Our Group integrates a series of lines of research, interrelated and developed in the context of High Performance Computing (HPC)

Research Lines

Considering HPC for Apps & Simulation, the main research lines focuses on two broad areas:

  1. HPC Technology
  2. Applications with Societal Impact

Detailed subjects:

Our project aims to provide solutions to the following problems, while it creates the corresponding technology that allows these solutions to be transferred:

  1. Performance and Efficiency in the use of HPC resources
    • Effect of the interconnection network on the performance of applications
    • Performance prediction. Scalability.
    • Efficient execution of applications: programming model, energy consumption, heterogeneous multicore.
  2. Availability of HPC resources (available to user)
    • Fault tolerance for HPC in numerical (technical-scientific) and transactional applications.
    • Integrity against attacks in use and/or access to the HPC resources (Vulnerability)
  3. Design and optimization of HPC systems, for “workloads” specific (application-specific domains)
    • CPU, network interconnection, I/O & Availability
    • Support tools (simulation)
  4. Social projection (impact) applications (solutions to problems of social impact -smart applications-, which require the capacity of HPC systems):
    • Simulation and optimization of Emergency Services in Hospitals (Smarter Health Services)
    • Simulation of Individuals oriented Models
      • HPCNelogo: Environment for the concurrent execution of ABM models under Netlogo-Behavior Space in an unattended/remote way on an HPC cluster using SGE (Github).
    • Simulation and optimization of movements of individuals in constrained environments (Smart Evacuations): Emergency Evacuations.


Dr. Elham Shojaei received the extraordinary doctoral award 2023 at the UAB Doctoral School. Congratulations!!

New project granted to HPC4EAS PhD candidate at CESGA.

For access to the resources of the Spanish Supercomputing Network (RES), the objectives of the project, reasons for the need for the activities and efficiency are analyzed. The researchers present their projects, which are evaluated by the Executive Commission of the BSC-CNS (http://www.bsc.es/RES).

In this third call, the RES has allocated resources for data services for the 2023-2027 period, specifically 2.14 PB for 2023; 2.91 PB for 2024; 3.61 PB for 2025, 1.30 PB for 2026 and 1.10 PB for 2027. Additionally, virtual machines and 1M CPU hours have been allocated to contribute to the exploitation of stored data.

PhD student Edixon Parraga (HPC4EAS member) with the project Modeling the file Input/Output behavior of parallel scientific applications and Machine Learning/Deep Learning in HPC systems has been granted with the following TB from 2023 onwards Project: 95, 130, 185, 185, 185 Backup: 5, 10, 15, 15, 15 and 50,000 hours of computing per year at CESGA.

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