About the lab

The GRyCAP focuses on the application of the facets of distributed computing (e.g. Cloud computing, High-Performance Computing, Grid computing, Serverless computing, etc.) to different scientific areas that include, but are not limited to biomedicine, medical imaging and structural buildings. The GRyCAP has extensive expertise in the development of open-source software for Distributed Computing Infrastructures. It participates in large-scale European H2020 projects and has extensive experience in the leadership of Brazil-European projects.

The GRyCAP also has significant training capacities in the area of Cloud computing, actively leading subjects in the Master’s Degree in Parallel and Distributed Computing and in the Master’s Degree in Big Data Analytics.

Featured projects (4)

Project
In this context, INDIGO-DataCloud (INtegrating Distributed data Infrastructures for Global ExplOitation), a project funded under the Horizon2020 framework program of the European Union, aims at developing a data and computing platform targeting scientific communities, deployable on multiple hardware and provisioned over hybrid (private or public) e-infrastructures. By filling existing gaps in PaaS and SaaS levels, INDIGO-DataCloud will help developers, resources providers, e-infrastructures and scientific communities to overcome current challenges in the Cloud computing, storage and network areas.
Project
EUBra-BIGSEA will provide an Integrated, Elastic and Dynamic Big Data Cloud Platform to address Knowledge Discovery by tackling Data Volume, Variety, Velocity and Veracity issues as well as privacy, security and QoS challenges.
Project
The key concept proposed in the DEEP Hybrid DataCloud project is the need to support intensive computing techniques that require specialized HPC hardware, like GPUs or low latency interconnects, to explore very large datasets.
Project
The overarching objective of EUBrazil Cloud Connect is to drive cooperation between Europe and Brazil by strengthening the scientific and knowledge-based society as key to sustainable and equitable socioeconomic development. The core of this collaboration is defined through the scientific uses cases selected, which will require the collaboration between Brazil and Europe in the provision of data, services and expertise. None of the 3 use cases could feasibly be taken forward without the cooperation between Brazilian and European entities and without the availability of computing and data resources.

Featured research (1)

This paper introduces a platform to support serverless computing for scalable event-driven data processing that features a multi-level elasticity approach combined with virtualization of GPUs. The platform supports the execution of applications based on Docker containers in response to file uploads to a data storage in order to perform the data processing in parallel. This is managed by an elastic Kubernetes cluster whose size automatically grows and shrinks depending on the number of files to be processed. To accelerate the processing time of each file, several approaches involving virtualized access to GPUs, either locally or remote, have been evaluated. A use case that involves the inference based on deep learning techniques on transthoracic echocardiography imaging has been carried out to assess the benefits and limitations of the platform. The results indicate that the combination of serverless computing and GPU virtualization introduce an efficient and cost-effective event-driven accelerated computing approach that can be applied for a wide variety of scientific applications.

Lab head

Ignacio Blanquer
Department
  • Institute for Molecular Imaging Technologies (I3M)

Members (12)

Germán Moltó
  • Universitat Politècnica de València
José M. Alonso
  • Universitat Politècnica de València
Miguel Caballer
  • Universitat Politècnica de València
Damià Segrelles
  • Universitat Politècnica de València
Carlos De Alfonso
  • Universitat Politècnica de València
Fernando Alvarruiz
  • Universitat Politècnica de València
Amanda Calatrava
  • Universitat Politècnica de València
David Arce
  • Universitat Politècnica de València