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SALSA SALSA High Performance Biomedical Applications Using Cloud Technologies HPC and Grid Computing in the Cloud Workshop (OGF27 ) October 13, 2009, Banff Canada Judy Qiu [email protected] www.infomall.org/s a lsa Community Grids Laboratory Pervasive Technology Institute Indiana University

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High Performance Biomedical Applications Using Cloud Technologies . Judy Qiu [email protected] www.infomall.org/s a lsa Community Grids Laboratory Pervasive Technology Institute Indiana University. HPC and Grid Computing in the Cloud Workshop (OGF27 ) October 13, 2009, Banff Canada. - PowerPoint PPT Presentation

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Page 1: High  Performance Biomedical Applications Using  Cloud Technologies

SALSASALSA

High Performance Biomedical Applications Using Cloud Technologies

HPC and Grid Computing in the Cloud Workshop (OGF27 )October 13, 2009, Banff Canada

Judy [email protected] www.infomall.org/salsa

Community Grids LaboratoryPervasive Technology Institute

Indiana University

Page 2: High  Performance Biomedical Applications Using  Cloud Technologies

SALSA

Collaborators in SALSA Project

Indiana UniversitySALSA Technology Team

Geoffrey Fox Judy QiuScott BeasonJaliya Ekanayake Thilina GunarathneThilina Gunarathne

Jong Youl ChoiYang RuanSeung-Hee BaeHui LiSaliya Ekanayake

Microsoft ResearchTechnology Collaboration

Azure (Clouds)Dennis GannonDryad (Parallel Runtime)Roger BargaChristophe Poulain CCR (Threading)George ChrysanthakopoulosDSS (Services)Henrik Frystyk Nielsen

Applications

Bioinformatics, CGB Haixu Tang, Mina Rho, Peter Cherbas, Qunfeng DongIU Medical School Gilbert LiuDemographics (Polis Center) Neil DevadasanCheminformatics David Wild, Qian ZhuPhysics CMS group at Caltech (Julian Bunn)

Community Grids Laband UITS RT – PTI

Page 3: High  Performance Biomedical Applications Using  Cloud Technologies

SALSA

Bio-Computing a Major Focus of 22nd Annual SC Conference

• “Biological research today is driven by the acceleration of knowledge creation, explosion in data around the world, and growing interdependence of disciplines. New HPC solutions allow for far more comprehensive approaches to scientific investigation and enable a systems approach to understanding and predicting life, which is fundamental to the global challenges in medicine, energy and defense.”

Peg Folta, head of the SC09 Bio-Computing Thrust Area

• “Our discussion at SC09 will explore the possibility of on-demand access to computing resources that democratize access to the diverse, rapidly expanding and distributed data generated in biology, along with sharing information about our planned Systems Biology Knowledgebase.”

Susan Gregurick, DOE Program Manager

• “Bio-Computing and computationally intense applications in genomics and sequencing represent a tremendous growth area for HPC technologies, and an emerging area of interest for a large amount of HPC professionals. “

Chris Heier, president of Tycrid Platform Technology

Page 4: High  Performance Biomedical Applications Using  Cloud Technologies

SALSA

Data Intensive (Science) Applications

Bare metal (Computer, network, storage)

FutureGrid/VM

Cloud Technologies(MapReduce, Dryad, Hadoop)

Classic HPCMPI, Threading

Applications Biology: Expressed Sequence Tag (EST) sequence assembly (CAP3) Biology: Pairwise Alu sequence alignment (SW) Health: Correlating childhood obesity with environmental factors Cheminformatics: Mapping PubChem data into low dimensions to aid drug discovery

