IU ORE-Chem Update

1. IU ORE-Chem Update Marlon Pierce, Geoffrey Fox Indiana University

2. IU to lead New US NSF Track 2d $10M Award See http://www.futuregrid.org for more information.

3. What We Said We Would Do • Apply data-centric workflow technologies (Dryad) • Significant effort • Install and run triple store • Done locally. • Need to do this in Azure. • Design alternative formats for ORE (JSON, Microformats) • Nothing to report yet • Design secure services, compositions, mash-ups • OAuth piece done. • Significant effort on social network interfaces • Nothing to report on ORE-chem enabled services yet • Investigate clouds for ORE-Chem • Infrastructure and runtime • Significant effort on virtual data stores, overheads of virtualization.

4. Layer Cake of IU Activities Web 2.0 Research: Security for REST Services Cloud Computing: Infrastructure and Runtimes Infrastructure: Windows HPC Testbeds

5. Hardware Layer

6. Cloud Infrastructure • Tempest: HP distributed shared memory cluster with 768 processor cores and 1.5 TB total memory capacity. The cluster includes 13.7 TB of local spinning disk. • Tempest can be dynamically reconfigured to act as either a Windows HPC or Linux cluster. • Smaller versions Madrid and Barcelona • Other machines: • The IBM iDataPlex system is an IBM e1350 distributed shared memory cluster with 1024 processor cores and 3 TB total memory capacity. • Cray XT5m distributed shared memory cluster with 672 processor cores and 1.3 TB total memory capacity. • A shared memory system with at least 480 cores and 640 GB of RAM will also be installed at IU as part of the FutureGrid award.

8. Data Cloud Infrastructure

9. Triple Store: Intellidimension • This has been installed on IU servers. • We are ready for data. • Efforts to install this on MS Azure were not successful. • Inadequate documentation earlier in the year. • We will revisit this.

10. Open Elastic Block Store • Amazon EBS is a way to mount virtual disks in cloud-space. • Empty disk space or archived data stores • ORECHEM enabled data sets, for example. • Clone-able, so keep your own version of community data. • We are implementing an open version of this. • Contribute to Nimbus, an open-source EC2 • But independent of Xen, etc. • Would be interesting to do this for Windows • Eventual backbone: IU has over a petabyte disk space of lustre file system. • Can be used to load and store VMs. • X. Gao won best student poster award at TG09. • Paper accepted to E-Science 2009

11. Block Store Architecture Volume Server Virtual Machine Manager (Xen Dom 0) VM instance ( Xen Dom U) VBD iSCSI Volume Delegate VMM Delegate Create Volume, Export Volume, Create Snapshot,Etc. Import Volume, Attach Device, Detach Device,Etc. VBS Web Service VBS Client

12. Integration with Cloud Computing Systems iSCSI Volume Server Xen Dom 0 Xen Dom U VBD Xen Delegate Volume Delegate Create Volume, Export Volume, Create Snapshot,Etc. Import Volume, Attach Device, Detach Device,Etc. Nimbus Workspace Service VBS Web Service Query for Xen Dom0 Host and DomUId with <Nimbus Instance Id> VBS_Nimbus Web Service Attach-volume <volId> <Nimbus Instance Id> <device> VBS Client

13. Programming Clouds

14. Multicore and Cloud Technologies to support Data Intensive applications • Using Dryad (Microsoft) and MPI to study structure of Gene Sequences on Tempest Cluster. We are working on PubChem. See http://www.infomall.org/salsa for lab projects (X. Qiu).

15. Courtesy of Jong Y. Choi PubChem dataset consists of binary 166 MACCS keys (fingerprints), which indicate whether a each chemical compound has a special functional molecule or not We have total 26,466,421 chemical compounds. (i.e, the total PubChem dataset has 166 dimensions and 26M records) Randomly selected 50K chemicals to produce 3D GTM map. GTM is an algorithm to find a lower dimension structure from higher dimensional data (3D in this case). http://www.youtube.com/watch?v=nylgjKgnSLg

16. IU’s ORE-CHEM Pipeline Harvest NIH PubChem for 3D Structures Convert Gaussian Output to CML Convert CML to RDF->ORE-Chem Convert PubChem XML to CML Submit Jobs to TeraGrid with Swarm Insert RDF into RDF Triple Store Goal is to create a public, searchable triple store populated with ORE-CHEM data on drug-like molecules. Convert PubChem XML to CML Convert CML to Gaussian Input Conversions are done with Jumbo/CML tools from Peter Murray Rust’s group at Cambridge. Swarm is a Web service capable of managing 10,000’s of jobs on the TeraGrid. We are developing a Dryad version of the pipeline.

