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Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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Enhancing the performance of the aggregated bit vector algorithm in network packet classification using GPU.

Mahdi Abbasi1, Razieh Tahouri2, Milad Rafiee1

  • 1Department of Computer Engineering, Engineering Faculty, Bu-Ali Sina University, Hamedan, Iran.

Peerj. Computer Science
|April 5, 2021
PubMed
Summary

This study introduces a graphics processing unit (GPU) accelerated packet classification algorithm, the aggregated bit vector, and an analysis method to predict its performance. Results confirm the GPU kernel

Keywords:
Aggregated bit vectorAnalysisGPUParallel processingPerformance

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Area of Science:

  • Computer Science
  • Network Engineering
  • High-Performance Computing

Background:

  • Packet classification is crucial for network functions in high-speed systems.
  • Graphics Processing Units (GPUs) offer parallel processing capabilities for network tasks.
  • The aggregated bit vector algorithm is suitable for parallelization.

Purpose of the Study:

  • To develop and evaluate a GPU-accelerated aggregated bit vector algorithm for packet classification.
  • To adapt an asymptotic analysis method for predicting the performance of the GPU kernel.
  • To validate the efficiency of the GPU implementation and the accuracy of the analysis.

Main Methods:

  • Implementation of a parallel kernel for the aggregated bit vector algorithm on GPUs.
  • Adaptation of an asymptotic analysis technique to predict empirical results.
  • Experimental evaluation of the GPU kernel's performance and analysis method's accuracy.

Main Results:

  • The proposed parallel kernel demonstrates efficient packet classification on GPUs.
  • The asymptotic analysis method accurately predicts experimental trends.
  • GPU acceleration significantly enhances the performance of the aggregated bit vector algorithm.

Conclusions:

  • The GPU-accelerated aggregated bit vector algorithm is an efficient solution for high-speed packet classification.
  • The developed analysis method provides accurate performance predictions.
  • This work validates the use of GPUs for accelerating network classification tasks.