In this guest post, Fredrik Hellman, a PhD student at the Division of Scientific Computing, Department of Information Technology, Uppsala University, report on how cloud computing resources in SSC were used in recent work with collaborators at UU and Chalmers/GU.
In many engineering applications the probability of system failures are of particular interest. A special application is the assessment of storage capacity of underground carbon dioxoide storage reservoirs,where a failure is that the capacity of the target reservoir is smaller than expected. Since the rock properties are generally uncertain, the uncertainty in the reservoir capacity is also large.
The SNIC Science Cloud was used in our work on estimating failure probability to assess the performance of four different Monte Carlo method setups for estimating failure probability in a porous media fluid flow simulation with uncertain rock properties. For all four methods, the basic algorithm was to generate a set of realizations of the uncertain rock properties and distribute the work of performing the simulation for each realization in a network of virtual machines in the SNIC Science Cloud. All algorithms thus exhibit single program, multiple data (SPMD) parallelism.
The code performing the simulations was written in Python, using
finite element assembly routines from the FEniCS project. The project benefited from using a cloud based service mainly for two reasons. First, the virtualization allowed for good control over the software environment. Experimental versions of software could easily be used without administrative overhead. Second, the IPython based MOLNs software for setting up and managing a virtual computing network for distributed computations was readily available and simplified the management of the computations.