Using Cloud Technologies for Bioinformatics Applications MTAGS Workshop SC09 Portland Oregon November 16 2009 Judy Qiu [email protected] www.infomall.org/salsa Community Grids Laboratory Pervasive Technology Institute Indiana University SALSA.
Download ReportTranscript Using Cloud Technologies for Bioinformatics Applications MTAGS Workshop SC09 Portland Oregon November 16 2009 Judy Qiu [email protected] www.infomall.org/salsa Community Grids Laboratory Pervasive Technology Institute Indiana University SALSA.
Using Cloud Technologies for Bioinformatics Applications MTAGS Workshop SC09 Portland Oregon November 16 2009 Judy Qiu [email protected] www.infomall.org/salsa Community Grids Laboratory Pervasive Technology Institute Indiana University SALSA Collaborators in SALSA Project Microsoft Research Indiana University Technology Collaboration SALSA Technology Team Azure (Clouds) Dennis Gannon Roger Barga Dryad (Parallel Runtime) Christophe Poulain CCR (Threading) George Chrysanthakopoulos DSS (Services) Henrik Frystyk Nielsen Community Grids Lab and UITS RT – PTI Geoffrey Fox Judy Qiu Scott Beason Jaliya Ekanayake Thilina Gunarathne Jong Youl Choi Yang Ruan Seung-Hee Bae Hui Li Saliya Ekanayake Thilina Gunarathne Applications Bioinformatics, CGB Haixu Tang, Mina Rho, Peter Cherbas, Qunfeng Dong IU Medical School Gilbert Liu Demographics (Polis Center) Neil Devadasan Cheminformatics David Wild, Qian Zhu Physics CMS group at Caltech (Julian Bunn) SALSA Convergence is Happening Data Intensive Paradigms Data intensive application (three basic activities): capture, curation, and analysis (visualization) Cloud infrastructure and runtime Clouds Multicore Parallel threading and processes SALSA MapReduce “File/Data Repository” Parallelism Instruments Map = (data parallel) computation reading and writing data Reduce = Collective/Consolidation phase e.g. forming multiple global sums as in histogram Communication via Messages/Files Disks Map1 Map2 Map3 Computers/Disks Reduce Portals /Users SALSA Cluster Configurations Feature GCB-K18 @ MSR iDataplex @ IU Tempest @ IU CPU 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 32 256 768 Total CPU Cores Used 256 DryadLINQ Hadoop/ Dryad / MPI DryadLINQ / MPI SALSA Dynamic Virtual Cluster Architecture Applications Runtimes Infrastructure software Smith Waterman Dissimilarities, CAP-3 Gene Assembly, PhyloD Using DryadLINQ, High Energy Physics, Clustering, Multidimensional Scaling, Generative Topological Mapping Apache Hadoop / MapReduce++ / MPI Linux Baresystem Linux Virtual Machines Xen Virtualization Microsoft DryadLINQ / MPI Windows Server 2008 HPC Bare-system Windows Server 2008 HPC Xen Virtualization XCAT Infrastructure Hardware iDataplex Bare-metal Nodes • Dynamic Virtual Cluster provisioning via XCAT • Supports both stateful and stateless OS images 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 125 million distances 4 hours & 46 minutes • 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) 20000 18000 DryadLINQ 16000 MPI 14000 12000 10000 8000 Processes work better than threads when used inside vertices 100% utilization vs. 70% 6000 4000 2000 0 35339 50000 SALSA SALSA SALSA Hierarchical Subclustering SALSA Pairwise Clustering 30,000 Points on Tempest 6 Clustering by Deterministic Annealing 5 MPI 4 Parallel Overhead 3 2 Thread Thread Thread Thread 1 MPI Thread Thread 0 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 96 128 128 192 288 384 384 480 576 672 744 Parallelism MPI -1 Thread MPI SALSA Dryad versus MPI for Smith Waterman Performance of Dryad vs. MPI of SW-Gotoh Alignment Time per distance calculation per core (miliseconds) 7 6 Dryad (replicated data) 5 Block scattered MPI (replicated data) Dryad (raw data) 4 Space filling curve MPI (raw data) Space filling curve MPI (replicated data) 3 2 1 0 0 10000 20000 30000 40000 50000 60000 Sequeneces Flat is perfect scaling SALSA Hadoop/Dryad Comparison “Homogeneous” Data 0.012 Time per Alignment (ms) Dryad 0.01 0.008 Hadoop 0.006 0.004 0.002 0 30000 35000 40000 45000 50000 55000 Number of Sequences Dryad with Windows HPCS compared to Hadoop with Linux RHEL on Idataplex Using real data with standard deviation/length = 0.1 SALSA Time (s) Hadoop/Dryad Comparison Inhomogeneous Data I