PPT-Workflows and Abstractions for Map-Reduce

1 Recap Mapreduce Algorithms with multiple mapreduce steps Naïve bayes test routine for large datasets and large models Cleanly describing these algorithms workflow or dataflow languages . All Programmable Abstractions push beyond traditional RTL design methodologies to automate all aspects of system development and algorithm deployment into all programmable FPGAs SoC and 3D ICs Xilinx and its Alliance members are working together to 1. Abstractions On Top Of . Hadoop. We’ve decomposed some algorithms into a map-reduce “workflow” (series of map-reduce steps). naive Bayes training. naïve Bayes testing. phrase scoring. How else can we express these sorts of computations? Are there some common special cases of map-reduce steps we can parameterize and reuse?. Nima Sarshar, Ph.D.. INTUIT . Inc. ,. Nima_sarshar@intuit.com . Intuit, . Graphs and Me. Me: . Large-scale graph data processing, complex networks analysis, graph algorithms … . Intuit: . QuickBooks, TurboTax, . Open Repositories 2012. Stacy Kowalczyk. Beth . Plale. Kavitha Chandrasekar. Yiming Sun. Agenda . Introduction to Digital Curation. Workflow Systems Overview. Workflows for Digital Curation. Break. Implementing Workflows in Trident . ish. ) Frameworks. William Cohen. 1. Outline. More concise languages for map-reduce pipelines. Abstractions built on top of map-reduce. General comments. Specific systems. Cascading, Pipes. PIG, Hive. Eurocrypt. May 1. st. , 2017. Rafael Pass, Elaine Shi, . Florian Tramèr. Trusted hardware: . Different . communities. , different . world views. Crypto. Architecture. Systems. & . Security. “Minimal” trusted . Basics. Divide and conquer. Partition large problem into smaller . subproblems. Worker work on . subproblems. in parallel. Threads in a core, cores in multi-core processor, multiple processor in a machine, machines in a cluster. Disjoint Set Union-Find . and . Minimum Spanning Trees. Kate Deibel. Summer 2012. August 13, 2012. CSE 332 Data Abstractions, Summer 2012. 1. Making Connections. You have a set of nodes (numbered 1-9) on a network. . Lecture 9: B Trees. Dan Grossman. Spring 2010. Our goal. Problem: A dictionary with so much data most of it is on disk. Desire: A balanced tree (logarithmic height) that is even shallower than AVL trees so that we can minimize disk accesses and exploit disk-block size. HUBzero. : How to Use Pegasus to Execute Computational Pipelines. Ewa Deelman. USC . Information Sciences Institute. Acknowledgement: . Steven Clark, Derrick Kearney, Michael McLennan (. HUBzero. ) . Madhu M Nayak Assistant Professor, Department of C SE, GSSSIETW, Mysuru Pradeep.S Assistant Professor, Department of C SE, GEC, Kushal Nagar Abstract - Due to the increasing popularity of cheap Jiaul. Paik. Email:. . jia.paik@gmail.com. Today’s Topics. Map-reduce: Additional Details. Inverted Index using Map-reduce. Introduction to Spark. Spark Demo. Map-reduce Internals: Additional Details. kindly visit us at www.nexancourse.com. Prepare your certification exams with real time Certification Questions & Answers verified by experienced professionals! We make your certification journey easier as we provide you learning materials to help you to pass your exams from the first try. Cast of thousands. Mihai. Pop. Michael Schatz. Dan . Sommer. University of Maryland Center for Computational Biology. Faisal Khan, Ken Hahn UW . David Schwartz, LMCG. In 2003…. http://labs.google.com/papers/gfs.html.