature Mining Using FREERIDE
Leonid Glimcher
P. 9
Computer Science and Engineering
Vortex detection challenges
• Challenge:
– Classification of
boundary points
requires
communication.
– Multi-step aggregation
is complex and
requires special data
structures for
efficiency
ipdps’04
• Solution:
– Replication of
boundary points for
every chunk.
– Saving “face imprints”
for every incomplete
core region.
Parallelizing Feature Mining Using FREERIDE
Leonid Glimcher
P. 10
Computer Science and Engineering
Partitioning and boundary replication.
ipdps’04
Parallelizing Feature Mining Using FREERIDE
Leonid Glimcher
P. 11
Computer Science and Engineering
Experimental Results
• Experimental results for
up to 8 and 16 nodes
• Experimental Platform:
– Cluster (1-16) of 700
MHz Pentium machines
– Connected through
Myrinet LANai 7.0
– 1 GB memory each
node
– Datasets ranging in
size from 30 MB to 1.8
GB.
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400
350
300
260-com
250
200
260nocom
30-com
150
100
50
0
1 Node
8
Nodes
Parallelizing Feature Mining Using FREERIDE
Leonid Glimcher
P. 12
Computer Science and Engineering
Experimental Results (Cont’d.)
• Scalability confirmed by
tests (up to 16 nodes)
• Partitioning overhead:
more chunks means less
of a speedup, when
compared to sequential
application
• Parallelization overhead is
high for smaller datasets,
but shrinks for larger data.
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3000
2500
2000
1850nocom
710-com
1500
1000
710nocom
500
0
1 Node
8
Nodes
Parallelizing Feature Mining Using FREERIDE
Leonid Glimcher
P. 13
Computer Science and Engineering
Absolute Speedups (710 MB)
500
450
400
350
300
250
200
150
100
50
0
1 Node
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Parallel,
1 chunk
Parallel,
8
chunks
• If the data is partitioned
into a larger number of
chunks, the overhead will
grow.
• Speedups are sub-linear,
when based on the 1
chunk – 1 node
configuration, but such
configuration doesn’t
support parallel execution.
4
Nodes
Parallelizing Feature Mining Using FREERIDE
Leonid Glimcher
P. 14
Computer Science and Engineering
Conclusions & Future Work
•
•
Currently: working on parallelizing a feature mining application
detecting molecular defects in crystalline grids created by physics
and material science simulations.
Conclusions:
– FREERIDE can be used to implement a variety of data and
scientific mining algorithms, creating scalable parallel
implementations
– Such parallelization can be performed more easily than
“hand-coding” a parallel application
– There’s an overhead that’s incurred with increasing
granularity, but parallelization overhead is usually quite small
– If the number of chunks remains constant, speedups are
linear, proving communication or I/O overheads very small
– Parallel applications created using FREERIDE allow working
efficiently with disk-resident datasets
ipdps’04
Parallelizing Feature Mining Using FREERIDE
Leonid Glimcher
P. 15
Computer Science and Engineering
Questions?
ipdps’04
Parallelizing Feature Mining Using FREERIDE
Leonid Glimcher
P. 16