'Map tasks' presentation slideshows

CS 61C: Great Ideas in Computer Architecture (formerly called Machine Structures) Map Reduce

CS 61C: Great Ideas in Computer Architecture (formerly called Machine Structures) Map Reduce. Instructor: David A. Patterson http://inst.eecs.Berkeley.edu/~cs61c/sp12. Review.

813 views • 72 slides

Scalable Programming and Algorithms for Data Intensive Life Science Applications

Scalable Programming and Algorithms for Data Intensive Life Science Applications. Judy Qiu http://salsahpc.indiana.edu Assistant Professor, School of Informatics and Computing Assistant Director, Pervasive Technology Institute Indiana University. Data Intensive Seattle, WA.

693 views • 50 slides

Big Data Learning Systems

Big Data Learning Systems. Cloud and Information Services Lab, Microsoft. Machine Learning Workflow. Step I: Example Formation Feature and Label Extraction Step II: Modeling Step III: Evaluation (and eventually Deployment). Examples. Model. Example Formation. Modeling. Evaluation.

526 views • 41 slides

Overview of Cyberinfrastructure and the Breadth of Its Application

Overview of Cyberinfrastructure and the Breadth of Its Application. Geoffrey Fox gcf@indiana.edu http://www.infomall.org http://www.futuregrid.org Director, Digital Science Center, Pervasive Technology Institute

472 views • 37 slides

Programming Your Network at Run-Time for Big Data Applications

Programming Your Network at Run-Time for Big Data Applications. Guohui Wang, TS Eugene Ng, Anees Shaikh Presented by Jon Logan. Objectives. Why Change Dynamically? ? Hadoop Essentials How this is accomplished SDN <-> Master Interaction Traffic Patterns Why Application Aware?

531 views • 37 slides

MapReduce and Hadoop Distributed File System

MapReduce and Hadoop Distributed File System. K. Madurai and B. Ramamurthy. Contact: Dr. Bina Ramamurthy CSE Department University at Buffalo (SUNY) bina@buffalo.edu http://www.cse.buffalo.edu/faculty/bina Partially Supported by NSF DUE Grant: 0737243. The Context: Big-data.

718 views • 36 slides

Shark: Hive (SQL) on Spark

Shark: Hive (SQL) on Spark. Reynold Xin UC Berkeley AMP Camp Aug 29, 2013. UC BERKELEY. Stage 0: Map-Shuffle-Reduce Mapper(row) { fields = row.split ("t") emit(fields[0], fields[1]); } Reducer(key, values) { sum = 0; for (value in values) { sum += value; }

470 views • 35 slides

Scott Miao, Trend Micro s cott_miao@trend.tw @ takeshi.miao

Threat Connect : a visualized cyber-threats entity reporting system backed with H adoop ecosystem. Scott Miao, Trend Micro s cott_miao@trend.com.tw @ takeshi.miao. Who am I. RD, SPN, Trend Micro 3+ years for Hadoop eco system Expertise in HDFS/MR/ HBase @ takeshi.miao. Agenda.

588 views • 33 slides

Hadoop & Cheetah

Hadoop & Cheetah. Key words . Cluster ? data center Lots of machines thousands Node ? a server in a data center Commodity device fails very easily Slot ? a fraction of a server Allows you to share a server among many jos Job ? an application that the user wants to run

462 views • 31 slides

Resource Provisioning Framework for MapReduce Jobs with Performance Goals

Resource Provisioning Framework for MapReduce Jobs with Performance Goals. Abhishek Verma 1,2 , Lucy Cherkasova 2 , Roy H. Campbell 1 1 University of Illinois at Urbana-Champaign 2 HP Labs. Unprecedented Data Growth.

445 views • 30 slides

Twister: A Runtime for Iterative MapReduce

665 views • 26 slides

Map/Reduce on Lustre Hadoop Performance in HPC Environments

Map/Reduce on Lustre Hadoop Performance in HPC Environments. Nathan Rutman, Xyratex James B. Hofmann, Naval Research Laboratory. Agenda. Map Reduce Overview The Case for Moving Data A Combined Lustre / HDFS Cluster Theoretical Comparisons Benchmark Study The Effects of Tuning

393 views • 25 slides

NetFlow Analysis with MapReduce

NetFlow Analysis with MapReduce. Wonchul Kang , Yeonhee Lee, Youngseok Lee. Chungnam National University {teshi85, yhlee06, lee}@cnu.ac.kr 2010.04.24(Sat). based on "An Internet Traffic Analysis Method with MapReduce", Cloudman workshop, April 2010. Introduction.

1.25k views • 22 slides

MapReduce : Simpliyed Data Processing on Large Clusters

MapReduce : Simpliyed Data Processing on Large Clusters. Jeffrey Dean and Sanjay Ghemawat. To appear in OSDI 2004 ( O perating S ystems D esign and I mplementation) . Jeff Dean. Sanjay Ghemawat. Introduce.

332 views • 21 slides

Parallel Programming Models

433 views • 16 slides

CS525 : Big Data Analytics

CS525 : Big Data Analytics. MapReduce Computing Paradigm & Apache Hadoop Open Source Fall 2013 Elke A. Rundensteiner. Large-Scale Data Analytics. Many enterprises turn to Hadoop computing paradigm for big data applications : . vs. Database.

367 views • 15 slides

Beyond Mapper and Reducer

Beyond Mapper and Reducer. Rozemary Scarlat September 13, 2011. Partitioner , Combiner, Hadoop P arameters and more. Data flow with multiple reducers. Partitioner. the map tasks partition their output, each creating one partition for each reduce task

294 views • 11 slides

7 Fun Things to do with MapReduce

194 views • 10 slides

Dynamic Virtual Cluster provisioning via XCAT on iDataPlex Supports both stateful and stateless OS images

Science Cloud (Dynamic Virtual Cluster) Architecture. Smith Waterman Dissimilarities, CAP-3 Gene Assembly, PhyloD Using DryadLINQ, High Energy Physics, Clustering, Multidimensional Scaling, Generative Topological Mapping. Applications. Services and Workflow. Apache Hadoop / Twister/ MPI.

310 views • 9 slides

Extending Map-Reduce for Efficient Predicate-Based Sampling

Extending Map-Reduce for Efficient Predicate-Based Sampling. Raman Grover Dept of Computer Science, University of California, Irvine. P roblem Statement . Collect and process as much as you can ! Collected data may accumulate to tera / peta bytes . Sampling !

238 views • 8 slides