11 citations · 32 across the 7 of their papers we have counts for
6 papers · 1 filter
RCOMPSs: A Scalable Runtime System for R Code Execution on Manycore Systems
Xiran Zhang, Javier Conejero, Sameh Abdulah +5
R has become a cornerstone of scientific and statistical computing due to its extensive package ecosystem, expressive syntax, and strong support for reproducible analysis. However,…
Parallel Reduced Order Modeling for Digital Twins using High-Performance Computing Workflows
S. Ares de Parga, J. R. Bravo, N. Sibuet +9
The integration of reduced-order models (ROMs) with high-performance computing (HPC) is critical for developing digital twins, particularly for real-time monitoring and predictive…
Portability and Scalability Evaluation of Large-Scale Statistical Modeling and Prediction Software through HPC-Ready Containers
Sameh Abdulah, Jorge Ejarque, Omar Marzouk +5
HPC-based applications often have complex workflows with many software dependencies that hinder their portability on contemporary HPC architectures. In addition, these applications…
The BioExcel methodology for developing dynamic, scalable, reliable and portable computational biomolecular workflows
Jorge Ejarque, Pau Andrio, Adam Hospital +4
Developing complex biomolecular workflows is not always straightforward. It requires tedious developments to enable the interoperability between the different biomolecular simulati…
Towards Enabling I/O Awareness in Task-based Programming Models
Hatem Elshazly, Jorge Ejarque, Francesc Lordan +1
Storage systems have not kept the same technology improvement rate as computing systems. As applications produce more and more data, I/O becomes the limiting factor for increasing…
An Oracle for Guiding Large-Scale Model/Hybrid Parallel Training of Convolutional Neural Networks
Albert Njoroge Kahira, Truong Thao Nguyen, Leonardo Bautista Gomez +3
Deep Neural Network (DNN) frameworks use distributed training to enable faster time to convergence and alleviate memory capacity limitations when training large models and/or using…