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High performance computing methods for the integration and analysis of biomedical data using SAS.

Justin R Brown1, Valentin Dinu

  • 1Arizona State University, Department of Biomedical Informatics, 13212 East Shea Boulevard, Scottsdale, AZ 85259, United States.

Computer Methods and Programs in Biomedicine
|September 21, 2013
PubMed
Summary
This summary is machine-generated.

High-performance computing (HPC) methods in SAS can analyze large biomedical datasets efficiently. These accessible techniques, including parallel and distributed processing, offer cost-effective alternatives to traditional supercomputing clusters.

Keywords:
High performance computingParallel processingSAS

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • Biomedical data, including microarrays and next-generation sequencing, is increasing exponentially.
  • Analyzing large datasets requires advanced computing power and methodologies.
  • Existing high-performance computing (HPC) solutions often demand specialized programming skills and supercomputing access.

Purpose of the Study:

  • To demonstrate the accessible implementation of HPC methods within SAS for large biomedical datasets.
  • To showcase how SAS can leverage database connectivity, pipeline parallelism, multi-core processing, and distributed computing.
  • To provide a cost-benefit analysis of these SAS-based HPC methods versus traditional supercomputing clusters.

Main Methods:

  • Utilized SAS for implementing parallel processing techniques.
  • Demonstrated database connectivity for data handling.
  • Implemented pipeline parallelism and multi-core parallel processing.
  • Configured distributed processing across multiple machines.
  • Presented simulation results for parallel and distributed processing.

Main Results:

  • Simulation results confirm the effectiveness of parallel and distributed processing in SAS.
  • SAS-based HPC methods can be implemented without extensive programming knowledge.
  • These methods provide a viable alternative to traditional HPC supercomputing clusters.

Conclusions:

  • Accessible HPC methods in SAS can effectively manage and analyze large-scale biomedical data.
  • SAS offers a practical and potentially cost-effective solution for researchers facing big data challenges.
  • The study highlights the benefits of leveraging existing SAS infrastructure for advanced computational tasks in life sciences.