iterativemapreduce.org iterativemapreduce.org

iterativemapreduce.org

Twister: Iterative MapReduce

Subscribe to Twister Discussion Group. Twister version 0.9 is now available. Is a lightweight MapReduce runtime we have developed by incorporating these enhancements. Provides the following features to support MapReduce computations. ( Twister. Is developed as part of Jaliya Ekanayakes. PhD research and is supported by the S. Distinction on static and variable data. Configurable long running (cacheable) map/reduce tasks. Pub/sub messaging based communication/data transfers. Data access via local disks.

http://www.iterativemapreduce.org/

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CONTACTS AT ITERATIVEMAPREDUCE.ORG

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Twister: Iterative MapReduce | iterativemapreduce.org Reviews
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Subscribe to Twister Discussion Group. Twister version 0.9 is now available. Is a lightweight MapReduce runtime we have developed by incorporating these enhancements. Provides the following features to support MapReduce computations. ( Twister. Is developed as part of Jaliya Ekanayakes. PhD research and is supported by the S. Distinction on static and variable data. Configurable long running (cacheable) map/reduce tasks. Pub/sub messaging based communication/data transfers. Data access via local disks.
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Twister: Iterative MapReduce | iterativemapreduce.org Reviews

https://iterativemapreduce.org

Subscribe to Twister Discussion Group. Twister version 0.9 is now available. Is a lightweight MapReduce runtime we have developed by incorporating these enhancements. Provides the following features to support MapReduce computations. ( Twister. Is developed as part of Jaliya Ekanayakes. PhD research and is supported by the S. Distinction on static and variable data. Configurable long running (cacheable) map/reduce tasks. Pub/sub messaging based communication/data transfers. Data access via local disks.

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iterativemapreduce.org iterativemapreduce.org
1

Twister: Iterative MapReduce

http://www.iterativemapreduce.org/license.html

Subscribe to Twister Discussion Group. Software License for Twister. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:. 3) Any documentation included with all redistributions must include the following acknowledgement:. This product includes software developed by the Community Grid Labs. For further information contact the Community Grid Labs at http:/ www.pti.iu.edu/. Indiana University provides no reassurances ...

2

Twister: Iterative MapReduce

http://www.iterativemapreduce.org/userguide.html

Subscribe to Twister Discussion Group. Managing Data Using Twister. Use API to create Partition File. Configuring Twister Runtime in a single Machine. This section explains how to setup Twister in a single machine. Please follow the steps below. These steps assume that you are on Linux operating system and have already configured Java in all compute nodes. 1 Setting up NaradaBrokering. Download the latest version of NaradaBrokering from here: link. 2 Setting up Twister. File and set broker host. 3 Open $...

3

Twister: Iterative MapReduce

http://www.iterativemapreduce.org/samples.html

Subscribe to Twister Discussion Group. Sample Applications for Twister. MapReduce for Kmeans Clustering Algorithm. In the following figure, we compare the performance of different parallel programming runtimes for this iterative computation. Different Parallel Programming Runtimes for Kmeans Clustering Computation. More details of this analysis can be found in this paper. You can also download the MPI version of the Kmeans clustering program from this link. Task is fixed throughout the computation, while...

4

Twister Documentation - 0.7

http://www.iterativemapreduce.org/docs/index.html

This document is designed to be viewed using the frames feature. If you see this message, you are using a non-frame-capable web client. Link to Non-frame version.

5

Twister: Iterative MapReduce

http://www.iterativemapreduce.org/download.html

Subscribe to Twister Discussion Group. Released on 03/16/2011 (Recommended version). Leave Feedback about Twister.

