it will work. But i want to avoid case of computing the median by sub
machines. So as to improve upon the complexity, i want the sub
machines to just take a random number k and split their data into two
sets < and > and then pass the information to host machine which after
having all the outputs of sub machines tells them to discard < or >
data on basis of something. I want this "something".

On Jul 17, 8:51 pm, Ashish Goel <ashg...@gmail.com> wrote:
> WOULDN'T MEDIAN OF MEDIANS WORK? mappers and reducers using hadoop
> Best Regards
> Ashish Goel
> "Think positive and find fuel in failure"
> +919985813081
> +919966006652
>
>
>
> On Sun, Jul 17, 2011 at 8:57 PM, Dumanshu <duman...@gmail.com> wrote:
> > Given billions of integers in a file. Memory Constraints exist so need
> > to parallelize. One solution can be to split the file over a 100
> > machines and each machine calculates median of their part using
> > quickselect and sends the result to host machine. Now the host
> > calculates median of these medians and asks the sub machines to
> > discard all the numbers less than this final median. So, map reduce!
>
> > Now i want to do something like this -
>
> > Each sub machine has billion/100 integers. It chooses an integer k
> > randomly and divides its integers into two parts, one is less than k
> > and other set is more than k (like quickselect algo). This machine
> > returns to the host basically 3 things, one is k, second and third is
> > number of elements in both sets.
> > After having all that data from submachines, the host does some
> > calculation and asks each machine to discard either the less than set
> > or more than set. then whole thing is repeated with lesser number of
> > elements.
>
> > Any ideas about how the host machine would do that and on what basis?
>
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