have a look at the various machine learning applications of Map Reduce: they do lots of computations and here, the data corresponds to intermediate values being used to update counts etc.
bedtime reading: Mahout: (machine learning under Hadoop) http://lucene.apache.org/mahout/ some machine learning papers: Fully Distributed EM for Very Large Datasets. Jason Wolfe, Aria Haghighi and Dan Klein www.cs.berkeley.edu/~aria42/pubs/icml08-distributedem.pdf another one: www.cs.stanford.edu/people/ang//papers/nips06-mapreducemulticore.pdf Miles 2008/9/4 Tenaali Ram <[EMAIL PROTECTED]>: > Hi, > > I am new to hadoop. What I have understood so far is- hadoop is used to > process huge data using map-reduce paradigm. > > I am working on problem where I need to perform large number of > computations, most computations can be done independently of each other (so > I think each mapper can handle one or more such computations). However there > is no data involved. Its just number crunching job. Is it suited for Hadoop > ? > > Has anyone used hadoop for merely number crunching? If yes, how should I > define input for the job and ensure that computations are distributed to all > nodes in the grid? > > Thanks, > Tenaali > -- The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336.