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salsahpc.indiana.edu salsahpc.indiana.edu

The 2nd International Conference on Cloud Computing 2010

http://salsahpc.indiana.edu/CloudCom2010

Content on this page requires a newer version of Adobe Flash Player. Business and Industry Seminar. Director, CI, Distinguished Fellow, Argonne National Laboratory, IL, USA. Director of Applications for the Cloud Computing, Microsoft Research, WA, USA. Kai Hwang, Professor. IEEE Fellow, Director, Internet and Grid Comp. Lab., USC, USA. Business and Industry Seminar. What Clouds can do for your business? Cloud Computing with Windows Azure. CTO, Cloud Computing, Software. Division at Huawei, USA. Have a gr...

peet.ldee.org peet.ldee.org

Peter Lubell-Doughtie

http://peet.ldee.org/page2

Mobile Technologies presentation to the Mount Sinai Global Health Program. Friday, January 17, 2014. And I gave a lecture on Wednesday discussing mobile technologies and their intersection with global health. We demoed Formhub. And talked about Drishti. We also talked about the importance of user centric design in mobile technology. The slides from our lecture are below:. Introduction to Clojure Presenation at Intent Media. Wednesday, November 27, 2013. The full presentation is available on github. The p...

salsahpc.org salsahpc.org

Spotlight |

http://www.salsahpc.org/content/spotlight

Skip to Main Content. BLOOMINGTON, Ind. - Twister, a new software tool released by Indiana University, supports faster execution of many data mining applications implemented as MapReduce programs. Developed by researchers from the Pervasive Technology Institute at IU, the tool extends the functionality of MapReduce, a distributed programming technique patented by Google for large-scale data processing in datacenter environments. Here are news of our spotlight, Twister :. Indiana University at Bloomington.

salsahpc.indiana.edu salsahpc.indiana.edu

Million Sequence Clustering

http://salsahpc.indiana.edu/millionseq

The goal of million sequence clustering was to find clusters for large sequence sets with around million sequences. We classified two data sets in this regard of which the details are given below. This set contained a total of 1160946 16S sRNA sequences where 684769 of them were unique. This set contained a total of 957387 fungi sequences where 482158 of them were unique and we considered the ones with lengths greater than 200 resulting 446041 sequences. Assign each remaining (the ones not in the sample ...

salsahpc.indiana.edu salsahpc.indiana.edu

Big Data for Science: July 26-30, 2010

http://salsahpc.indiana.edu/tutorial/index.html

Keynote: Data Intensive Computing. The Johns Hopkins University. Break (lunch for Eastern, Central time). Making the most of the I/O Software Stack. Break (lunch Mountain, Pacific time). Data movement & Storage (Data Capacitor WAN Filesystem). Scalable and Distributed Visualization using Paraview. Tutorial on using FutureGrid. Break (lunch for Eastern, Central time). Tutorial on MapReduce and Hadoop. Break (lunch Mountain, Pacific time). Introduction to Amazon EC2. Indiana University and IBM Research.

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资源 - Lei  Liu

http://cn.liuleics.com/3616428304.html

23398;术工作. 22823;规模机器学习资源:. 3/24/2013: RHadoop (R Hadoop). 1/22/2013: Vowpal wabbit (project to design a fast, scalable learning method by John Langford. Project home: http:/ hunch.net/ vw/. Video: http:/ videolectures.net/nipsworkshops2010 langford vow/. 1/22/2013: Large Scale Machine Learning Class from NYU (by Yann LeCun. Http:/ cilvr.cs.nyu.edu/doku.php? 1/15/2013: PEGASUS: A Peta-scale graph mining system. Http:/ www.cs.cmu.edu/ pegasus/what%20is%20pegasus.htm. 1/15/2013: Paralleled SVM. Http:/ deep...

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Twister: Iterative MapReduce

Subscribe to Twister Discussion Group. Twister version 0.9 is now available. Is a lightweight MapReduce runtime we have developed by incorporating these enhancements. Provides the following features to support MapReduce computations. ( Twister. Is developed as part of Jaliya Ekanayakes. PhD research and is supported by the S. Distinction on static and variable data. Configurable long running (cacheable) map/reduce tasks. Pub/sub messaging based communication/data transfers. Data access via local disks.

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