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Related Concept Videos

Parallel Processing01:20

Parallel Processing

The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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Parallel Resonance01:23

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The parallel RLC circuit is an arrangement where the resistor (R), inductor (L), and capacitor (C) are all connected to the same nodes and, as a result, share the same voltage across them. The parallel RLC circuit is analyzed in terms of admittance (Y), which reflects the ease with which current can flow. The admittance is given by:
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Computational Analysis Tutorial for Chimeric Small Noncoding RNA: Target RNA Sequencing Libraries
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SPRINT: a new parallel framework for R.

Jon Hill1, Matthew Hambley, Thorsten Forster

  • 1EPCC, The University of Edinburgh, James Clerk Maxwell Building, Mayfield Road, Edinburgh EH93JZ, UK. j.hill@epcc.ed.ac.uk

BMC Bioinformatics
|December 31, 2008
PubMed
Summary
This summary is machine-generated.

High Performance Computing (HPC) systems can accelerate genomic data analysis in R. The Simple Parallel R INTerface (SPRINT) framework enables easy use of HPC resources for biostatisticians without parallel programming expertise.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Microarray analysis generates massive datasets, challenging current bioinformatics infrastructure.
  • High Performance Computing (HPC) offers a solution with its numerous processors and memory.
  • R and Bioconductor are widely used for microarray data analysis, but exploiting HPC requires parallel processing expertise.

Purpose of the Study:

  • To develop a user-friendly method for R users to leverage HPC systems for genomic data analysis.
  • To enable biostatisticians to analyze large datasets without mastering complex parallel programming.

Main Methods:

  • Designed and implemented a prototype framework called Simple Parallel R INTerface (SPRINT).
  • SPRINT acts as a wrapper for parallelized functions, requiring minimal modifications to existing R scripts.
  • Developed a function for computing pairwise correlation matrices as a demonstration.

Main Results:

  • SPRINT facilitates the easy exploitation of HPC systems for R users.
  • The pairwise correlation matrix computation using SPRINT on an 8-processor HPC reduced computation time by over three times compared to single-processor R.
  • Minimal R script modification and no parallel computing expertise are needed.

Conclusions:

  • SPRINT empowers biostatisticians to focus on research rather than computational complexities.
  • The framework allows effective utilization of HPC resources for large-scale genomic data analysis.
  • Further development and addition of functions will enhance SPRINT's utility.