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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".