Randomly Distributed Inhomogeneous Data Mean: 400, Dataset Size: 10000 1900 1850 1800 1750 1700 1650 1600 1550 1500 0 50 DryadLinq SWG 100 150 200 Standard Deviation Hadoop SWG 250 300 Hadoop SWG on VM Inhomogeneity of data does not have a significant effect when the sequence lengths are randomly distributed Dryad with Windows HPCS compared to Hadoop with Linux RHEL on Idataplex (32 nodes) SALSA Hadoop/Dryad Comparison Inhomogeneous Data II Skewed Distributed Inhomogeneous data Mean: 400, Dataset Size: 10000 6,000 Total Time (s) 5,000 4,000 3,000 2,000 1,000 0 0 50 DryadLinq SWG 100 150 200 250 300 Standard Deviation Hadoop SWG Hadoop SWG on VM This shows the natural load balancing of Hadoop MR dynamic task assignment using a global pipeline in contrast to the DryadLinq static assignment Dryad with Windows HPCS compared to Hadoop with Linux RHEL on Idataplex (32 nodes) SALSA Hadoop VM Performance Degradation 30% 25% 20% 15% 10% 5% 0% 10000 20000 30000 40000 50000 No. of Sequences Perf. Degradation On VM (Hadoop) • 15.3% Degradation at largest data set size SALSA PhyloD using Azure and DryadLINQ • Derive associations between HLA alleles and HIV codons and between codons themselves SALSA Mapping of PhyloD to Azure Tracking Tables Local Storage Local Storage Local Storage Web Role Local Storage Blob containers Worker Roles Welcome User PhyloD (Phylogeny-Based Association Analysis) Submit Job Track Jobs Sign Out Job Title: Use Sample Files Sample Tree File: Help Select Tree File Browse… Select Predictor File Browse… Select Target File Browse… Download ((((((((((((((((((((((((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) Work-Item Queue Sample Predictor File: Download var AnHla AnHla AnHla AnHla cid 1 2 3 4 Sample Target File: var AnAA@APos AnAA@APos AnAA@APos AnAA@APos AnAA@APos Distribution: val 1 0 0 1 Download cid 1 2 3 4 5 val 0 0 0 1 0 Partition Count: FDR Method: Min. Null Count: Include Targets as Predictors Min. Observation Count: 3 Submit ©2008 Microsoft Corporation. All rights reserved. Terms of Use | Privacy Statement | Contact Us Client SALSA PhyloD Azure Performance • 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 SALSA Iterative Computations K-means Performance of K-Means Matrix Multiplication Parallel Overhead Matrix Multiplication SALSA Kmeans Clustering Time for 20 iterations • • • • • • Iteratively refining operation New maps/reducers/vertices in every iteration Large File system based communication Overheads 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) SALSA MapReduce++ (CGL-MapReduce) Pub/Sub Broker Network Worker Nodes D M R D M R Data Split • • • • • • M R M R MR Driver User Program File System M Map Worker R Reduce Worker D MRDeamon Communication 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 SALSA SALSA HPC Dynamic Virtual Cluster Hosting Monitoring Infrastructure SW-G Using Hadoop SW-G Using Hadoop SW-G Using DryadLINQ Linux Bare-system Linux on Xen Windows Server 2008 Baresystem SW-G SW-G Using Using Hadoop DryadLINQ Cluster Switching from Linux Baresystem to Xen VMs to Windows 2008 HPC SW-G Using Hadoop XCAT Infrastructure iDataplex Bare-metal Nodes (32 nodes) SW-G : Smith Waterman Gotoh Dissimilarity Computation – A typical MapReduce style application SALSA Monitoring Infrastructure Monitoring Interface Pub/Sub Broker Network Virtual/Physical Clusters XCAT Infrastructure Summarizer Switcher iDataplex Bare-metal Nodes (32 nodes) 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 Only 2) Map followed by reductions 3) Iterative “Map followed by reductions” – Extension of Current Technologies that supports much linear algebra and datamining Clouds SALSA Applications & Different Interconnection Patterns Map Only Input map Classic MapReduce Input map Ite rative Reductions MapReduce++ Input map Loosely Synchronous iterations Pij Output reduce reduce CAP3 Analysis Document conversion (PDF -> HTML) Brute force searches in cryptography Parametric sweeps High Energy Physics (HEP) Histograms SWG gene alignment Distributed search Distributed sorting Information retrieval Expectation maximization algorithms Clustering Linear 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 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 SALSA