Visible to the public Towards a Science Gateway for Bioinformatics: Experiences in the Brazilian System of High Performance Computing

TitleTowards a Science Gateway for Bioinformatics: Experiences in the Brazilian System of High Performance Computing
Publication TypeConference Paper
Year of Publication2019
AuthorsOcaña, Kary, Galheigo, Marcelo, Osthoff, Carla, Gadelha, Luiz, Gomes, Antônio Tadeu A., De Oliveira, Daniel, Porto, Fabio, Vasconcelos, Ana Tereza
Conference Name2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)
KeywordsBioinfoPortal, Bioinformatics, bioinformatics applications, Brazilian HPC resources, Brazilian National HPC System, Brazilian system, cloud computing, compositionality, data management resources, High performance computing, learning (artificial intelligence), machine learning, parallel processing, parallel/distributed executions, Predictive Metrics, pubcrawl, resilience, Resiliency, SaaS model, science gateway, Scientific Computing Security, Software as a service, workflow management software, workflow management systems
Abstract

Science gateways bring out the possibility of reproducible science as they are integrated into reusable techniques, data and workflow management systems, security mechanisms, and high performance computing (HPC). We introduce BioinfoPortal, a science gateway that integrates a suite of different bioinformatics applications using HPC and data management resources provided by the Brazilian National HPC System (SINAPAD). BioinfoPortal follows the Software as a Service (SaaS) model and the web server is freely available for academic use. The goal of this paper is to describe the science gateway and its usage, addressing challenges of designing a multiuser computational platform for parallel/distributed executions of large-scale bioinformatics applications using the Brazilian HPC resources. We also present a study of performance and scalability of some bioinformatics applications executed in the HPC environments and perform machine learning analyses for predicting features for the HPC allocation/usage that could better perform the bioinformatics applications via BioinfoPortal.

DOI10.1109/CCGRID.2019.00082
Citation Keyocana_towards_2019