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Related Experiment Videos

An Interface for Biomedical Big Data Processing on the Tianhe-2 Supercomputer.

Xi Yang1, Chengkun Wu2, Kai Lu3

  • 1College of Computer, National University of Defense Technology, Changsha 410073, China. yangxi1016@nudt.edu.cn.

Molecules (Basel, Switzerland)
|December 2, 2017
PubMed
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This summary is machine-generated.

Orion simplifies big data processing on the Tianhe-2 supercomputer by enabling applications to run via a single command. This interface optimizes resource allocation, making big data analytics on high-performance computing (HPC) more accessible and efficient.

Area of Science:

  • Computer Science
  • Data Science
  • High-Performance Computing

Background:

  • Convergence of big data, cloud computing, and high-performance computing (HPC).
  • Cloud platforms facilitate big data processing using frameworks like Hadoop and Spark.
  • China's growing HPC capacity presents opportunities for big data challenges.

Purpose of the Study:

  • Introduce Orion, a big data interface for the Tianhe-2 supercomputer.
  • Enable seamless execution of big data applications on HPC infrastructure.
  • Simplify the initiation of big data tasks through automated configuration.

Main Methods:

  • Developed Orion as a user-friendly interface for Tianhe-2.
  • Implemented an "allocate-when-needed" resource management strategy.
Keywords:
HadoopSparkTianhe-2big datagenomics big data

Related Experiment Videos

  • Tested Orion with a large genomic dataset using Hadoop/Spark applications.

    • Main Results:

    • Orion allows multiple users to launch multiple tasks efficiently.
    • Automated configuration minimizes user effort for initiating big data applications.
    • Satisfactory performance achieved on Tianhe-2 with minimal application modifications.

    Conclusions:

    • Orion offers a practical and economical solution for big data processing on Tianhe-2.
    • The interface enhances accessibility of HPC resources for big data analytics.
    • Facilitates efficient utilization of computational resources by avoiding idle occupation.