Data mining AlgorithmClustering (Pairwise , Vector)MDS, GTM, PCA, CCA

VisualizationPlotViz

Page 5: High  Performance Biomedical Applications Using  Cloud Technologies

SALSA

FutureGrid Architecture

Page 6: High  Performance Biomedical Applications Using  Cloud Technologies

SALSA

Cloud Computing: Infrastructure and Runtimes

• Cloud infrastructure: outsourcing of servers, computing, data, file space, etc.– Handled through Web services that control virtual machine

lifecycles.• Cloud runtimes: tools (for using clouds) to do data-parallel

computations. – Apache Hadoop, Google MapReduce, Microsoft Dryad, and others – Designed for information retrieval but are excellent for a wide

range of science data analysis applications– Can also do much traditional parallel computing for data-mining if

extended to support iterative operations– Not usually on Virtual Machines

Page 7: High  Performance Biomedical Applications Using  Cloud Technologies

SALSA

Data Intensive Architecture

Prepare for Viz

MDS

InitialProcessing

Instruments

User Data

Users

Database

Database

Database

Database

Files

Files

Database

Database

Database

Database

Files

Files

Database

Database

Database

Database

Files

Files

Higher LevelProcessingSuch as R

PCA, ClusteringCorrelations …

Maybe MPI

VisualizationUser PortalKnowledgeDiscovery

Page 8: High  Performance Biomedical Applications Using  Cloud Technologies

SALSA

MapReduce “File/Data Repository” Parallelism

Instruments

Disks

Computers/Disks

Map1 Map2 Map3 Reduce

Communication via Messages/Files

Map = (data parallel) computation reading and writing dataReduce = Collective/Consolidation phase e.g. forming multiple global sums as in histogram

Portals/Users

Page 9: High  Performance Biomedical Applications Using  Cloud Technologies

SALSA

Cluster ConfigurationsFeature GCB-K18 @ MSR iDataplex @ IU Tempest @ IUCPU Intel Xeon

CPU L5420 2.50GHz

Intel Xeon CPU L5420 2.50GHz

Intel Xeon CPU E7450 2.40GHz

# CPU /# Cores per node

2 / 8 2 / 8 4 / 24

Memory 16 GB 32GB 48GB

# Disks 2 1 2

Network Giga bit Ethernet Giga bit Ethernet Giga bit Ethernet /20 Gbps Infiniband

Operating System Windows Server Enterprise - 64 bit

Red Hat Enterprise Linux Server -64 bit

Windows Server Enterprise - 64 bit

# Nodes Used 32 32 32

Total CPU Cores Used 256 256 768

DryadLINQ Hadoop/ Dryad / MPI DryadLINQ / MPI

Page 10: High  Performance Biomedical Applications Using  Cloud Technologies

SALSA

Alu Sequencing Workflow

• Data is a collection of N sequences – 100’s of characters long– These cannot be thought of as vectors because there are missing characters– “Multiple Sequence Alignment” (creating vectors of characters) doesn’t seem

to work if N larger than O(100)• First calculate N2 dissimilarities (distances) between sequences (all pairs)• Find families by clustering (much better methods than Kmeans). As no vectors, use

vector free O(N2) methods• Map to 3D for visualization using Multidimensional Scaling MDS – also O(N2)• N = 50,000 runs in 10 hours (all above) on 768 cores• Our collaborators just gave us 170,000 sequences and want to look at 1.5 million –

will develop new algorithms!• MapReduce++ will do all steps as MDS, Clustering just need MPI Broadcast/Reduce

Page 11: High  Performance Biomedical Applications Using  Cloud Technologies

SALSA

Gene Family from Alu Sequencing

• Calculate pairwise distances for a collection of genes (used for clustering, MDS)

• O(N^2) problem • “Doubly Data Parallel” at Dryad Stage• Performance close to MPI• Performed on 768 cores (Tempest Cluster)

35339 500000

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

DryadLINQMPI

1250 million distances4 hours & 46 minutes

Processes work better than threads when used inside vertices 100% utilization vs. 70%

Page 12: High  Performance Biomedical Applications Using  Cloud Technologies

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Alu Sequencing WorkflowData is a collection of N sequences – 100’s of characters long

These cannot be thought of as vectors because there are missing characters

“Multiple Sequence Alignment” (creating vectors of characters) doesn’t seem to work if N larger than O(100)

Can calculate N2 dissimilarities (distances) between sequences (all pairs)

