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High Performance Computing



Additional Research Computing Services



HPC Training
HPC Support FAQ

SHAMU is one of UTSA's high performance computational clusters and is maintained by University Technology Solutions (UTS) Research Computing Support Group (RCSG). RCSG provides performance solutions to Shamu that includes:


  • Support hardware, operating system, and applications
  • Troubleshoot performance issues, system errors, etc 


   Requesting Accounts on Shamu



About SHAMU:



HPC can be used to:

  • Develop and redesign products
  • Optimize production and delivery processes
  • Analyze or develop large datasets
  • Conduct large-scale research projects
  • Store large amounts of data for future analysis
  • Perform consumer trend monitoring, searching or profiling
  • Create computer visualizations that explain research results
  • Carry out simulations and / or modelling of complex processes

Available Software on SHAMU


Major applications include:

  • Data storage and analysis
  • Data mining
  • Simulations
  • Modelling
  • Software development
  • Visualization of complex data
  • Rapid mathematical calculations 

      * additional software can be installed by contacting RCSG *






Please remember to acknowledge the use of Shamu in any publications, papers, reports, etc. Wording should be as stated below:

"This work received computational support from UTSA’s HPC cluster SHAMU, operated by the Office of Information Technology."


second harmonic generation theory figure           figure of a hybrid metallic semiconductor nano structure
 Figure representing the second harmonic generation polarization dependence for two gold nanocrescents by Dr. Nicholas Large    Figure showing a hybrid metallic-semiconductor nanostructure (gold nanodisks covered by a monlayer of MoSe2) by Dr. Nicholas Large.


Publications Using SHAMU:

Olufunso, O.,  Giacomoni, M. (2017). Enhancing the performance of multiobjective evolutionary algorithm for sanitary sewer overflow reduction.  Journal of Water Resources Management and Planning, 143(7). doi:10.1061/(asce)wr.1943-5452.0000774

Itaquy, B., Olufunso, O.,  Giacomoni, M. (2017). Application of multi-objective genetic algorithm to reduce wet weather sanitary sewer overflows and surcharge. Journal of Sustainable Water in the Built Environment, 3. doi:10.1061/jswbay.0000826

Maiti, A., Maity, A., Satpati, B., Large, N., & Chini, T.K. (2016). Efficient excitation of higher order modes in the plasmonic response of individual concave gold nanocubes. Journal of Physics and Chemistry, 12, 731-740. doi:10.1021/acs.jpcc.6b11018

Bigdely-Shamlo, N.,Touryan, J., Ojeda, A., Kothe, C., Mullen, T., & Robbins, K. (2019). Automated EEG mega-analysis I: Spectral and amplitude characteristics across studies. Neuroimage, 203, pp. 116361. doi: 10.1016/j.neuroimage.2019.116361

Bigdely-Shamlo, N.,Touryan, J., Ojeda, A., Kothe, C., Mullen, T., & Robbins, K. (2019). Automated EEG mega-analysis II: Cognitive aspects of event related features. NeuroImage, 203, pp. 116054. doi: 10.1016/j.neuroimage.2019.116054



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