mapreduce vs other parallel processing solutions

Question

So, the questions are: 1. Is mapreduce overhead too high for the following problem? Does anyone have an idea of how long each map/reduce cycle (in Disco for example) takes for a very light job? 2. Is there a better alternative to mapreduce for this problem?

In map reduce terms my program consists of 60 map phases and 60 reduce phases all of which together need to be completed in 1 second. One of the problems I need to solve this way is a minimum search with about 64000 variables. The hessian matrix for the search is a block matrix, 1000 blocks of size 64x64 along a diagonal, and one row of blocks on the extreme right and bottom. The last section of : block matrix inversion algorithm shows how this is done. Each of the Schur complements S_A and S_D can be computed in one mapreduce step. The computation of the inverse takes one more step.

From my research so far, mpi4py seems like a good bet. Each process can do a compute step and report back to the client after each step, and the client can report back with new state variables for the cycle to continue. This way the process state is not lost computation can be continued with any updates. http://mpi4py.scipy.org/docs/usrman/index.html

This wiki holds some suggestions, but does anyone have a direction on the most developed solution: http://wiki.python.org/moin/ParallelProcessing

Thanks !

Answer 1

MPI is a communication protocol that allows for the implementation of parallel processing by passing messages between cluster nodes. The parallel processing model that is implemented with MPI depends upon the programmer.

I haven't had any experience with MapReduce but it seems to me that it is a specific parallel processing model and is designed to be simple to implement. This kind of abstraction should save you programming time and may or may not provide a suitable solution to your problem. It all depends on the nature of what you are trying to do.

The trick with parallel processing is that the most suitable solution is often problem specific and without knowing more specifics about your problem it is hard to make recommendations.

If you can tell us more about the environment that you are running your job on and where your program fits into Flynn's taxonomy, I might be able to provide some more helpful suggestions.