17. Iterative MapReduce- Kmeans Clustering and Matrix Multiplication Overhead of parallel runtimes – Matrix Multiplication • Compute intensive application O(n^3) • Higher data transfer requirements O(n^2) • CGL-MapReduce shows minimal overheads next to MPI Iterative MapReduce algorithm for Matrix Multiplication Overhead of parallel runtimes – Kmeans Clustering • O(n) calculations in each iteration • Small data transfer requirements O(1) • With large data sets, CGL-MapReduce shows negligible overheads • Extremely higher overheads in Hadoop and Dryad Kmeans Clustering implemented as an iterative MapReduce application Jaliya Ekanayake {jekanaya@cs.indiana.edu}

18. High Performance Parallel Computing on Cloud • Performance of MPI on virtualized resources • Evaluated using a dedicated private cloud infrastructure • Exactly the same hardware and software configurations in bare-metal and virtual nodes • Applications with different communication: computation ratios • Different virtual machine(VM) allocation strategies {1-VM per node to 8-VMs per node} Performance of Matrix multiplication under different VM configurations Overhead under different VM configurations for Concurrent Wave Equation Solver • O(1) communication (Smaller messages) • More susceptible to latency • Higher overheads under virtualized resources • O(n^2) communication (n = dimension of a matrix) • More susceptible to bandwidth than latency • Minimal overheads under virtualized resources Jaliya Ekanayake {jekanaya@cs.indiana.edu}

19. Conclusions: Dryad for Scientific Computing • Investigated several applications with various computation, communication, and data access requirements • All DryadLINQ applications work, and in many cases perform better than Hadoop • We can definitely use DryadLINQ (and Hadoop) for scientific analyses • We did not implement (find) • Applications that can only be implemented using DryadLINQ but not with typical MapReduce • Current release of DryadLINQ has some performance limitations • DryadLINQ hides many aspects of parallel computing from user • Coding is much simpler in DryadLINQ than Hadoop (provided that the performance issues are fixed) • Key issue is support of inhomogeneous data

20. IU’s ORE-CHEM Pipeline Harvest NIH PubChem for 3D Structures Convert Gaussian Output to CML Convert CML to RDF->ORE-Chem Convert PubChem XML to CML Submit Jobs to TeraGrid with Swarm Insert RDF into RDF Triple Store Goal is to create a public, searchable triple store populated with ORE-CHEM data on drug-like molecules. Convert PubChem XML to CML Convert CML to Gaussian Input Conversions are done with Jumbo/CML tools from Peter Murray Rust’s group at Cambridge. Swarm is a Web service capable of managing 10,000’s of jobs on the TeraGrid. We are developing a Dryad version of the pipeline.

21. Architecture of Swarm Service Swarm-Analysis Standard Web Service Interface Large Task Load Optimizer Swarm-Grid Connector Swarm-Dryad Connector Swarm-Hadoop Connector Local RDMBS Swarm-Grid Swarm-Dryad Swarm-Hadoop Grid HPC/ Condor Cluster Cloud Comp. Cluster Windows Server Cluster

22. Swarm-Grid • Swarm considers traditional Grid HPC cluster are suitable for the high-throughput jobs. • Parallel jobs (e.g. MPI jobs) • Long running jobs • Resource Ranking Manager • Prioritizes the resources with QBETS, INCA • Fault Manager • Fatal faults • Recoverable faults Swarm-Grid Standard Web Service Interface Request Manager QBETS Web Service Resource Ranking Manager Data Model Manager Fault Manager Hosted by UCSB User A’s Job Board Local RDMBS Job Queue Job Distributor MyProxy Server Grid HPC/Condor pool Resource Connector Hosted by TeraGrid Project Condor(Grid/Vanilla) with Birdbath Grid HPC Clusters Grid HPC Clusters Condor Cluster Grid HPC Clusters Grid HPC Clusters

23. Some Details • We can submit jobs to 3 different TeraGrid machines • Abe, Mercury, Cobalt (all at NCSA) • IU’s BigRed has some technical problems • Can do about 100-200 molecules per day in tests. • Approach is fragile because application/system admins have tendency to change things every few months. • Don’t validate Globus invocations of codes

24. Dryad Data Partitioning • Two methods: • Manually place the files in every node or • Write a C# code that uses DryadLINQ partitioning operators like Hash Partition<T,K> or Range Partition<T,K> • A partitioned data set consists of 2 types of files: • A metadata file (.pt as extension) containing metadata that describes the partitions • Set of partition files, one for each data partition. \DryadData\UserName\InputData (file path and name) 4 (number of partitions depending on number of nodes available) 0,2000,NODE01 (Partition files: Partition number, size(in bytes), node name : File path) 1,2000,NODE02,NODE03:FilePath 2,2000,NODE03,NODE04 3,2000,NODE04

