A Parallel Algorithm for the Counting of Ellipses Present in Conglomerates Using GPU

Yam-Uicab, R.; Martínez, José Luis López; Llanes-Castro, Erika Rossana; Narváez-Díaz, Lizzie Edmea; Trejo-Sánchez, Joel Antonio

doi:10.1155/2018/5714638

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…Second, we use Graphic Processing Units for the cost-benefit ratio. [32][33][34] For CUDA, we use Dynamic Parallelism (DM). 32 Dynamic parallelism consists of hierarchical kernels in a tree structure, where a thread is defined as a father kernel.…”

Section: Contributionmentioning

confidence: 99%

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

Sánchez‐Lara,

López‐Martínez,

Trejo‐Sánchez

et al. 2023

Concurrency and Computation

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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.

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Section: Contributionmentioning

confidence: 99%

“…It is shown that using OpenMP, the computation is highly optimized for the simulation. Second, we use Graphic Processing Units for the cost‐benefit ratio 32‐34 . For CUDA, we use Dynamic Parallelism (DM) 32 .…”

Section: Introductionmentioning

confidence: 99%

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

Sánchez‐Lara,

López‐Martínez,

Trejo‐Sánchez

et al. 2023

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…CUDA Overview and GPU Architecture. CUDA is a general-purpose parallel computing platform and programming model for researchers to perform codes on GPU flexibly to solve many complex scientific problems [9,12,28]. In CUDA, a C program is extended to the execution of kernels, which are executed by a large number of threads.…”

Section: Cuda and Gpu Parallel Algorithm For Cfdmentioning

confidence: 99%

A Multi-GPU Parallel Algorithm in Hypersonic Flow Computations

Lai

Tian

et al. 2019

Mathematical Problems in Engineering

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Computational fluid dynamics (CFD) plays an important role in the optimal design of aircraft and the analysis of complex flow mechanisms in the aerospace domain. The graphics processing unit (GPU) has a strong floating-point operation capability and a high memory bandwidth in data parallelism, which brings great opportunities for CFD. A cell-centred finite volume method is applied to solve three-dimensional compressible Navier–Stokes equations on structured meshes with an upwind AUSM+UP numerical scheme for space discretization, and four-stage Runge–Kutta method is used for time discretization. Compute unified device architecture (CUDA) is used as a parallel computing platform and programming model for GPUs, which reduces the complexity of programming. The main purpose of this paper is to design an extremely efficient multi-GPU parallel algorithm based on MPI+CUDA to study the hypersonic flow characteristics. Solutions of hypersonic flow over an aerospace plane model are provided at different Mach numbers. The agreement between numerical computations and experimental measurements is favourable. Acceleration performance of the parallel platform is studied with single GPU, two GPUs, and four GPUs. For single GPU implementation, the speedup reaches 63 for the coarser mesh and 78 for the finest mesh. GPUs are better suited for compute-intensive tasks than traditional CPUs. For multi-GPU parallelization, the speedup of four GPUs reaches 77 for the coarser mesh and 147 for the finest mesh; this is far greater than the acceleration achieved by single GPU and two GPUs. It is prospective to apply the multi-GPU parallel algorithm to hypersonic flow computations.

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Generalization of midpoint ellipse drawing algorithm in computer graphics

Kaleem¹,

Verma²

2023

AIP Conference Proceedings

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A Parallel Algorithm for the Counting of Ellipses Present in Conglomerates Using GPU

Cited by 4 publications

References 11 publications

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

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

A Multi-GPU Parallel Algorithm in Hypersonic Flow Computations

Generalization of midpoint ellipse drawing algorithm in computer graphics

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