Data-Intensive Computing: From Multi-Cores and GPGPUs to Cloud Computing and Deep Web

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Data-Intensive Computing From Multi-Cores and
GPGPUs to Cloud Computing and Deep Web

• Gagan Agrawal

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Data-Intensive Computing

• Simply put scalable analysis of large datasets
• How is it different from related to
• Databases
• Emphasis on processing of static datasets
• Data Mining
• Community focused more on algorithms, and not
  scalable implementations
• High Performance / Parallel Computing
• More focus on compute-intensive tasks, not I/O or
  large datasets
• Datacenters
• Use of large resources for hosting data, less on
  their use for processing

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Why Now ?

• Amount of data is increasing rapidly
• Cheap Storage
• Better connectivity, easy to move large datasets
  on web/grids
• Science shifting from compute-X to X-informatics
  
• Business intelligence and analysis
• Googles Map-Reduce has created excitement

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Architectural Context

• Processor architecture has gone through a major
  change
• No more scaling with clock speeds
• Parallelism multi-core / many-core is the trend
  
• Accelerators like GPGPUs have become effective
• More challenges for scaling any class of
  applications

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Grid/Cloud/Utility Computing

• Cloud computing is a major new trend in industry
  
• Data and computation in a Cloud of resources
• Pay for use model (like a utility)
• Has roots in many developments over the last
  decade
• Service-oriented computing, Software as a Service
  (SaaS)
• Grid computing use of wide-area resources

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My Research Group

• Data-intensive computing on emerging
  architectures
• Data-intensive computing in Cloud Model
• Data-integration and query processing deep web
  data
• Querying low-level datasets through automatic
  workflow composition
• Adaptive computation time as a constraint

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Personnel

• Current students
• 6 PhD students
• 2 MS thesis students
• Talking to several first year students
• Past students
• 7 PhDs completed between 2005 and 2008

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Outline

• FREERIDE Data-intensive Computing on Cluster of
  Multi-cores
• A system for exploiting GPGPUs for data-intensive
  computing
• FREERIDE-G Data-intensive computing on Cloud
  Environments
• Quick overview of three other projects

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FREERIDE - Motivation

• Availability of very large datasets and its
  analysis (Data-intensive applications)
• Adaptation of Multi-core and inevitability of
  parallel programming
• Need for abstraction of difficulties of parallel
  programming.

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FREERIDE

• A middle-ware for parallelizing Data-intensive
  applications
• Motivated by difficulties in implementing and
  performance tuning of Datamining applications
• Based on observation of similar generalized
  reduction among datamining, OLAP and other
  scientific applications

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Generalized Reduction structure
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SMP Techniques

• Full-replication(f-r) (obvious technique)
• Locking based techniques
• Full-locking (f-l)
• Optimized Full-locking(o-f-l)
• Fixed Locking(fi-l)
• Cache-sensitive locking( Hybrid of o-f-l fi-l)

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Memory Layout of SMP techs
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Experimental setup

• Intel Xeon E5345 CPU
• 2 Quad-core machine
• Each core 2.33GHz
• 6GB Main memory
• Nodes in cluster connected by Infiniband

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Experimental Results K-means (CMP)
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K-means (cluster)
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Apriori (CMP)
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Apriori (cluster)
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E-M (CMP)
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E-M (cluster)
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Summary of Results

• Both Full-replication and Cache-sensitive locking
  can outperform each other based on the nature of
  application
• Cache-sensitive locking seems to have high
  overhead when there is little computation between
  updates in ReductionObject
• MPI processes competes well with best of other
  two when run on smaller cores, but experiences
  communication overheads when run on larger number
  of cores

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Background GPU Computing

• Multi-core architectures are becoming more
  popular in high performance computing
• GPU is inexpensive and fast
• CUDA is a high level language that supports
  programming on GPU

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Architecture of GeForce 8800 GPU (1
multiprocessor)?
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Challenges of Data-intensive Computing on GPU

• SIMD shared memory programming
• 3 steps involved in the main loop
• Data read
• Computing update
• Writing update

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Complication of CUDA Programming

• User has to have thorough knowledge of the
  architecture of GPU and the programming model of
  CUDA
• Must specify the grid configuration
• Has to deal with the memory allocation and copy
• Need to know what data to be copied onto shared
  memory and how much shared memory to use

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Architecture of the Middleware

• User input
• Code analyzer
• Analysis of variables (variable type and size)
• Analysis of reduction functions (sequential code
  from the user)
• Code Generator ( generating CUDA code and C
  code invoking the kernel function)

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Architecture of the middleware
Variable Analyzer
Host Program
Variable information
Variable Access Pattern and Combination Operations
Kernel functions
Reduction functions
Code Generator
Grid configuration and kernel invocation
Optional functions
Code Analyzer( In LLVM)
Executable
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User Input
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Analysis of Sequential Code

• Get the information of access features of each
  variable
• Figure out the data to be replicated
• Get the operator for global combination
• Calculate the size of shared memory to use and
  which data to be copied to shared memory

