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Adjusting a Traverse01:12

Adjusting a Traverse

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In the site survey of a four-sided traverse, internal angles are essential to ensure geometric accuracy. The survey revealed that the sum of the measured internal angles was 359 degrees and 48 minutes, which is 12 minutes less than the expected 360 degrees. This discrepancy signals an error likely arising from measurement inaccuracies during the fieldwork.To rectify this error, the adjustment process involved distributing the 12-minute shortfall equally across the four internal angles. By...
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Each human somatic cell contains 6 billion base pairs of DNA. Each base pair is 0.34 nm long, meaning each diploid cell contains a staggering 2 meters of DNA. This long DNA strand is packed inside a nucleus measuring only 10-20 microns in diameter with the help of specialized DNA-binding proteins called histones. Together they form a compact DNA-protein complex called chromatin. The chromatin is further compacted into higher-order structures. The highest level of compaction is achieved during...
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Each human somatic cell contains 6 billion base-pairs of DNA. Each base-pair is 0.34 nm long, which means that each diploid cell contains a staggering 2 meters of DNA. How is such a long DNA strand packed inside a nucleus measuring only 10 - 20 microns in diameter? 
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Traverse angle computations are a critical component of surveying, used to compute the internal angles within a closed traverse. A traverse consists of a series of connected lines forming a closed loop, often used for land boundary delineation or mapping. Calculating the internal angles ensures accuracy in the traverse geometry and is essential for checking survey data integrity.The process begins with known azimuths and bearings of the traverse sides. Internal angles at each vertex are...
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cuRnet: an R package for graph traversing on GPU.

Vincenzo Bonnici1, Federico Busato1, Stefano Aldegheri1

  • 1Department of Computer Science, University of Verona, Strada le Grazie, 15, Italy, Verona, Italy.

BMC Bioinformatics
|October 28, 2018
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Summary
This summary is machine-generated.

This study introduces cuRnet, an R package enabling GPU-accelerated graph algorithms like BFS, SSSP, and SCC for bioinformatics. cuRnet significantly speeds up biological network analysis by leveraging parallel computing on graphics processing units (GPUs).

Keywords:
Biological network analysisGPU parallel implementationGraph traversalHigh-throughput omics network annotationPrize-collecting Steiner forestTopological network analysis

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • High-Performance Computing

Background:

  • R is a standard analysis environment in bioinformatics, with many packages for biological network analysis.
  • Graph algorithms are computationally intensive, necessitating high-performance computing solutions.
  • Parallel computing on Graphics Processing Units (GPUs) is increasingly used to address execution time constraints in bioinformatics.

Purpose of the Study:

  • Introduce cuRnet, a novel R package for parallel graph algorithm implementation on GPUs.
  • Enable faster analysis of biological networks and omics data through GPU acceleration.
  • Provide efficient implementations of Breadth-First Search (BFS), Single-Source Shortest Paths (SSSP), and Strongly Connected Components (SCC) algorithms.

Main Methods:

  • Developed cuRnet, an R package for parallel execution of graph algorithms on GPUs.
  • Implemented BFS, SSSP, and SCC algorithms using GPU parallelization.
  • Tested cuRnet performance on large protein interaction networks and omics data analysis.

Main Results:

  • cuRnet offers parallel implementations of BFS, SSSP, and SCC algorithms for GPUs.
  • The package accelerates runtime by up to tenfold compared to sequential CPU computations.
  • Demonstrated efficiency in analyzing large biological networks and interpreting omics data.

Conclusions:

  • cuRnet is an R package designed to accelerate graph traversal and analysis using GPU parallel computation.
  • The package enhances biological network analysis by speeding up fundamental graph algorithms.
  • cuRnet efficiently supports complex bioinformatics procedures that rely on graph algorithms.