Re: [Cleveland-AI-ML-support-group] Map-Reduce Vs MPI Vs Shared memory

From: Timmy W.
Sent on: Friday, September 21, 2012 2:02 PM
Joe,

You may also be interested in pregel (graph-centric distributed processing):

http://www.royans...­

I'm using this implementation from CMU:

http://graphlab.o...­

It lends itself nicely to large scale graph analysis, and agent based modeling



On Fri, Sep 21, 2012 at 1:42 PM, Joe <[address removed]> wrote:
>  To perhaps get our discussion started a little early, here's a question on my mind. In the Big Data week 3 lectures, the professor says that Map-Reduce is a big improvement on MPI. So I read an MPI code example on wikipedia, and all it looks like is a way to communicate between threads running on different cores:
> http://en.wikiped...­
>
> Am I correct in thinking that's all there is to MPI? Since that looks like that's the case, maybe one could look at Map-Reduce as a kind of pattern for certain types of problems using the message passing approach. Are there other kinds of parallel patterns suitable to other kinds of parallel problems, or is Map-Reduce (and cuda for graphics) the only general one (for a certain class of problems) created yet?
>
>  In reading about the Shared Memory approach to parallel programing, it also seemed to be nothing but a way of passing data around. MPI is a serialized passing of data between different processes, and SM is a way to pass larger chunks of data, is that all there is to these two parallel programming methods? Apparently before Map-Reduce, there was no general way of writing (none cuda problem oriented) parallel algos, just very primitive standardized ways of passing data around, which one used to make totaly custom programs every time.
>
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