Arc is UTSA's high-performance computing cluster and is maintained by Tech Solutions’ (UTS) Research Computing Support Group (RCSG). RCSG provides technical support for Arc, which includes:

  • Supporting hardware, operating system, and applications
  • Troubleshooting performance issues, system errors, and more


Arc comprises:
  • 167 total compute/GPU nodes and 2 login nodes, the majority of these are Intel Cascade Lake CPUs and some are AMD EPYC CPUs
  • 30 GPU nodes - each containing two CPUs with 20 cores each for a total of 40 cores, 384GB RAM, and each including one V100 Nvidia GPU accelerator
  • 5 GPU nodes - each containing two CPUs with 20 cores each for a total of 40 cores, 384GB RAM, and each including two V100 Nvidia GPU accelerators
  • 2 GPU nodes - each containing two CPUs and 4 V100 GPUs, and 384 GB RAM
  • 2 GPU nodes - each having two AMD EPYC CPUs and having one A100 80 GB GPU, and 1 TB RAM
  • 2 large-memory nodes, each containing four CPUs with 20 cores each for a total of 80 cores, and each including 1.5TB of RAM
  • 1 large-memory node, equipped with two AMD EPYC CPUs and 2 TB of RAM
  • 1 node equipped with two AMD EPYC CPUs and having 1 TB of RAM
  • 5 nodes - each equipped with two AMD EPYC CPUs and 1 NEC vector engine and 1 TB of RAM
  • 100Gb/s Infiniband connectivity
  • Two Lustre filesystems: /home and /work, where /home has 110 TBs capacity and /work has 1.1 PB of capacity
  • A cumulative total of 250TB of local scratch (approximately 1.5 TB of /scratch space on most compute/GPU nodes)

Software installation can be requested by submitting a ticket via ServiceNow.


Please remember to cite/acknowledge the use of UTSA's HPC cluster in any publications, papers, reports, etc. Wording should be as stated below:

"This work received computational support from UTSA’s HPC cluster, operated by University Tech Solutions."

Publications Supported by the Research Computing Infrastructure at UTSA

  • Castillo, O., Mancillas, J., Hughes, W., & Brancaleon, L. (2022). Characterization of the interaction of metal-protoporphyrins photosensitizers with β- lactoglobulin. Biophysical Chemistry, 106918.

  • Fenner, K., Redgate, A., & Brancaleon, L. (2022). A 200 nanoseconds all-atom simulation of the pH-dependent EF loop transition in bovine β-lactoglobulin. The role of the orientation of the E89 side chain. Journal of Biomolecular Structure and Dynamics, 40(1), 549–564.

  • Luis Montaño-Priede, J., & Large, N. (2022). Photonic band structure calculation of 3D-finite nanostructured supercrystals. Nanoscale Advances, 4(21), 4589–4596.

  • Montaño-Priede, J. L., Mlayah, A., & Large, N. (2022). Raman energy density in the context of acoustoplasmonics. Physical Review B, 106(16), 165425.

  • Padilla, L. A., León-Islas, A. A., Funkhouser, J., Armas-Pérez, J. C., & Ramírez-Hernández, A. (2021). Dynamics and phase behavior of two-dimensional size-asymmetric binary mixtures of core-softened colloids. The Journal of Chemical Physics, 155(21), 214901.

  • Salinas-Soto, C. A., Leon-Islas, J. G., Herrera-Alonso, M., & Ramírez-Hernández, A. (2022). Hydrophobic Solute Encapsulation by Amphiphilic Mikto-Grafted Bottlebrushes: A Dissipative Particle Dynamics Study. ACS Applied Polymer Materials, 4(10), 7340–7351.