Hadoop for grid computing Background:
I run a product that compares sets of data (data matching and data reconciliation).
To get the result we need to compare each row in a data set with every N rows on the opposing data set
Now however we get sets of up to 300 000 rows of data in each set to compare and are getting 90 Billion computations to handle.

So my question is this:
Even though we dont have the data volumes to use Hadoop, we have the computational need for something distributed. Is Hadoop a good choice for us?
Your job seems like a map-reduce job and hence might be good for Hadoop. Hadoop has a zoo of an ecosystem though. 

Hadoop is a distributed file system. It distributes data on a cluster and because this data is split up it can be analysed in parallel. Out of the box, Hadoop allows you to write map reduce jobs on the platform and this is why it might help with your problem.

The following technologies work on Hadoop: 


If the data can be represented in a table format you might want to check out technologies like hive and impala. Impala uses all the distributed memory across a cluster and is very performant while it allows you to still work with a table structure. 
A more new, but promising alternative might also be spark which allows for more iterative procedures to be run on the cluster. 


Don't underestimate the amount of time setting up and the amount of time needed to understand Hadoop.