Find families by clustering (much better methods than Kmeans). As no vectors, use vector free O(N2) methods

Page 13: High  Performance Biomedical Applications Using  Cloud Technologies

SALSA

Page 14: High  Performance Biomedical Applications Using  Cloud Technologies

SALSA

1 2 4 4 4 8 8 8 8 8 8 8 16 16 16 16 16 24 32 32 48 48 48 48 48 64 64 64 64 96 96128

128192

288384

384480

576672

744

-1

0

1

2

3

4

5

6

MPIMPI

MPI

Parallel Overhead

ThreadThread

Thread

Parallelism

Clustering by Deterministic Annealing

ThreadThread

Thread

MPI

Thread

Pairwise Clustering30,000 Points on Tempest

Page 15: High  Performance Biomedical Applications Using  Cloud Technologies

SALSA

Dryad versus MPI for Smith Waterman

0

1

2

3

4

5

6

7

0 10000 20000 30000 40000 50000 60000

Tim

e pe

r dis

tanc

e ca

lcul

ation

per

core

(m

ilise

cond

s)

Sequeneces

Performance of Dryad vs. MPI of SW-Gotoh Alignment

Dryad (replicated data)

Block scattered MPI (replicated data)Dryad (raw data)

Space filling curve MPI (raw data)Space filling curve MPI (replicated data)

Flat is perfect scaling

Page 16: High  Performance Biomedical Applications Using  Cloud Technologies

SALSA

Dryad Scaling on Smith Waterman

0

1

2

3

4

5

6

7

288 336 384 432 480 528 576 624 672 720

Tim

e pe

r dis

tanc

e ca

lcul

ation

per

core

(m

illis

econ

ds)

Cores

DryadLINQ Scaling Test on SW-G Alignment

Flat is perfect scaling

Page 17: High  Performance Biomedical Applications Using  Cloud Technologies

SALSA

Dryad for Inhomogeneous Data

Flat is perfect scaling – measured on Tempest

1100

1150

1200

1250

1300

1350

0 50 100 150 200 250 300 350

Tim

e (s

)

Standard Deviation of sequence lengths

Tim

e (m

s)

Sequence Length Standard Deviation

Mean Length 400

Page 18: High  Performance Biomedical Applications Using  Cloud Technologies

SALSA

Hadoop/Dryad ComparisonInhomogeneous Data

0 50 100 150 200 250 300 3501200

1300

1400

1500

1600

1700

1800Time

Sequence Length Standard Deviation

Mean Length 400

Hadoop

Dryad

Dryad with Windows HPCS compared to Hadoop with Linux RHEL on IDataplex

Page 19: High  Performance Biomedical Applications Using  Cloud Technologies

SALSA

Hadoop/Dryad Comparison“Homogeneous” Data

Dryad with Windows HPCS compared to Hadoop with Linux RHEL on IdataplexUsing real data with standard deviation/length = 0.1

30000 35000 40000 45000 50000 550000

0.002

0.004

0.006

0.008

0.01

0.012

Number of Sequences

Tim

e pe

r Alig

nmen

t (m

s)

Dryad

Hadoop

Page 20: High  Performance Biomedical Applications Using  Cloud Technologies

SALSA

DryadLINQ on Cloud

• HPC release of DryadLINQ requires Windows Server 2008• Amazon does not provide this VM yet• Used GoGrid cloud provider• Before Running Applications

– Create VM image with necessary software• E.g. NET framework

– Deploy a collection of images (one by one – a feature of GoGrid)– Configure IP addresses (requires login to individual nodes)– Configure an HPC cluster– Install DryadLINQ– Copying data from “cloud storage”

We configured a 32 node virtual cluster in GoGrid

Page 21: High  Performance Biomedical Applications Using  Cloud Technologies

SALSA

DryadLINQ on Cloud contd..

