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Aurelie Edwards

My graduate student Christopher Mooney performs simulations of unsteady, turbulent fluid flow in a bioreactor with a stir-bar, using Femlab engineering software. Prior to having access to the Tufts cluster, he was experiencing extensive memory usage problems. On a PC with 2GB of RAM using Windows XP, he was only able to
access about 40% of the memory, due to fragmentation issues, and his simulations did not converge. We were both relieved to learn that we could have access to the
Tufts cluster and its Linux platform that offers 4GB+ of memory space. The latter has thankfully allowed us to solve increasingly complex models. For example, using his PC, Chris could solve finite element Navier-Stokes fluid flow problems with an element mesh density that limited the problem to about 100,000 degrees of freedom, beyond which he ran out of memory. He often received "low mesh quality" error messages that hindered the mathematical convergence of the solution. On the cluster, he now has enough memory to refine the mesh and run models with 300,000 degrees of freedom. Chris still runs into "out of memory" problems on the cluster, but much less frequently. The technical staff at Femlab, when told of the kinds of problems we envision solving in the coming years, suggested using a server with 10 to 16GB of memory space to run these models with adequate mesh resolution. In other words, if you were to increase the capacity of the Tufts cluster, we would be takers!

Gabriel Wachman

I use the cluster to conduct experiments relating to my work in machine learning. I am in the computer science department. The experiments I have been running have generally been to aid in the comparison of different learning algorithms. By running many experiments over a range of parameters, I can collect data that helps me to draw conclusions on the behavior of the algorithms. Without the cluster, much of the work I have done would have been impossible or at best severely limited.

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