SALSASALSA

Using Cloud Technologies for Bioinformatics Applications

MTAGS Workshop SC09Portland Oregon November 16 2009

Judy Qiuxqiu@indiana.edu www.infomall.org/salsa

Community Grids Laboratory

Pervasive Technology Institute

Indiana University

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 GannonRoger BargaDryad (Parallel Runtime)Christophe 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

SALSA

Convergence is Happening

Multicore

Clouds

Data IntensiveParadigms

Data intensive application (three basic activities):capture, curation, and analysis (visualization)

Cloud infrastructure and runtime

Parallel threading and processes

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

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

SALSA

• Dynamic Virtual Cluster provisioning via XCAT• Supports both stateful and stateless OS images

iDataplex Bare-metal Nodes

Linux Bare-system

Linux Virtual Machines

Windows Server 2008 HPC

Bare-system Xen Virtualization

Microsoft DryadLINQ / MPIApache Hadoop / MapReduce++ / MPI

Smith Waterman Dissimilarities, CAP-3 Gene Assembly, PhyloD Using DryadLINQ, High Energy Physics, Clustering, Multidimensional Scaling,

Generative Topological Mapping

XCAT Infrastructure

Xen Virtualization

Applications

Runtimes

Infrastructure software

Hardware

Windows Server 2008 HPC

Dynamic Virtual Cluster Architecture

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

SALSA

Alu and 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)• 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• 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

SALSA

Pairwise Distances – ALU Sequences

• 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

125 million distances4 hours & 46

minutes

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

SALSA

SALSA

SALSAHierarchical Subclustering

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

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

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

SALSA

Hadoop/Dryad Comparison Inhomogeneous Data I

Dryad with Windows HPCS compared to Hadoop with Linux RHEL on Idataplex (32 nodes)

0 50 100 150 200 250 300150015501600165017001750180018501900

Randomly Distributed Inhomogeneous Data Mean: 400, Dataset Size: 10000

DryadLinq SWG Hadoop SWG Hadoop SWG on VM

Standard Deviation

Tim

e (s

)

Inhomogeneity of data does not have a significant effect when the sequence lengths are randomly distributed

SALSA

Hadoop/Dryad Comparison Inhomogeneous Data II

Dryad with Windows HPCS compared to Hadoop with Linux RHEL on Idataplex (32 nodes)

0 50 100 150 200 250 3000

1,000

2,000

3,000

4,000

5,000

6,000

Skewed Distributed Inhomogeneous dataMean: 400, Dataset Size: 10000

DryadLinq SWG Hadoop SWG Hadoop SWG on VMStandard Deviation

Tota

l Tim

e (s

)

This shows the natural load balancing of Hadoop MR dynamic task assignment using a global pipeline in contrast to the DryadLinq static assignment

SALSA

Hadoop VM Performance Degradation

• 15.3% Degradation at largest data set size

10000 20000 30000 40000 50000

-5%

0%

5%

10%

15%

20%

25%

30%

Perf. Degradation On VM (Hadoop)

No. of Sequences

Performance Degradation = (Tvm – Tbaremetal)/Tbaremetal

SALSA

PhyloD using Azure and DryadLINQ

• Derive associations between HLA alleles and HIV codons and between codons themselves

SALSA

Mapping of PhyloD to Azure

Help

Track Jobs

Submit Job

PhyloD (Phylogeny-Based Association Analysis)Welcome User

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Job Title:

Distribution:Partition Count:

FDR Method:

Include Targets as Predictors

Min. Null Count:

Min. Observation Count:

Browse…Select Tree File((((((((((((((((((((((((754:0.100769,557:0.073734):0.024153,(663:0.022593,475:0.034225):0.021583):0.021470,(564:0.017860,528:0.026359):0.014597):0.006955,((646:0.005174,337:0.005753):0.063339,(454:0.041017,293:0.139149):0.025256):0.020785):0.011426,(((712:0.012147,(170:0.034105,(((329:0.039189,275:0.021962):0.016105,(((((393:

