SIAM PP 2014! MapReduce in Scientific Computing! February 19, 2014

Transcription

1 SIAM PP 2014! MapReduce in Scientific Computing! February 19, 2014 Paul G. Constantine! Applied Math & Stats! Colorado School of Mines David F. Gleich! Computer Science! Purdue University Hans De Sterck! Applied Mathematics! University of Waterloo Join us for dinner! Dragonwell 6:15pm 0.2 mi; Meet in the 6 Gleich & De Sterck! Two introductions to MapReduce Constantine & Benson! MapReduce-based model reduction Papalexakis! Scaling up tensor factorization Plantenga! Generating large graphs 10:35 11:00 11:25 11:50 Ching! Apache Giraph for big graphs Zaharia! Data flow computing Weimer! Relayering the big-data stack Plimpton! MapReduce & MPI 2:40 3:05 3:30 3:55

2 minisymposium: Parallel Algorithms for MapReduce-Based Scientific Computing a (second) brief introduction Hans De Sterck Department of Applied Mathematics University of Waterloo, Canada SIAM PP14, Portland, February 2014

3 origins of MapReduce Google engineers invented MapReduce Google went from nothing to $400B market cap in 15 years ( organize the world's information ) Google s initial success was built on two pillars: PageRank algorithm (random walk on web graph; spamresistant compared to counting inlinks; better search results!) MapReduce framework for scalable (parallel) processing of big data (file-based) on commodity hardware (fault-tolerant, (private) cloud pioneers) new business/legal models (advertising, creative new ways of dealing with copyright owners,...)

4 Google s big data processing framework 1. Google File System (published 2003) fault-tolerant: store every file chunk 3 times scalable 2. MapReduce (published 2004) fault-tolerant: restrict expressivity (e.g., no easy pointto-point messages), asynchronous within map and reduce: fault-tolerant through restart scalable, and efficient for big data: put computing were data resides 3. BigTable (published 2006) scalable data store

5 Hadoop: open source version of Google s framework 1. Google File System Hadoop Distributed File System (HDFS) 2. MapReduce 3. BigTable HBase used (and co-developed) by Yahoo, Facebook, Twitter,... and many, many other companies

6 MapReduce example (wordcount) (very large file) (adapted from blog.trifork.com) fault-tolerant, scalable, compute where data resides file/disk-based: slow communication, and slow to iterate (stateless, read stored data from disk, not from memory) (slow but scalable)

7 large-scale distributed/parallel computing traditional large-scale distributed/parallel computing: science, engineering,... linear algebra, PDEs, optimization, molecular dynamics, Markov chain Monte Carlo,... mostly in MPI-type (messaging) environments last decade: large-scale parallel/distributed computing has become essential in many new areas: web ranking, graph processing, social networks, data mining, machine learning, cyber security/spying, business intelligence, big data,... a significant part of these applications use MapReduce-type paradigms

8 large-scale distributed/parallel computing is a much bigger space now aspects of the MPI and MapReduce paradigms may converge... (opportunities for SIAM PP community!) e.g., can MapReduce-type paradigms act as inspiration for exascale parallel computing? (fault-tolerance, scalability, compute where data resides,..., but slow...) it makes sense to consider Scientific Computing in the broad sense (linear algebra, optimization, data mining, machine learning, graph processing,...)

9 MapReduce for scientific computing basic algorithms (e.g., linear algebra) not much explored yet (libraries: Pegasus, Mahout,...) MapReduce framework inspires (new?) recursive algorithms for linear algebra and combinatorial scientific computing (e.g., Matrix Inversion (recursive block LU) and Scalable Maximum Clique Computation Using MapReduce, Jingen Xiang, Waterloo) we have 3 talks on MapReduce for scientific computing in the rest of this morning session:

10 this session Scientific Computing Applications with MapReduce Matrix Factorizations in MapReduce with Applications to Model Reduction Paul Constantine, Colorado School of Mines; Austin Benson, Stanford Scaling Up Tensor Decompositions with MapReduce Evangelos Papalexakis, Carnegie Mellon University Generating Large Graphs with Desired Community Structure Todd Plantenga, Sandia National Laboratories

11 afternoon session scalable data analytics environments beyond MapReduce: can we extend and improve MapReduce-type approaches? (make it faster? HPC?) Apache Giraph: Large-Scale Graph Processing Infrastructure on Hadoop Avery Ching, Facebook graph algorithms (Giraph, Pregel, in memory) Large-Scale Numerical Computation Using a Data Flow Engine Matei Zaharia, MIT Spark: (fault-tolerant, scalable) data flow engine in memory (maintaining state, faster iteration, and faster communication)

12 afternoon session REEF - Beyond MapReduce by Re-Layering the Big Data Stack Markus Weimer, Microsoft YARN/REEF: more versatile scheduling, maintaining state Traditional and Streaming MapReduce via MPI for Graph Analytics Steve Plimpton, Karen D. Devine, Timothy Shead, Sandia National Labs MapReduce and MPI

13 SIAM PP 2014! MapReduce in Scientific Computing! February 19, 2014 Paul G. Constantine! Applied Math & Stats! Colorado School of Mines David F. Gleich! Computer Science! Purdue University Questions? Hans De Sterck! Applied Mathematics! University of Waterloo Gleich & De Sterck! Two introductions to MapReduce Constantine & Benson! MapReduce-based model reduction Papalexakis! Scaling up tensor factorization Plantenga! Generating large graphs 10:35 11:00 11:25 11:50 Ching! Apache Giraph for big graphs Zaharia! Data flow computing Weimer! Relayering the big-data stack Plimpton! MapReduce & MPI 2:40 3:05 3:30 3:55

14 Two themes AM Session! What is possible in the MapReduce model & Hadoop? Gleich & De Sterck! Two introductions to MapReduce Constantine & Benson! MapReduce-based model reduction Papalexakis! Scaling up tensor factorization Plantenga! Generating large graphs PM Session! How can we build-on or improve the MapReduce model? Ching! Apache Giraph for big graphs Zaharia! Spark & data flow computing Weimer! Relayering the big-data stack Plimpton! MapReduce & MPI David Gleich Purdue #SIAMPP14 27