High-Performance Computing (HPC) and its Applications: Iberogun Cluster – A development perspective from the Universidade Tecnológica de Panamá (UTP)
Bibliographic record
Abstract
The challenges in advancing research development in various scientific fields and the need to strengthen research at the Universidad Tecnológica Panamá (UTP) led to the creation of the IBEROGUN supercomputing platform. This initiative aims to expand and strengthen UTP's high-performance computing infrastructure through projects funded by SENACYT, including EIE18-16 and FID16-275. The primary goal is to boost research development that requires high computational demand. It aims to position UTP and Panama at the forefront of international research involving high-performance computing (HPC) in environmental, climate change, water resources, and industrial applications. The IBEROGUN HPC cluster incorporates an NVIDIA DGX A100 GPU, ASUS servers, and Volta and Titan graphic cards. It has supported projects on atmospheric climate variability and indentation-induced plasticity in CuZr glasses and is currently aiding hydrological modeling of the Upper Chagres River watershed. Using the AceCAST model, the Iberogun cluster improved precipitation resolution and reduced processing time for weather forecasting. Molecular dynamics simulations of CuZr glasses revealed a correlation between plasticity and stoichiometry. Hydrological modeling of the Upper Chagres River is being conducted to determine the impacts of Climate Change and anthropogenic activities on this vital watershed. The IBEROGUN cluster has enhanced Panama's research capabilities in high-computational demand scientific areas. Accessible HPC technology is crucial for advancing scientific production, especially in developing countries. Ensuring an effective system for user access to the cluster is vital. The authors thank SNI-SENACYT for financial support, NVIDIA for donating a Titan graphic card, and CoCeCAR for technical support.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".