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Sputnik: ad hoc distributed computation.

Gunnar Völkel1, Ludwig Lausser2, Florian Schmid2

  • 1Core Unit Medical Systems Biology, Theoretical Computer Science, Ulm University, D-89069 Ulm, Germany and Leibniz Institute for Age Research-Fritz Lipmann Institute and FSU Jena, D-07745 Jena Core Unit Medical Systems Biology, Theoretical Computer Science, Ulm University, D-89069 Ulm, Germany and Leibniz Institute for Age Research-Fritz Lipmann Institute and FSU Jena, D-07745 Jena.

Bioinformatics (Oxford, England)
|December 16, 2014
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Summary
This summary is machine-generated.

The Sputnik framework enables efficient, ad hoc distributed computation using Java, requiring no admin rights and fully utilizing CPU cores. This simplifies complex bioinformatic analyses, demonstrated here with microarray data feature selection.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Distributed Computing

Background:

  • Computational demands in bioinformatics necessitate parallelization for faster processing.
  • Setting up distributed computing environments is often complex and time-consuming.
  • Existing solutions may require administrator privileges or complex installations.

Purpose of the Study:

  • To develop a lightweight, ad hoc framework for distributed computation.
  • To enable fault-tolerant computation with minimal setup requirements.
  • To effectively utilize all available CPU cores for bioinformatic tasks.

Main Methods:

  • The Sputnik framework utilizes the Java Virtual Machine for distributed computation.
  • It offers a graphical user interface for deployment setup.
  • A web user interface provides real-time monitoring of computation jobs.

Main Results:

  • Sputnik achieves full utilization of supplied CPU cores.
  • It requires only a Java runtime and no administrator rights.
  • The framework was successfully demonstrated on feature selection for microarray data.

Conclusions:

  • Sputnik offers a simplified, efficient solution for ad hoc distributed computation in bioinformatics.
  • The framework reduces the overhead associated with setting up parallelized computations.
  • It enhances the accessibility and effectiveness of computational analyses in biological research.