To understand how the cluster supports research at Tufts, the following user comments shows a wide range of applications. If you wish to contribute a short description of your usage, please contact durwood.marshall@tufts.edu or lionel.zupan@tufts.edu.
Eugene Morgan
The Tufts linux cluster allows me to work with large amounts of data within a reasonable time frame. I first used the cluster to interpolate sparse data points over a fairly large 3-dimensional space. The cluster has also dramatically sped up the calculation of semivariance for dozens of sections of seafloor containing vast numbers of data points, quickly performed thousands of Monte Carlo simulations, and computed statistics on one of the largest global wind speed datasets containing ~3.6 billion data points. I have most recently used the cluster find optimal parameters for rock physics equations using a genetic algorithm. Most of these activities have been or will be incorporated in technical publications.
Eric Thompson
We have used the Tufts Linux Cluster to further our understanding of
the seismic response of near-surface soils. This behavior, often
termed "site response," can often explain why locations heavily
damaged by an earthquake are frequently observed adjacent to undamaged
locations. Standard modeling procedures often fail to accurately model
this behavior. The failure of these models is often attributed to the
uncertainty of the soil properties. However, using the Tufts Linux
Cluster we have shown that the underlying theoretical assumptions of
the standard model (vertically incident plane SH-wave propagation
through a laterally constant medium) are responsible for the failure
to match the observed site response behavior.
Andrew Margules
The research that I am currently conducting is in the area of Passively Actuated Deformable Airfoils. The largest presence of airfoils today is contained within the aerospace and transportation industries. Like those on commercial and military aircraft, the basic teardrop airfoil shape is augmented with a series external structures which aid in take-off, landing, and cruising flight. While they perform specific and important functions, they add additional weight to a system which is highly immersed in weight management. What my research is looking into, is try find a way to develop an internal structure for an airfoil that would provide similar shape change, without the added external mechanisms. To do this, I am using two different computational software packages. COMSOL Multiphysics allows for the examination of the fluid-structure interaction of the airfoil and moving air. Using different internal rib structures, a goal of finding an appropriate structure is hoped to be achieved. In addition, I am using the computational fluid dynamics package Fluent to help visualize velocity and pressure fields over deformed and undeformed airfoil shapes. If this software was not available through the academic research cluster, this research would extremely slow process. The governing physics behind these simulations is complex enough that without the computing power of the cluster, I do not believe that we would be able to perform it. In the last twenty or so years, a focus has shifted from passive actuation to active actuation. Hopefully, this research will help to launch a renewed interested in this field.
Ke Betty Li
I am a researcher in the Department of Civil and Environmental Engineering.
Our research focuses on the investigation of how various contaminants affect
the ground water quality and how we could design remediation systems. An
important approach we are using for this type of investigation is modeling
contaminant fate and transport in the subsurface on computers. The resources
provided by Tufts Cluster Center are very important to us. Our simulations
usually take days or even weeks on a single CPU. The clusters can either
expedite each simulation if we use simulators that enable parallel
computing, or allow us to simulate multiple serial processes simultaneously.
The significant improvement in computing efficiency is critical for us to
commit work quality to funding sponsors. We expect that our work will
improve the cuurent understanding of contamination in the subsurface,
provide cutting-edge assessment tools, and stimulate innovative treatment
technologies.
Eric Miller
Our work concerns the development of tomographic processing methods for environmental remediation problems. Specifically, we are interested in using electrical resistance tomography (ERT) to estimate the geometry of regions of the subsurface contaminated by chemicals such as TCE or PCE. Though the concept of ERT is not unlike the more familiar computed axial tomography (CAT) used for medical imaging, the physics of ERT are a bit more complicated thereby leading to computationally intensive methods for turning data into pictures. Luckily these computational issues are, at a high level, easily parallelizable. Thus, we have turned to Star-P as the tool of choice for the rapid synthesis of our algorithms.
Michael A. Simon
Nonlinear dynamic modeling of Lepidopteron mechanosensors
The Trimmer Lab is interested in the control of locomotion and other movements in soft bodied animals. I have been analyzing the activity of a specific mechanosensor trying to understand how it influences abdominal movement, a critical question for animals with no rigid components. One particularly powerful analytical tool for analyzing such sensors is nonlinear analysis using Gaussian white noise as a stimulus. One challenge of this technique, however, is that it is computationally complex. Even storing the matrices involved in these computations is beyond the capabilities of the typical personal computer. The Tufts Linux Research Cluster offers me the resources necessary to run these computations and analyze the results without needing to invest in new, complicated, or expensive analytical hardware or software. It also allows me to use software that would have been difficult to acquire for our lab, alone. Without this resource, following this line of inquiry would have proved a costly endeavor, possibly prohibitively so. We hope to apply our results to the development of computer and robotic models, with the eventual goal of designing a soft robot, a groundbreaking engineering application with substantial implications for design in the biomedical engineering arena, as well as in other areas of engineering.
Katherine L. Tucker
Use of the Bioinformatics cluster has been invaluable to our research. We use a genetic analysis software named SOLAR which is Linux/Unix based. This software and the methods used in it are cutting edge. We are able to perform varous genetic computations with ease. In the past some student have had to do these calculations by hand because of a lack of access to such software. However, hand calculations are only possible for small sample sizes and simple genetic analysis. Our current work with Solar includes over 5,000 individuals and we are using some of the most advanced methods available. The cluster allows us to do large computational runs that would not be otherwise possible. Thus, our current work would not have been able without access to SOLAR on the bioinformatics cluster. In addition, this type of analysis is being more common and will be a greater part of our efforts in future years. Use of the bioinformatics cluster helps our research to remain competitive and important in our grant application process. Our lab is the first to use SOLAR on the bioinformatics cluster, however, since we have been using it, many labs have inquired about how to gain access. I sincerely thank you for your work in helping us gain access to the software and the service you have provided through the Bioinformatics cluster.
Jeffery S. Jackson
I am a grad student in Mechanical Engineering and I am conducting research on microfluidic mixers. I use the Cluster01 to create and run fluid flow models on COMSOL Multiphysics. The COMSOL program solves the Navier Stokes equations for transient fluid flow and the convection diffusion equation. For the models that I create to be accurate, though, they require more elements and time steps than my computer, or the computers in the EPDC, can handle. This is where the cluster comes in very handy. I usually have the Cluster run any model that is more complicated than a 2D model with 30,000 elements. The most complicated model I have had the cluster solve consisted of 90,000 elements. This model took 30 hours for the Cluster to solve, which is something that no other computer resource I have access to could do. Another nice benefit of the Cluster is being able to use it from home. I live in Providence, RI and it takes me two hours to get to Tufts by train. So, I only come in when I have to. Having remote access to the Cluster makes this possible. Without the Cluster, or the very helpful people who provide excellent technical support, I would never have been able to do the research I needed to to finish my Master's Thesis.
Erin Munro
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.