1. L. Glimcher, R. Jin, G. Agrawal Presented by: Leo Glimcher
FREERIDE-G: Framework for Developing Grid-Based Data Mining Applications
L. Glimcher, R. Jin, G. Agrawal
Presented by: Leo Glimcher
ICPP’06

2. Distributed Data-Intensive Science
Compute Cluster ?
User
Data Repository Cluster
ICPP’06

3. Challenges for Application Development
Analysis of large amounts of disk resident data
Incorporating parallel processing into analysis
Processing needs to be independent of other elements and easy to specify
Coordination of storage, network and computing resources required
Transparency of data retrieval, staging and caching is desired
ICPP’06

4. FREERIDE-G Goals Support High-End Processing
Enable efficient processing of large scale data mining computations
Ease Use of Parallel Configurations
Support shared and distributed memory parallelization starting from a common high-level interface
Hide Details of Data Movement and Caching
Data staging and caching (when feasible/appropriate) needs to be transparent to application developer
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5. System architecture and overview Applications used for evaluation
Presentation Road Map
Motivation and goals
System architecture and overview
Applications used for evaluation
Experimental evaluation
Related work in distributed data-intensive science
Conclusions and future work
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6. FREERIDE-G Architecture
User cluster
Data Repository
Data Processing
Data Retrieval
Caching Retrieval
Communication
Data Distribution
Compute Nodes
Communication
Data server
Data Processing
Caching Retrieval
Communication
Data server
Data Processing
Caching Retrieval
Data Retrieval
Communication
Data Distribution
Compute Nodes
Communication
Data Processing
Caching Retrieval
Communication
ICPP’06

7. Data Server Functionality
Data retrieval:
data chunks read from repository disks
Data distribution:
each chunk assigned a processing node destination in user cluster
Data communication:
each chunk forwarded to destination processing node
Data server runs on every on-line data repository node, automating data delivery to the end-user
ICPP’06

8. Compute Node Functionality
Data communication:
data chunks received from corresponding data node
Computation:
application specific processing performed on each chunk
Data caching & retrieval:
for multi-pass algorithms data cached locally on 1st pass and retrieved locally for sub-sequent passes
Compute server runs on every processing node to receive data and process it in an application specific way
ICPP’06

9. Processing structure of FREERIDE-G
Built on FREERIDE
KEY observation: most algorithms follow canonical loop
Middleware API:
Subset of data to be processed
Reduction object
Local and global reduction operations
Iterator
Supports:
Disk resident datasets
Shared & Distributed Memory
While( ) {
forall( data instances d) {
I = process(d)
R(I) = R(I) op d }
…….
ICPP’06

10. Summary of implementation issues
Managing and communicating remote data:
2-way coordination required
Load distribution:
if compute cluster bigger than data cluster
Parallel processing on compute cluster:
FREERIDE-G supports generalized reductions
Caching:
benefits multi-pass algorithms
ICPP’06

11. Managing data communication:
Remote Data Issues
Managing data communication:
ADR library used for scheduling and performing data retrieval at repository site
communication timing coordinated between source and destination
Caching:
local file system used for caching
avoids redundant communication of data for (P-1)/P iterations
ICPP’06

12. Parallel data processing issues
Load distribution:
Needed when more compute nodes are available then data nodes
Hashing on unique chunk ID
Parallel processing on compute cluster:
After data is distributed, local reduction performed on every node
Reduction object gathered at Master node
Global combination (reduction) performed on Master node
ICPP’06

13. Application Summary Data Mining: K-Nearest Neighbor search
K-means clustering
EM clustering
Scientific Feature Mining:
Vortex detection in the fluid flow dataset
Molecular defect detection in the molecular dynamics dataset
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14. Goals for Experimental Evaluation
Evaluation parallel scalability of applications developed:
Numbers of data and compute nodes kept equal with variable parallel configurations
Evaluating scalability of compute nodes:
Number of compute nodes kept independent of number of data nodes
Evaluating benefits of caching:
Multi-pass algorithms evaluated
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15. Evaluating Overall Scalability
Cluster of 700 MHz Pentiums
Connected through Myrinet LANai 7.0 (no access to high bandwidth network)
Equal number of repository and compute nodes
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16. All 5 applications tested: High parallel efficiency
Overall Scalability
All 5 applications tested:
High parallel efficiency
Good scalability with respect to:
problem size
processing node number
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17. Evaluating Scalability of Compute Nodes
Compute cluster size is greater than data repository cluster size.
Applications (single pass only):
kNN search,
molecular defect detection,
vortex detection (next slide),
Parallel configurations:
Data nodes: 1 to 8
Compute nodes: 1 to 16.
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18. Compute Node Scalability
Only data processing work parallelized
Data retrieval and communication times not effected
Speedups are sub-linear
Better resource utilization leads to analysis time decrease
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19. Evaluating effects of caching
Network bandwidth simulated: 500 KB/sec
Caching vs. non-caching versions compared
Comparing data communication times (P passes):
factor of P decrease from caching
Caching benefit depends on:
application
network bandwidth
ICPP’06

20. Related Work Support for grid-based data mining:
Knowledge Grid toolset
Grid-Miner toolkit
Discovery Net layer
DataMiningGrid framework
No interface for easing parallelization and abstracting data movement
GRIST – support for astronomy related mining on the grid
Specific to the astronomical domain
FREERIDE-G is built directly on top of FREERIDE.
ICPP’06

21. FREERIDE-G supports remote data analysis from high-level interface
Conclusions
FREERIDE-G supports remote data analysis from high-level interface
Evaluated on variety of algorithms
Demonstrated scalability in terms of:
Even data-compute scale-up
Compute node scale-up (only processing time)
Multi-pass algorithms benefit from data caching
ICPP’06

22. Continuing Work on FREERIDE-G
High bandwidth network evaluation
Performance prediction based resource selection
Resource allocation
More sophisticated caching and data communication mechanisms (SRB)
Data format issues: wrapper integration
Higher-level front-end to further ease development of data analysis tools for the grid
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23. ICPP’06