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Architecture and Performance of Runtime Environments for Data Intensive Scalable Computing,[object Object],SC09 Doctoral Symposium,  Portland, 11/18/2009,[object Object],Student: Jaliya Ekanayake,[object Object],Advisor: Prof. Geoffrey Fox,[object Object],Community Grids Laboratory, ,[object Object],Digital Science Center,[object Object],Pervasive Technology Institute,[object Object],Indiana University,[object Object]
Cloud Runtimes for Data/Compute Intensive Applications,[object Object],Cloud Runtimes,[object Object],MapReduce ,[object Object],Dryad/DryadLINQ,[object Object],Sector/Sphere ,[object Object],Moving Computation to  Data,[object Object],Simple communication topologies,[object Object],MapReduce,[object Object],Directed Acyclic Graphs (DAG)s,[object Object],Distributed File Systems,[object Object],Fault Tolerance,[object Object],[object Object]
Represented as filter pipelines
Parallelizable filters,[object Object]
Applications using Hadoop and DryadLINQ (2),[object Object],PhyloD [1]project from Microsoft Research,[object Object],Derive associations between HLA alleles and HIV codons and between codons themselves,[object Object],DryadLINQ  implementation,[object Object],[1] Microsoft Computational Biology Web Tools, http://research.microsoft.com/en-us/um/redmond/projects/MSCompBio/,[object Object]
Applications using Hadoop and DryadLINQ (3),[object Object],125 million distances,[object Object],4 hours & 46 minutes,[object Object],Calculate  Pairwise Distances (Smith Waterman Gotoh),[object Object],Calculate pairwise distances for a collection of genes (used for clustering, MDS),[object Object],Fine grained tasks in MPI,[object Object],Coarse grained tasks in DryadLINQ,[object Object],Performed on 768 cores (Tempest Cluster),[object Object]
Applications using Hadoop and DryadLINQ (4),[object Object],[object Object]
K-Means Clustering
Matrix Multiplication
Multi-Dimensional Scaling (MDS),[object Object]
Applications & Different Interconnection Patterns,[object Object],Input,[object Object],map,[object Object],iterations,[object Object],Input,[object Object],Input,[object Object],map,[object Object],map,[object Object],Output,[object Object],Pij,[object Object],reduce,[object Object],reduce,[object Object],MPI,[object Object],Domain of MapReduce and Iterative Extensions,[object Object]
i-MapReduce,[object Object],[object Object]
Distinction on static data and variable data (data flow vs. δ flow)
Cacheable map/reduce tasks (long running tasks)
Combine operation
Support fast intermediate data transfersStatic,[object Object],data,[object Object],Configure(),[object Object],Iterate,[object Object],User Program,[object Object],δ flow,[object Object],Map(Key, Value)  ,[object Object],Reduce (Key, List<Value>) ,[object Object],Close(),[object Object],Combine (Key, List<Value>),[object Object],Different synchronization and intercommunication mechanisms used by the parallel runtimes,[object Object]
i-MapReduceProgramming Model,[object Object],runMapReduce(),[object Object],  Iterations,[object Object],Worker Nodes,[object Object],configureMaps(),[object Object],Local Disk,[object Object],configureReduce(),[object Object],Cacheable map/reduce tasks,[object Object],while(condition){,[object Object],Can send <Key,Value> pairs directly,[object Object],Map(),[object Object],Reduce(),[object Object],Combine() operation,[object Object],Communications/data transfers via the pub-sub broker network,[object Object],updateCondition(),[object Object],Two configuration options :,[object Object],Using local disks (only for maps),[object Object],Using pub-sub bus ,[object Object],} //end while,[object Object],close(),[object Object],User program’s process space,[object Object]
i-MapReduceArchitecture,[object Object],Pub/Sub Broker Network,[object Object],Map Worker,[object Object],M,[object Object],Worker Nodes,[object Object],Reduce Worker,[object Object],D,[object Object],MR,[object Object],Driver,[object Object],User,[object Object],Program,[object Object],D,[object Object],R,[object Object],M,[object Object],M,[object Object],M,[object Object],M,[object Object],MRDeamon,[object Object],D,[object Object],R,[object Object],R,[object Object],R,[object Object],R,[object Object],Data Read/Write,[object Object],File System,[object Object],Communication,[object Object],Data Split,[object Object],[object Object]
Eliminates file based communication
Cacheable map/reduce tasks
Static data remains in memory

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Architecture and Performance of Runtime Environments for Data Intensive Scalable Computing

Notes de l'éditeur

  1. Currently uses NaradaBrokering, but it is easily extensible to use any other pub/sub message infrastructure such as Apache ActiveMQ.