Parallel Performance and I/O Profiling of HPC RNA-Seq Applications

Authors

  • Lucas Cruz National Laboratory of Scientific Computing
  • Micaella Coelho National Laboratory of Scientific Computing
  • Marcelo Galheigo National Laboratory of Scientific Computing
  • Andre Carneiro National Laboratory of Scientific Computing
  • Diego Carvalho Federal Center for Technological Education Celso Suckow da Fonseca
  • Luiz Gadelha National Laboratory of Scientific Computing
  • Francieli Boito University of Bordeaux
  • Philippe Navaux Federal University of Rio Grande do Sul
  • Carla Osthoff National Laboratory of Scientific Computing
  • Kary Ocaña National Laboratory of Scientific Computing

DOI:

https://doi.org/10.13053/cys-26-4-4437

Keywords:

Supercomputing, sorkflow, RNA-seq

Abstract

Transcriptomics experiments are often expressed as scientific workflows and benefit from high-performance computing environments. In these environments, workflow management systems can allow handling independent or communicating tasks across nodes, which may be heterogeneous. Specifically, transcriptomics workflows may treat large volumes of data. ParslRNA-Seq is a workflow for analyzing RNA-Seq experiments, which efficiently manages the estimation of differential gene expression levels from raw sequencing reads and can be executed in varied computational environments, ranging from personal computers to high-performance computing environments with parallel scripting library Parsl. In this work, we aim to investigate CPU and I/O metrics critical for improving the efficiency and resilience of current and upcoming RNA-Seq workflows. Based on the resulting profiling of CPU and I/O data collection, we demonstrate that we can correctly identify anomalies of transcriptomics workflow performance that is an essential resource to optimize its use of high-performance computing systems.

Author Biography

Philippe Navaux, Federal University of Rio Grande do Sul

Informatics Institute

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Published

2022-12-25

Issue

Vol. 26 No. 4 (2022): 26(4) 2022

Section

Articles

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