0.015664,171:0.037004):0.005747,(207:0.014198,198:0.015145):0.038824):0.003974,688:0.057600)

Sample Tree File: Download

Browse…Select Predictor Filevar cid valAnHla 1 1AnHla 2 0AnHla 3 0AnHla 4 1

Sample Predictor File: Download

Browse…Select Target File

Sample Target File: Download

Submit

3

var cid valAnAA@APos 1 0AnAA@APos 2 0AnAA@APos 3 0AnAA@APos 4 1AnAA@APos 5 0

Use Sample Files

Client

Web Role

Tracking Tables

Work-Item Queue

Local Storage

Local Storage

Local Storage

Blob containers

Worker Roles

Local Storage

SALSA

• Efficiency vs. number of worker roles in PhyloD prototype run on Azure March CTP

• Number of active Azure workers during a run of PhyloD application

PhyloD Azure Performance

SALSA

Iterative Computations

K-means Matrix Multiplication

Performance of K-Means Parallel Overhead Matrix Multiplication

SALSA

Kmeans Clustering

• Iteratively refining operation• New maps/reducers/vertices in every iteration • File system based communication• Loop unrolling in DryadLINQ provide better performance• The overheads are extremely large compared to MPI• CGL-MapReduce is an example of MapReduce++ -- supports MapReduce

model with iteration (data stays in memory and communication via streams not files)

Time for 20 iterations

LargeOverheads

SALSA

MapReduce++ (CGL-MapReduce)

• Streaming based communication• Intermediate results are directly transferred from the map tasks to

the reduce tasks – eliminates local files• Cacheable map/reduce tasks - Static data remains in memory• Combine phase to combine reductions• User Program is the composer of MapReduce computations• Extends the MapReduce model to iterative computations

Data Split

D MRDriver

UserProgram

Pub/Sub Broker Network

D

File System

MR

MR

MR

MR

Worker Nodes

M

R

D

Map Worker

Reduce Worker

MRDeamon

Communication

SALSA

SALSA HPCDynamic Virtual Cluster Hosting

iDataplex Bare-metal Nodes (32 nodes)

XCAT Infrastructure

Linux Bare-system

Linux on Xen

Windows Server 2008 Bare-

system

Cluster Switching from Linux Bare-system to Xen VMs to Windows 2008

HPC

SW-G Using Hadoop

SW-G : Smith Waterman Gotoh Dissimilarity Computation – A typical MapReduce style application

SW-G Using

Hadoop

SW-G Using DryadLINQ

SW-G Using Hadoop

SW-G Using

Hadoop

SW-G Using

DryadLINQ

Monitoring Infrastructure

SALSA

Monitoring Infrastructure

Pub/Sub Broker Network

Summarizer

Switcher

Monitoring Interface

iDataplex Bare-metal Nodes (32 nodes)

XCAT Infrastructure

Virtual/Physical Clusters

SALSA

SALSA HPC Dynamic Virtual Clusters

SALSA

Application Classes(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

Grids

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

Grids

6 MapReduce++ It describes file(database) to file(database) operations which has three subcategories.

1) Pleasingly Parallel Map Only2) Map followed by reductions3) Iterative “Map followed by reductions” –

Extension of Current Technologies that supports much linear algebra and datamining

Clouds

SALSA

Applications & Different Interconnection PatternsMap Only Classic

MapReduceIte rative Reductions

MapReduce++Loosely Synchronous

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

High Energy Physics (HEP) HistogramsSWG gene alignmentDistributed 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

SALSA

Summary: Key Features of our Approach II

• Dryad/Hadoop/Azure promising for Biology computations• Dynamic Virtual Clusters allow one to switch between

different modes• Overhead of VM’s on Hadoop (15%) acceptable• Inhomogeneous problems currently favors Hadoop over

Dryad• MapReduce++ allows iterative problems (classic linear

algebra/datamining) to use MapReduce model efficiently