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User-friendly scheduler Using a hybrid architecture and supercomputing for big data processing.

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This study introduces a new scheduler for bioinformatics workflows, optimizing data processing and resource use. It enhances efficiency for omics data analysis on diverse computing environments.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Omics data is growing exponentially, necessitating advanced solutions for data management.
  • Current bioinformatics workflows face challenges in storage, transfer, and processing.
  • Efficient resource utilization in high-performance computing (HPC) is crucial for large-scale data analysis.

Purpose of the Study:

  • To develop a novel scheduler for optimizing bioinformatics workflows.
  • To enable transparent mapping of workflow steps to diverse execution environments, including HPC clusters.
  • To improve resource utilization and reduce the cost and time for omics data processing.

Main Methods:

  • Utilized the Temporal.io workflow framework for scheduler development.
  • Implemented a graphical user interface (GUI) for user-friendly workflow management.
  • Developed a universal, platform-agnostic JSON representation for workflows.
  • Created a custom executor plugin for translating workflows (e.g., Nextflow) to the JSON format.
  • Enabled asynchronous workflow execution for better resource utilization.

Main Results:

  • The scheduler successfully mapped workflow steps to HPC resources via SLURM.
  • Asynchronous execution ensured reasonable resource utilization.
  • The universal JSON format abstracted platform-specific details.
  • Benchmarking on a bulk RNA sequencing workflow demonstrated reduced cost and time.
  • The GUI facilitated ease of use for non-technical users.

Conclusions:

  • The proposed scheduler offers significant optimizations for bioinformatics workflows.
  • The cross-platform approach enhances accessibility and efficiency in omics data analysis.
  • This method effectively addresses the challenges posed by the exponential growth of omics data.