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.
Eliyar Asgarieh
My Civil Eng. Ph.D research identifies robust models for real-world civil structures, which heavily relied upon optimization and deterministic/stochastic simulations. In the initial stages of my Ph.D studies, I needed to apply various optimization methods using MATLAB toolboxes, which also required running a structural analysis software, OpenSees, for structural simulation. Each optimization could take more than three days, and hundreds of models needed to be designed and optimized (calibrated). Tufts HPC Cluster helped me to run many optimizations/simulations simultaneously, which would never be possible on regular desktops. The final part of my research was on identifying probabilistic models of structures in Bayesian framework using an advanced version of Markov chain Monte Carlo (MCMC) method called TMCMC (transitional MCMC). Each probabilistic model identification case required submission of 1000 jobs in several consecutive steps, which would add up to approximately 20000 to 30000 jobs in each case! To be computationally feasible the jobs needed to be run in parallel in each step. The cluster made the impossible possible for me, and I could run millions of jobs to finish my research on probabilistic model identification.
Giovanni Widmer
At Tufts Veterinary School of Medicine we are using Illumina technology to sequence PCR amplicons obtained from the bacterial 16S rRNA gene. The analysis of millions of short sequences obtained with this method enables us to assess the taxonomic composition of bacterial populations and the impact of experimental interventions. Some of these analyses are computer-intensive and running them on the cluster saves time. Typically, we use Clustal Omega to align sequences. On the cluster, a samples of a few thousand sequence reads can be aligned in a few minutes. We have also installed mothur on the cluster (mothur.org) and are running sequence analysis programs from this collection. These programs are used to de-noise sequence data and to compute pairwise genetic distance matrices. We visualize the genetic diversity of microbial populations using Principal Coordinate Analysis, which is also computer-intensive. We have adapted this approach to analyze populations of the eukaryotic pathogen Cryptosporidium. Several Cryptosporidium species infect the gastro-intestinal tract of human and animals. Using a similar approach as applied to the analysis of bacterial populations, we assess the diversity of Cryptosporidium parasites infecting a host and monitor the impact of various interventions on the genetic diversity of this parasites.
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