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Typical Cluster Usage at Tufts Faculty, Research Staff and students use this resource in support of a variety of research projects. |
To understand how the cluster supports research at Tufts, the following user comments show a wide range of applications. If you wish to contribute a short description of your cluster usage, please contact durwood.marshall@tufts.edu or lionel.zupan@tufts.edu.
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I am a grauate student with the High Energy Physics Group and as part of the MINOS experiment collaboration, I have been one of the main people responsible for mass event reconstruction using the Fermilab fixed-target farm. Earlier this year, a Mock Data Challenge was issued to the experiment in order to shake down reconstruction and analysis shortcomings before real data collection starts in January. This effort requested the generation of a rather large MonteCarlo sample, which was subsequently reconstructed at Fermilab. However, the generation of the MC sample was quite hard to setup at Fermilab, where space constraints, e-bureaucracy and competition with other experiments meant we would not be able to do it in a timely manner. That was when I decided to test the Tufts Linux Cluster to perform this task. I was setup with an area on the /cluster/shared space within a day of my original request, and after a few tests, I was able to generate 80% of the total necessary MC sample in less than a week. I was of course lucky to be almost the exclusive user of the cluster for that period, but I really had no problems setting things up and using it in what is seen as a nice success of the Tufts High energy Physics Group. Giving this success we have volunteered to become one of the spearheading institutions taking part on the upcoming MC generation effort which should start later this month, and the gained experience was transformed in a document and relayed to other institutions that are starting to run their own clusters and hope to join this effort. I have used the cluster a second time to do a customized reprocessing data for the CC nue analysis group, which I integrate, which required compilation in the cluster of the MINOS Offline Software, installation of a mysql database and assembling some shell scripts to handle the job output. That went quite well, and the full data sample was processed in 2 hours, with about 1 day of setup. Having worked for 2 years with the Fermilab batch farm, I was mainly impressed by the speed of the network connection of the CPU nodes to the I/O node, almost 20 times the Fermilab data transfer speeds and also by the great flexibility of use given to the users, which implied minimal
back and forth contact with the admins and dramatically improved work efficiency.
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.
Alexandre B. Sousa
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