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
ABOUT ARC
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.
PUBLICATIONS
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
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Castillo, O., Mancillas, J., Hughes, W., & Brancaleon, L. (2022). Characterization of the interaction of metal-protoporphyrins photosensitizers with β- lactoglobulin. Biophysical Chemistry, 106918. https://doi.org/10.1016/j.bpc.2022.106918
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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. https://doi.org/10.1080/07391102.2020.1817785
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Luis Montaño-Priede, J., & Large, N. (2022). Photonic band structure calculation of 3D-finite nanostructured supercrystals. Nanoscale Advances, 4(21), 4589–4596. https://doi.org/10.1039/D2NA00538G
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Montaño-Priede, J. L., Mlayah, A., & Large, N. (2022). Raman energy density in the context of acoustoplasmonics. Physical Review B, 106(16), 165425. https://doi.org/10.1103/PhysRevB.106.165425
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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. https://doi.org/10.1063/5.0067449
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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. https://doi.org/10.1021/acsapm.2c01132