The increasing amount of heterogeneous data Big Data needed for that endeavor typically requires large computational and storage resources.
What constrained progress was the lack of data and access to large computational facilities.
Talented people are attracted to Google because we empower them to change the world; Google has large computational resources and distribution that enables individuals to make a difference.
However, this approach requires large computational calculations.
However its application involves large computational costs.
This optimization yields large computational savings.
Computational grids offer a distributed environment to divide up a large computational task among individual machines.
It is hoped that these relations will prove useful in engineering or computational endeavors where the speed and/or ease of their use may be advantageous.
This is not a new question; it comes out of the overall recent statistical and computational endeavors that focus on feature selection from massive and highly dimensional genomic data.
Even the humblest home machine can become a cog in a larger computational wheel, receiving data, churning through it at its own speed and returning the results.
