  Accurate estimation of the run time of computational codes has a number of
significant advantages for scientific computing. It is required information for
optimal resource allocation, improving turnaround times and utilization of
science gateways. Furthermore, it allows users to better plan and schedule
their research, streamlining workflows and improving the overall productivity
of cyberinfrastructure. Predicting run time is challenging, however. The inputs
to scientific codes can be complex and high dimensional. Their relationship to
the run time may be highly non-linear, and, in the most general case is
completely arbitrary and thus unpredictable (i.e., simply a random mapping from
inputs to run time). Most codes are not so arbitrary, however, and there has
been significant prior research on predicting the run time of applications and
workloads. Such predictions are generally application-specific, however. In
this paper, we focus on the Gaussian computational chemistry code. We
characterize a data set of runs from the SEAGrid science gateway with a number
of different studies. We also explore a number of different potential
regression methods and present promising future directions.