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Experiment Results
Speedup of k-means
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Speedup of EM
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Emergence of Cloud and Utility Computing

• Group generating data
• use remote resources for storing data
• Already popular with SDSC/SRB
• Scientist interested in deriving results from
  data
• use distinct but remote resources for processing
  
• Remote Data Analysis Paradigm
• Data, Computation, and User at Different
  Locations
• Unaware of location of other

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Remote Data Analysis

• Advantages
• Flexible use of resources
• Do not overload data repository
• No unnecessary data movement
• Avoid caching process once data
• Challenge Tedious details
• Data retrieval and caching
• Use of parallel configurations
• Use of heterogeneous resources
• Performance Issues
• Can a Grid Middleware Ease Application
  Development for Remote Data Analysis and Yet
  Provide High Performance ?

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Our Work

• FREERIDE-G (Framework for Rapid Implementation of
  Datamining Engines in Grid)
• Enable Development of Flexible and Scalable
  Remote Data Processing Applications

Middleware user
Repository cluster
Compute cluster
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Challenges

• Support use of parallel configurations
• For hosting data and processing data
• Transparent data movement
• Integration with Grid/Web Standards
• Resource selection
• Computing resources
• Data replica
• Scheduling and Load Balancing
• Data Wrapping Issues

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FREERIDE (G) Processing Structure

• 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
• Derived from precursor system FREERIDE

While( ) forall( data instances d)
(I , d) process(d) R(I) R(I)
op d .
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FREERIDE-G Evolution

• FREERIDE
• data stored locally
• FREERIDE-G
• ADR responsible for remote data retrieval
• SRB responsible for remote data retrieval
• FREERIDE-G grid service
• Grid service featuring
• Load balancing
• Data integration

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Evolution
FREERIDE-G-ADR
FREERIDE
Application Data ADR SRB Globus
FREERIDE-G-SRB
FREERIDE-G-GT
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FREERIDE-G System Architecture
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Compute Node

• More compute nodes than data hosts
• Each node
• Registers IO (from index)
• Connects to data host
• While (chunks to process)
• Dispatch IO request(s)
• Poll pending IO
• Process retrieved chunks

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FREERIDE-G in Action
Compute Node
Data Host
I/O Registration
Connection establishment
SRB Agent
While (more chunks to process)
I/O request dispatched
Pending I/O polled
MCAT
SRB Master
Retrieved data chunks analyzed
SRB Agent
Compute Node
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Implementation Challenges

• Interaction with Code Repository
• Simplified Wrapper and Interface Generator
• XML descriptors of API functions
• Each API function wrapped in own class
• Integration with MPICH-G2
• Supports MPI
• Deployed through Globus components (GRAM)
• Hides potential heterogeneity in service startup
  and management

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Experimental setup

• Organizational Grid
• Data hosted on Opteron 250 cluster
• Processed on Opteron 254 cluster
• Connected using 2 10 GB optical fibers
• Goals
• Demonstrate parallel scalability of applications
• Evaluate overhead of using MPICH-G2 and Globus
  Toolkit deployment mechanisms

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Deployment Overhead Evaluation

• Clearly a small overhead associated with using
  Globus and MPICH-G2 for middleware deployment.
• Kmeans Clustering with 6.4 GB dataset 18-20.
• Vortex Detection with 14.8 GB dataset 17-20.

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Deep Web Data Integration

• The emerge of deep web
• Deep web is huge
• Different from surface web
• Challenges for integration
• Not accessible through search engines
• Inter-dependences among deep web sources

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Motivating Example
Given a gene ERCC6, we want to know the amino
acid occurring in the corresponding position in
orthologous gene of non-human mammals
dbSNP
ERCC6
AA Positions for Nonsynonymous SNP
Alignment Database
Encoded Protein
Entrez Gene
Protein Sequence
Sequence Database
Encoded Orthologous Protein
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Observations

• Inter-dependences between sources
• Time consuming if done manually
• Intelligent order of querying
• Implicit sub-goals in user query

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Contributions

• Formulate the query planning problem for deep web
  databases with dependences
• Propose a dynamic query planner
• Develop cost models and an approximate planning
  algorithm
• Integrate the algorithm with a deep web mining
  tool

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HASTE Middleware Design Goals

• To Enable the Time-critical Event Handling to
  Achieve the Maximum Benefit, While Satisfying the
  Time Constraint
• To be Compatible with Grid and Web Services
• To Enable Easy Deployment and Management with
  Minimum Human Intervention
• To be Used in a Heterogeneous Distributed
  Environment

ICAC 2008
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HASTE Middleware Design
ICAC 2008
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Workflow Composition System
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Summary

• Several projects cross cutting Parallel
  Computing, Distributed Computing and Database/
  Data mining
• Number of opportunities for MS thesis, MS
  project, and PhD students
• Relevant Courses
• CSE 621/721
• CSE 762
• CSE 671 / 674