Twister: A Runtime for Iterative MapReduce HPDC – 2010 MAPREDUCE’10 Workshop, Chicago, 06/22/2010 Jaliya Ekanayake Community Grids Laboratory, Digital Science Center Pervasive Technology Institute Indiana University SALSA Acknowledgements to: • Co authors: Hui Li, Binging Shang, Thilina Gunarathne Seung-Hee Bae, Judy Qiu, Geoffrey Fox School of Informatics and Computing Indiana University Bloomington • Team at IU SALSA Motivation Data Deluge Experiencing in many domains MapReduce Classic Parallel Runtimes (MPI) Data Centered, QoS Efficient and Proven techniques Expand the Applicability of MapReduce to more classes of Applications Iterative MapReduce Map-Only Input map Output MapReduce More Extensions iterations Input map Input map reduce Pij reduce SALSA Features of Existing Architectures(1) Google, Apache Hadoop, Sector/Sphere, Dryad/DryadLINQ (DAG based) • Programming Model – MapReduce (Optionally “map-only”) – Focus on Single Step MapReduce computations (DryadLINQ supports more than one stage) • Input and Output Handling – Distributed data access (HDFS in Hadoop, Sector in Sphere, and shared directories in Dryad) – Outputs normally goes to the distributed file systems • Intermediate data – Transferred via file systems (Local disk-> HTTP -> local disk in Hadoop) – Easy to support fault tolerance – Considerably high latencies SALSA Features of Existing Architectures(2) • Scheduling – A master schedules tasks to slaves depending on the availability – Dynamic Scheduling in Hadoop, static scheduling in Dryad/DryadLINQ – Naturally load balancing • Fault Tolerance – – – – – Data flows through disks->channels->disks A master keeps track of the data products Re-execution of failed or slow tasks Overheads are justifiable for large single step MapReduce computations Iterative MapReduce SALSA A Programming Model for Iterative MapReduce • Distributed data access • In-memory MapReduce • 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 transfers Static data Configure() Iterate User Program Map(Key, Value) δ flow Reduce (Key, List<Value>) Combine (Map<Key,Value>) Close() Twister Constraints for Side Effect Free map/reduce tasks Computation Complexity >> Complexity of Size of the Mutant Data (State) SALSA Twister Programming Model Worker Nodes configureMaps(..) Local Disk configureReduce(..) Cacheable map/reduce tasks while(condition){ runMapReduce(..) May send <Key,Value> pairs directly Iterations Map() Reduce() Combine() operation updateCondition() } //end while close() User program’s process space Communications/data transfers via the pub-sub broker network Two configuration options : 1. Using local disks (only for maps) 2. Using pub-sub bus SALSA Twister API 1.configureMaps(PartitionFile partitionFile) 2.configureMaps(Value[] values) 3.configureReduce(Value[] values) 4.runMapReduce() 5.runMapReduce(KeyValue[] keyValues) 6.runMapReduceBCast(Value value) 7.map(MapOutputCollector collector, Key key, Value val) 8.reduce(ReduceOutputCollector collector, Key key,List<Value> values) 9.combine(Map<Key, Value> keyValues) SALSA Twister Architecture Master Node B Twister Driver B B B Pub/sub Broker Network Main Program One broker serves several Twister daemons Twister Daemon Twister Daemon map reduce Cacheable tasks Worker Pool Local Disk Worker Node Worker Pool Scripts perform: Data distribution, data collection, and partition file creation Local Disk Worker Node SALSA Input/Output Handling Node 0 Node 1 Node n Data Manipulation Tool Partition File A common directory in local disks of individual nodes e.g. /tmp/twister_data • Data Manipulation Tool: – Provides basic functionality to manipulate data across the local disks of the compute nodes – Data partitions are assumed to be files (Contrast to fixed sized blocks in Hadoop) – Supported commands: • mkdir, rmdir, put,putall,get,ls, • Copy