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

Computing and Deep Web

Gagan Agrawal

u

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

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 • Google’s Map-Reduce has created excitement

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

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

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

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

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

FREERIDE - Motivation

• Availability of very large datasets and it’s analysis (Data-intensive applications)

• Adaptation of Multi-core and inevitability of parallel programming

• Need for abstraction of difficulties of parallel programming.

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

Generalized Reduction structure

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)

Memory Layout of SMP techs

Experimental setup

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

Experimental Results – K-means (CMP)

K-means (cluster)

Apriori (CMP)

Apriori (cluster)

E-M (CMP)

E-M (cluster)

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

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

Architecture of GeForce 8800 GPU (1 multiprocessor)

Challenges of Data-intensive Computing on GPU

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

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• ……

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)

Architecture of the middleware

Variable information

Reduction functions

Optional functions Code

Analyzer( In LLVM)

Variable Analyzer

Code Generator

Variable Access Pattern and Combination Operations

Host Program

Grid configuration and kernel invocation

Kernel functions

Executable

User Input

A sequential reduction function

Optional functions (initialization function, combination function…)

Values of each variable (typically specified as length of arrays)

Variables to be used in the reduction function

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

Experiment Results

Speedup of k-means

Speedup of EM

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

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 ?

Computer Science and Engineering

Our WorkFREERIDE-G (Framework for Rapid Implementation of Datamining

Engines in Grid) Enable Development of Flexible and Scalable Remote Data Processing Applications

Repository cluster

Compute cluster

Middleware user

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

Computer Science and Engineering

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 • IteratorDerived from precursor system

FREERIDE

While( ) {

forall( data instances d) {

(I , d’) = process(d)

R(I) = R(I) op d’

}

…….

}

FREERIDE-G Evolution

FREERIDEdata stored locally

FREERIDE-G• ADR responsible for remote data retrieval• SRB responsible for remote data retrievalFREERIDE-G grid serviceGrid service featuring• Load balancing• Data integration

Computer Science and Engineering

EvolutionFREERIDE FREERIDE-G-ADR

FREERIDE-G-SRB FREERIDE-G-GT

ApplicationDataADRSRBGlobus

FREERIDE-G System Architecture

Compute Node

More compute nodes than data hosts

Each node:1. Registers IO (from index)2. Connects to data hostWhile (chunks to process)3. Dispatch IO request(s)4. Poll pending IO5. Process retrieved chunks

FREERIDE-G in Action

SRB Agent

SRB Agent

SRB MasterMCAT

Data Host I/O RegistrationConnection establishment

While (more chunks to process)

I/O request dispatchedPending I/O polled

Retrieved data chunksanalyzed

Compute Node

Compute Node

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

Experimental setup

Organizational Grid:• Data hosted on Opteron 250 cluster• Processed on Opteron 254 cluster• Connected using 2 10 GB optical fibersGoals:• Demonstrate parallel scalability of applications• Evaluate overhead of using MPICH-G2 and

Globus Toolkit deployment mechanisms

Computer Science and Engineering

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%.

020

040

060

0

Exec

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n Ti

me

(sec

)4 8

Data Repository Nodes (#)compute - GT 4comute - no GT 4compute - GT 8compute - no GT 8

wangfa@cse.ohio-state.edu

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

wangfa@cse.ohio-state.edu

Motivating Example

ERCC6

dbSNP

Entrez Gene

SequenceDatabase

AlignmentDatabase

AA Positions for Nonsynonymous SNP

Encoded Protein

Encoded Orthologous Protein

Protein Sequence

Given a gene ERCC6, we want to know the amino acid occurring in the corresponding position in orthologous gene of non-human mammals

wangfa@cse.ohio-state.edu

Observations

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

wangfa@cse.ohio-state.edu

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

49

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

50

HASTE Middleware Design

ICAC 2008

Application Layer

Service Layer

OGSA Infrastructure (Globus Toolkit 4.0)

Application Deployment Service

AUTONOMIC SERVICECOMPONENTS

App.Service 1

Agent/Controller

...

...App.

Service 3

Agent/Controller

App.Service 4

Agent/Controller

App.Service 5

Agent/ControllerApp.

Service 2

Agent/Controller

Application

Code ConfigurationFile

BenefitFunction

Time-CriticalEvent

Resource Allocation Service

Resource Monitoring Service

CPU Memory Bandwidth

SchedulingEfficiency

ValueEstimation

Autonomic Adaptation ServiceSystemModel Estimator

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