• CloudBurst and Kmeans did not run on cloud• VMs were crashing/freezing even at data partitioning

– Communication and data accessing simply freeze VMs– VMs become unreachable

• We expect some communication overhead, but the above observations are more GoGrid related than to Cloud

• CAP3 works on cloud• Used 32 CPU cores • 100% utilization of virtual

CPU cores• 3 times more time in cloud

than the bare-metal runs on different

• FutureGrid would give us much better results

Page 22: High  Performance Biomedical Applications Using  Cloud Technologies

SALSA

MPI on Clouds Kmeans Clustering

• Perform Kmeans clustering for up to 40 million 3D data points• Amount of communication depends only on the number of cluster centers• Amount of communication << Computation and the amount of data processed• At the highest granularity VMs show at least 3.5 times overhead compared to

bare-metal• Extremely large overheads for smaller grain sizes

Performance – 128 CPU cores Overhead

Page 23: High  Performance Biomedical Applications Using  Cloud Technologies

SALSA

Application Classes • Application—parallel software/hardware in terms of 5 “Application

Architecture” Structures– 1) Synchronous – Lockstep Operation as in SIMD architectures– 2) Loosely Synchronous – Iterative Compute-Communication stages with independent compute

(map) operations for each CPU. Heart of most MPI jobs– 3) Asynchronous – Compute Chess; Combinatorial Search often supported by dynamic threads– 4) Pleasingly Parallel – Each component independent – in 1988, Fox estimated at 20% of total

number of applications– 5) Metaproblems – Coarse grain (asynchronous) combinations of classes 1)-4). The preserve of

workflow.

• Grids greatly increased work in classes 4) and 5)• Previous parallel computing work largely described simulations and not data

processing. Now we should admit the class which crosses classes 2) 4) 5) above– 6) MapReduce++ which describe file(database) to file(database) operations– 6a) Pleasing Parallel Map Only (cap3, HEP)– 6b) Map followed by reductions (SWG)– 6c) Iterative “Map followed by reductions” – Extension of Current Technologies that supports

much linear algebra and datamining (pairwise, MDS)

• Note overheads in 1) 2) 6c) go like Communication Time/Calculation Time and basic MapReduce pays file read/write costs while MPI is microseconds

Page 24: High  Performance Biomedical Applications Using  Cloud Technologies

SALSA

Applications & Different Interconnection PatternsMap Only Classic

MapReduceIterative Reductions Loosely

Synchronous

CAP3 AnalysisDocument conversion (PDF -> HTML)Brute force searches in cryptographyParametric sweeps

High Energy Physics (HEP) HistogramsDistributed searchDistributed sortingInformation retrieval

Expectation maximization algorithmsClusteringLinear Algebra

Many MPI scientific applications utilizing wide variety of communication constructs including local interactions

- CAP3 Gene Assembly- PolarGrid Matlab data analysis

- Information Retrieval - HEP Data Analysis- Calculation of Pairwise Distances for ALU Sequences

- Kmeans - Deterministic Annealing Clustering- Multidimensional Scaling MDS

- Solving Differential Equations and - particle dynamics with short range forces

Input

Output

map

Inputmap

reduce

Inputmap

reduce

iterations

Pij

Domain of MapReduce and Iterative Extensions MPI

Page 25: High  Performance Biomedical Applications Using  Cloud Technologies

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Summary: Key Features of our Approach

• Cloud technologies work very well for data intensive applications • Iterative MapReduce allows to build a complete system with single cloud technology

without MPI • FutureGrid allows easy Windows v Linux with and without VM comparison• Intend to implement range of biology applications with Dryad/Hadoop• Initially we will make key capabilities available as services that we eventually

implement on virtual clusters (clouds) to address very large problems– Basic Pairwise dissimilarity calculations– R (done already by us and others)– MDS in various forms– Vector and Pairwise Deterministic annealing clustering

• Point viewer (Plotviz) either as download (to Windows!) or as a Web service• Note much of our code written in C# (high performance managed code) and runs on

Microsoft HPCS 2008 (with Dryad extensions)– Hadoop code written in Java

Page 26: High  Performance Biomedical Applications Using  Cloud Technologies

SALSA

Project website

www.infomall.org/SALSA