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 Work FREERIDE-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 Iterator Derived from precursor system FREERIDE While( ) { forall( data instances d) { (I, d’) = process(d) R(I) = R(I) op d’ } ……. }

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

Computer Science and Engineering Evolution FREERIDE FREERIDE-G-ADR FREERIDE-G-SRBFREERIDE-G-GT Application Data ADR SRB Globus

FREERIDE-G System Architecture

Compute Node More compute nodes than data hosts Each node: 1.Registers IO (from index) 2.Connects to data host While (chunks to process) 1.Dispatch IO request(s) 2.Poll pending IO 3.Process retrieved chunks

FREERIDE-G in Action SRB Agent SRB Master MCAT Data Host I/O Registration Connection establishment While (more chunks to process) I/O request dispatched Pending I/O polled Retrieved data chunks analyzed 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 fibers Goals: 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%.

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

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

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

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

51 Workflow Composition System

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