Implementation and evaluation of two parallel computational models for the simulation of a long-haul DWDM system limited by FWM

Rafael Sánchez-Lara, José L. López-Martínez*, Joel A. Trejo-Sánchez, Herman L. Offerhaus, José A. Álvarez-Chávez

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Four-Wave Mixing (FWM) is one of the non-linear phenomena affecting long-reach communication systems and high bandwidth. Research communities use simulation tools for parameter optimization. Unfortunately, such a simulation is time-consuming and requires more time as the number of channels increases. This paper proposes two fast implementations of Dense Wavelength Division Multiplexing (DWDM) system, limited by FWM and the intrinsic Amplified Spontaneous Emission (ASE) noise of optical amplifiers employed in each segment. Additionally, this work compares the efficiency and speed improvement of the proposed parallelization model versus an earlier sequential model. We present the computational complexity analysis of sequential and parallel models. The paper considers two different parallel implementations: a multicore processor using Open MultiProcessing (OpenMP) and Compute Unified Device Architecture (CUDA), which is based on the use of a Graphics Processing Unit (GPU). Results show that parallelism using CUDA improves by up to 70 times the simulation performance compared to the sequential model. Parallelism with CUDA is up to 15 times compared to OpenMP using 12 logical processors. It is possible to simulate an increased number of channels within our parallel execution, which was impractical in the sequential simulation.

Original languageEnglish
Number of pages12
JournalConcurrency and Computation: Practice and Experience
Early online date20 Nov 2023
DOIs
Publication statusE-pub ahead of print/First online - 20 Nov 2023

Keywords

  • computer simulation
  • CUDA
  • dense wavelength division multiplexing
  • four-wave mixing
  • OpenMP
  • 2024 OA procedure
  • UT-Hybrid-D

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