Workshop Report - Pervasive Technology Institute - Indiana University

Dr. Richard Durisen is currently using the Data Capacitor and the TeraGrid to simulate gravitational
instabilities in protoplanetary discs to study the origins of gas giant planets. Durisen’s research team
uses Pople, a shared-memory system at PSC as well as NCSA’s Cobalt to produce more than 50 TBs
of simulation data. The second part of their workflow is to perform an analysis of the simulation
data produced. This step was performed at Mississippi State University on their Raptor cluster.
By using the Data Capacitor to bridge IU, PSC, and NCSA, Durisen’s team can see results as if
they were happening locally. Should there be a problem with the run or the input parameters it
is possible to stop the simulation and correct the problem without wasting valuable CPU time.
Additionally, being able to see the simulation unfold can give the researcher insight into the next
run (or set of runs) to be performed.
By using the Data Capacitor to bridge IU and MSU, Durisen’s team was able to take advantage of
some free compute cycles that were made available. Normally, this would require the researcher to
transfer the data between sites, but the Data Capacitor made this step unnecessary.
Conclusion
Using the Data Capacitor as a central filesystem has permitted data sharing across distance and
simplified workflows that would have previously been more complex and involved significant data
transfer. When data is centrally located it can make data management significantly easier.
The Data Capacitor also provides a resource for storing large data sets that might overwhelm a
smaller institution. For example, the scratch filesystem at MSU was 5TB in size. Durisen’s team
would have had to fill that filesystem 10 times over to complete their analysis.
One might expect significant performance degradation using a wide area filesystem, however
this was not always the case. In each case where data was produced across distance there was no
performance differential between local and remote mounts. In the case where data was analyzed at
MSU, there was a difference between the local and remote filesystems of 40%. However, this does
not take into account the considerable amount of time that would be required to transfer 50 TB of
data to the local filesystem.
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