25. Programming the Pipeline • IQueryable<T> represents query over the data • Input data is represented by a PartitionedTable<T> object • DryadLINQ programs apply LINQ query operations to PartitionedTable<T> objects. • LINQ queries on the PartitionTable object are executed on the Cluster. • Jobs will be executed on different nodes and the output would be collected in the outputDirectory. • IQueryable<LineRecord> filenames = PartitionedTable.Get<LineRecord> (filepathuri); • IQueryable<outputinfo> outputs = filenames.Select(s => XML2CML( execFileName, programSwitches, s.line, outputDirectory));

26. Security in Web 2.0

27. OAuth: REST Security • This is actually a Year 2 deliverable but we made progress in Year 1. • OAuth is essentially security for REST. • Provide authentication and authorization • Relevant to ORE-CHEM services • Use REST URL and HTTP method • Resources are identified by URLs • Access privileges are identified by HTTP methods (GET, POST, PUT, DELETE) • Extend OAuth • Add finer-grained authorization information in requests and responses.

28. OAuth Security Status • OAuth *Core* Code provides the fundamental piece of OAuth specification 1.0. • Includes minimal webapp example • The sample web apps just support shared secret. • We extend to support PKI • Also fixed some bugs in the code. • To support OAuth extensions, more code is needed in OAuth core. • For OpenID, we use library OpenID4Java and it seems to offer enough functionalities so far. • Tutorial given at TeraGrid09 • Slides: http://www.collab-ogce.org/ogce/images/3/39/OAuthOverview-TG09.ppt • Code: http://ogce.svn.sourceforge.net/viewvc/ogce/incubator/OGCE-OAuth/

29. Acknowledgments • Geoffrey Fox • Judy Qiu and SALSA team: data mining • www.infomall.org/salsa • Jaliya Ekanayake: Dryad and Cloud performance • Sangmi Pallickara: Swarm service • Xiaoming Gao: Virtual Block Store • Zhenhua Guo: OAuth, OpenID, and Social Networks

30. Full Stop

31. Dryad and DryadLINQ Dryad is a high-performance, general-purpose distributed computing engine that simplifies the task of implementing distributed applications on clusters of computers running a windows operating system. DryadLINQ allows us to implement Dryad applications in managed code by using an extended version of the LINQ programming model and API. LINQ was introduced with Microsoft .NET framework version 3.5. DryadLINQ provider translates the application’s LINQ queries into a Dryad job and runs the job as a distributed application on a windows HPC cluster. Dryad and DryadLINQ An Introduction version 1.0. June 30, 2009. by Microsoft Research

32. Dryad and DryadLINQ An Introduction version 1.0. June 30, 2009. by Microsoft Research

33. DryadLINQ Programming Guide version 1.0. June 30, 2009. by Microsoft Research

34. Client Workstation: runs DryadLINQ application. DryadLINQProvider creates a Windows HPC job on the cluster to handle the Dryad processing, receives the results, and returns them to the application. Job Manager: Windows HPC task that manages execution of associated Dryad job on the cluster. Head Node: manages the cluster, hosts the Windows HPC Administration Console and Dryad management service. Vertices: perform the data processing on the compute nodes.

35. java.exe + jumboconverters.jar xml -> cml cml -> gaussian input Local Machine Distribute gaussian Input files across the cluster and run gaussian.exe on every file at every node in the cluster. DryadLINQ Provider (LinqToDryad.dll) Dryad Cluster

36. Distribute all the initial xml files over the cluster Dryad Cluster xml to cml conversion Stage1 Stage2 cml to gaussian conversion run gaussian on every file Stage3

37. Drilling Though Data Clouds • Applications • Cheminformatics: Mapping PubChem data into low dimensions to aid drug discovery • Biology: Expressed Sequence Tag (EST) sequence assembly (CAP3) • Biology: PairwiseAlu sequence alignment (SW) • Health: Correlating childhood obesity with environmental factors Data mining Algorithm Clustering (Pairwise , Vector) MDS, GTM, PCA, CCA Visualization PlotViz Cloud Technologies (MapReduce, Dryad, Hadoop) Classic HPC MPI FutureGrid/VM/Virtual Storage Bare metal (Computer, network, storage)

38. Architecture and Performance of Runtime Environments for Data Intensive Scalable Computing CAP3 – Gene Assembly Program • Compute intensive application • Embarrassingly parallel operation • All runtimes performs equally well • Data intensive application • MapReduce style parallel operation • Both runtimes perform comparably well Measured using 32 Compute nodes each with 8 cores and 16 GB of memory Data/compute intensive applications implemented as MapReduce “filters” Number of Reads processed High Energy Physics Data Analysis Jaliya Ekanayake {jekanaya@cs.indiana.edu} Architecture of CGL-MapReduce

39. Job Execution Time in Swarm-Dryad with Windows HPC 16 nodes

40. Job Execution Time in Swarm-Dryad various number of nodes