PPT-Reduce inputs

Author : conchita-marotz | Published Date : 2016-08-06

Reduce droughtsensitive crops Play the season Alternative land uses past the break even point Ameliorate poor soils Fallowing Livestock Less rain at opening of

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Reduce inputs: Transcript

Reduce droughtsensitive crops Play the season Alternative land uses past the break even point Ameliorate poor soils Fallowing Livestock Less rain at opening of season Less growing season rainfall. The journal is aimed at the needs o Cost and Management Accountants CMA and provides inormation analyses and research on global and national developments The wide circulation and inputs rom academicians researchers and industry stalwarts have been t Reconfigurable Computing. http://www.ece.arizona.edu/~ece506. Lecture 3. Reconfigurable Architectures. Ali Akoglu. Complex Programmable Logic Device. Hierarchical design against size explosion of PLAs. . The world will thank you. . Objective. Target Audience. About the Leaf. The Big Idea. The Campaign. Print Campaign. Out of Home Campaign. Radio Campaign. Publicity. Conclusion. AGENDA. OBJECTIVE. MapReduce. Basics of . MapReduce. Stanford CS341 Project Course. Algorithm Theory for . MapReduce. Jeffrey D. Ullman. Stanford University. Free Book – . MapReduce. and Much More. Mining of Massive Datasets. -Yarn Prototype. Karthik. . Ramachandra. – IIT Bombay. Srinath Shankar. Microsoft Gray Systems Lab. 1. Motivation. Limitations of the Map Reduce Model. Combining/Joining multiple datasets. Possible but not elegant . Large Scale Duplicate Detection. Prof. Felix . Naumann. , . Arvid. . Heise. Agenda. Big Data. Word Count Example. Hadoop. Distributed File System. Hadoop. . Map/Reduce. Advanced . Map/Reduce. Stratosphere. Based on an article by . Foto. N. . Afrati. , Anish Das . Sarma. , . Semih. . Salihoglu. , Jeffrey D. Ullman. Images taken from slides by same authors.. Agenda. MapReduce. – a brief overview. Communication / Parallelism Tradeoff Model. A Common Mistake. Size/Communication Trade-Off. Specific Tradeoffs. Jeffrey D. Ullman. Stanford University. Research Is Joint Work of. Foto. . Afrati. (NTUA). Anish. Das . Sarma. (Google). Semih. 3, 2016. Restatement of goals. Want to verify software’s correctness .  . . Need to test . . Need to decide on test cases . . No set of test cases is sufficient. 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. 1. Word Count Example. We have a large file of words, one word to a line. Count the number of times each distinct word appears in the file. Sample application: analyze web server logs to find popular URLs. SIXTY-FOUR MIC INPUTS LEFT (MONO) 100-240V 50/60 Hz 15 WATTS LEFT RIGHT RANE CORPORATION RIGHT RETURN SESSION FLEXFX LOOP MAIN OUT LEFT RIGHT USB A USB B BOOTH OUT LEFT RIGHT IN IN OUT SEND R L anauctioncontractonEthereum,howdobiddersdeposittheirbidswiththecontractgiventhatalltransactionamountsinEthereumarepubliclyvisible?Thereareseveralwayspeopledealwiththisprobleminpractice.EthereumNameSer 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.

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