  Nowadays simulations can produce petabytes of data to be stored in parallel
filesystems or large-scale databases. This data is accessed over the course of
decades often by thousands of analysts and scientists. However, storing these
volumes of data for long periods of time is not cost effective and, in some
cases, practically impossible. We propose to transparently virtualize the
simulation data, relaxing the storage requirements by not storing the full
output and re-simulating the missing data on demand. We develop SimFS, a file
system interface that exposes a virtualized view of the simulation output to
the analysis applications and manages the re-simulations. SimFS monitors the
access patterns of the analysis applications in order to (1) decide the data to
keep stored for faster accesses and (2) to employ prefetching strategies to
reduce the access time of missing data. Virtualizing simulation data allows us
to trade storage for computation: this paradigm becomes similar to traditional
on-disk analysis (all data is stored) or in situ (no data is stored) according
with the storage resources that are assigned to SimFS. Overall, by exploiting
the growing computing power and relaxing the storage capacity requirements,
SimFS offers a viable path towards exa-scale simulations.