resources • Create Partition File SALSA Partition File File No Node IP Daemon No File partition path 4 156.56.104.96 2 /home/jaliya/data/mds/GD-4D-23.bin 5 156.56.104.96 2 /home/jaliya/data/mds/GD-4D-0.bin 6 156.56.104.96 2 /home/jaliya/data/mds/GD-4D-27.bin 7 156.56.104.96 2 /home/jaliya/data/mds/GD-4D-20.bin 8 156.56.104.97 4 /home/jaliya/data/mds/GD-4D-23.bin 9 156.56.104.97 4 /home/jaliya/data/mds/GD-4D-25.bin 10 156.56.104.97 4 /home/jaliya/data/mds/GD-4D-18.bin 11 156.56.104.97 4 /home/jaliya/data/mds/GD-4D-15.bin • Partition file allows duplicates • One data partition may reside in multiple nodes • In an event of failure, the duplicates are used to reschedule the tasks SALSA The use of pub/sub messaging • Intermediate data transferred via the broker network • Network of brokers used for load balancing – Different broker topologies • Interspersed computation and data transfer minimizes large message load at the brokers • Currently supports – NaradaBrokering – ActiveMQ E.g. 100 map tasks, 10 workers in 10 nodes ~ 10 tasks are producing outputs at once map task queues Map workers Broker network Reduce() SALSA Scheduling • Twister supports long running tasks • Avoids unnecessary initializations in each iteration • Tasks are scheduled statically – Supports task reuse – May lead to inefficient resources utilization • Expect user to randomize data distributions to minimize the processing skews due to any skewness in data SALSA Fault Tolerance • Recover at iteration boundaries • Does not handle individual task failures • Assumptions: – Broker network is reliable – Main program & Twister Driver has no failures • Any failures (hardware/daemons) result the following fault handling sequence – Terminate currently running tasks (remove from memory) – Poll for currently available worker nodes (& daemons) – Configure map/reduce using static data (re-assign data partitions to tasks depending on the data locality) – Re-execute the failed iteration SALSA Performance Evaluation • Hardware Configurations Cluster ID # nodes # CPUs in each node Cluster-I 32 6 Cluster-II 230 2 # Cores in each CPU 8 4 Total CPU cores Supported OSs 768 1840 Linux (Red Hat Enterprise Linux Red Hat Enterprise Linux Server release 5.4 -64 bit) Server release 5.4 -64 bit Windows (Windows Server 2008 64 bit) • We use the academic release of DryadLINQ, Apache Hadoop version 0.20.2, and Twister for our performance comparisons. • Both Twister and Hadoop use JDK (64 bit) version 1.6.0_18, while DryadLINQ and MPI uses Microsoft .NET version 3.5. SALSA Pair wise Sequence Comparison using Smith Waterman Gotoh • Typical MapReduce computation • Comparable efficiencies • Twister performs the best SALSA Pagerank – An Iterative MapReduce Algorithm Partial Adjacency Matrix Current Page ranks (Compressed) M Partial Updates R Iterations C Partially merged Updates • Well-known pagerank algorithm [1] • Used ClueWeb09 [2] (1TB in size) from CMU • Reuse of map tasks and faster communication pays off [1] Pagerank Algorithm, http://en.wikipedia.org/wiki/PageRank [2] ClueWeb09 Data Set, http://boston.lti.cs.cmu.edu/Data/clueweb09/ SALSA Multi-dimensional Scaling While(condition) { <X> = [A] [B] <C> C = CalcStress(<X>) } While(condition) { <T> = MapReduce1([B],<C>) <X> = MapReduce2([A],<T>) C = MapReduce3(<X>) } • Maps high dimensional data to lower dimensions (typically 2D or 3D) • SMACOF (Scaling by Majorizing of COmplicated Function)[1] [1] J. de Leeuw, "Applications of convex analysis to multidimensional scaling," Recent Developments in Statistics, pp. 133-145, 1977. SALSA Conclusions & Future Work • Twister extends the MapReduce to iterative algorithms • Several iterative algorithms we have implemented – – – – – K-Means Clustering Pagerank Matrix Multiplication Multi dimensional scaling (MDS) Breadth First Search • Integrating a distributed file system • Programming with side effects yet support fault tolerance SALSA Related Work • General MapReduce References: – Google MapReduce – Apache Hadoop – Microsoft DryadLINQ – Pregel : Large-scale graph computing at Google – Sector/Sphere – All-Pairs – SAGA: MapReduce – Disco SALSA Questions? Thank you! SALSA Extra Slides SALSA Hadoop (Google) Architecture Job tracker assigns some map outputs to a reducer 4 Job Tracker 3 Task Tracker Task tracker notifies job tracker M Map task reads Input data from HDFS 2 Map output goes to local disk first Task Tracker Reducer downloads map outputs using HTTP 5 1 R 6 Reduce output goes to HDFS Local Local HDFS • • • HDFS stores blocks, manages replications, handle failures Map/reduce are Java processes, not long running Failed maps are re-executed, failed reducers collect data from maps again SALSA Twister Architecture Twister API 1. configureMaps(PartitionFile partitionFile) Main program 2. configureMaps(Value[] values) 3. configureReduce(Value[] values) Broker Network Twister Driver 4. String key=addToMemCache(Value value) 5. removeFromMemCache(String key) B 6. runMapReduce() B B 4 7. runMapReduce(KeyValue[] keyValues) Broker Connection 8. runMapReduceBCast(Value value) Receive static data (1) OR Variable data (key,value) via the brokers (2) 2 1 M Read static data from local disk Twister Daemon Twister Daemon 1 Local 3 Map output goes directly to reducer R 4 Reduce output goes to local disk OR to Combiner Local • Scripts for file manipulations • Twister daemon is a process, but Map/Reduce tasks are Java Threads (Hybrid approach) SALSA Twister • In-memory MapReduce • 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 transfers Static data Iterate Configure() User Program Map(Key, Value) δ flow Reduce (Key, List<Value>) Combine (Key, List<Value>) Close() Different synchronization and intercommunication mechanisms used by the parallel runtimes SALSA Publications 1. Jaliya Ekanayake, (Advisor: Geoffrey Fox) Architecture and Performance of Runtime Environments for Data Intensive Scalable Computing, Accepted for the Doctoral Showcase, SuperComputing2009. 2. Xiaohong Qiu, Jaliya Ekanayake, Scott Beason, Thilina Gunarathne, Geoffrey Fox, Roger Barga, Dennis Gannon, Cloud Technologies for Bioinformatics Applications, Accepted for publication in 2nd ACM Workshop on Many-Task Computing on Grids and Supercomputers, SuperComputing2009. 3. Jaliya Ekanayake, Atilla Soner Balkir, Thilina Gunarathne, Geoffrey Fox, Christophe Poulain, Nelson Araujo, Roger Barga, DryadLINQ for Scientific Analyses, Accepted for publication in Fifth IEEE International Conference on e-Science (eScience2009), Oxford, UK. 4. Jaliya Ekanayake and Geoffrey Fox, High Performance Parallel Computing with Clouds and Cloud Technologies, First International Conference on Cloud Computing (CloudComp2009), Munich, Germany. – An extended version of this paper goes to a book chapter. 5. Geoffrey Fox, Seung-Hee Bae, Jaliya Ekanayake, Xiaohong Qiu, and Huapeng Yuan, Parallel Data Mining from Multicore to Cloudy Grids, High Performance Computing and Grids workshop, 2008. – An extended version of this paper goes to a book chapter. 6. Jaliya Ekanayake, Shrideep Pallickara, Geoffrey Fox, MapReduce for Data Intensive Scientific Analyses, Fourth IEEE International Conference on eScience, 2008, pp.277-284. 7. Jaliya Ekanayake, Shrideep Pallickara, and Geoffrey Fox, A collaborative framework for scientific data analysis and visualization, Collaborative Technologies and Systems(CTS08), 2008, pp. 339346. 8. Shrideep Pallickara, Jaliya Ekanayake and Geoffrey Fox, A Scalable Approach for the Secure and Authorized Tracking of the Availability of Entities in Distributed Systems, 21st IEEE International Parallel & Distributed Processing Symposium (IPDPS 2007). SALSA