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I'm studying Computational Neuroscience in the Math department. My research consists of doing MANY simulations. That being said, I would not be able to do this research without the cluster! I simulate networks of thousands of neurons interacting. While there are some simulations that take a few minutes, the majority of them take 45 minutes to an 1.5 hours on one node. The last time I calculated, I'd like to run over a month's worth of these simulations. On top of this, I've run several very important simulations that take 1.5 days on 16 nodes. I had to run these simulations in order to try to reproduce results from Roger Traub's research. My current project is to try to explain these results. We tried to find a simpler way to explain them without reproducing the full model, but we found that we couldn't do it. With the cluster, I have been able to reproduce the results to the best of my ability. Furthermore, I've been able to dissect the model, and run many more simulations to get a much better understanding of what is going on in his results. I feel like I'm coming close to fully explaining the results, and have just presented a talk at BU explaining my ideas. None of this would have been possible without the cluster.

Casey Foote

My research for my MS in Mechanical Engineering is based on using the software available on the cluster to model a cold forging process. This model, paired with experimental data, will then be used to develop a tool to predict forging work piece cracking. The tool will provide a manufacturer of airfoils for use in the aircraft engine industry a method to rapidly develop new processing while avoiding costly physical trials.

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.

Alexandre B. Sousa

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.