Comparative study of CUDA-based parallel programming in C and Python for GPU acceleration of the 4th order Runge-Kutta method

•Point reactor kinetics equations.•4th Order Runge-Kutta method.•Acceleration by GPU.•Python and C codes.•Speedup calculations. In this paper, a comparative study is presented on the application of General-purpose Computing on Graphics Processing Units for solving the point reactor kinetics equation...

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Published inNuclear engineering and design Vol. 421; p. 113050
Main Authors Fernandes, Davi F., Santos, Marcelo C., Silva, Adilson C., Lima, Alan M.M.
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.05.2024
Subjects
GPU
Point reactor kinetics equations
Python
Runge-Kutta Method
Point reactor kinetics equations
C
GPU
Runge-Kutta Method
Python
Online AccessGet full text
ISSN0029-5493
1872-759X
DOI10.1016/j.nucengdes.2024.113050

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Abstract •Point reactor kinetics equations.•4th Order Runge-Kutta method.•Acceleration by GPU.•Python and C codes.•Speedup calculations. In this paper, a comparative study is presented on the application of General-purpose Computing on Graphics Processing Units for solving the point reactor kinetics equations through the utilization of the 4th Order Runge-Kutta (RK4) method using the programming languages C and Python. Sequential and parallel algorithms of the RK4 method were developed in C/C++ and Python, with parallel algorithms specifically designed to operate on Graphics Processing Units (GPUs) utilizing the NVIDIA Compute Unified Device Architecture (CUDA) as the programming platform. As an experiment, the execution time for the sequential and parallel algorithms were compared for a reactivity value of ρ = 0.003 and a simulation time of t = 100 s. The parallel C and Python algorithms achieved, respectively, speedups of 9.33 and 409.7 when comparing the execution time on the best GPU utilized (RTX 3070Ti) with the best CPU (3600XT), while still maintaining numerical precision.
AbstractList •Point reactor kinetics equations.•4th Order Runge-Kutta method.•Acceleration by GPU.•Python and C codes.•Speedup calculations. In this paper, a comparative study is presented on the application of General-purpose Computing on Graphics Processing Units for solving the point reactor kinetics equations through the utilization of the 4th Order Runge-Kutta (RK4) method using the programming languages C and Python. Sequential and parallel algorithms of the RK4 method were developed in C/C++ and Python, with parallel algorithms specifically designed to operate on Graphics Processing Units (GPUs) utilizing the NVIDIA Compute Unified Device Architecture (CUDA) as the programming platform. As an experiment, the execution time for the sequential and parallel algorithms were compared for a reactivity value of ρ = 0.003 and a simulation time of t = 100 s. The parallel C and Python algorithms achieved, respectively, speedups of 9.33 and 409.7 when comparing the execution time on the best GPU utilized (RTX 3070Ti) with the best CPU (3600XT), while still maintaining numerical precision.
ArticleNumber 113050
Author Silva, Adilson C.
Lima, Alan M.M.
Santos, Marcelo C.
Fernandes, Davi F.
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Snippet •Point reactor kinetics equations.•4th Order Runge-Kutta method.•Acceleration by GPU.•Python and C codes.•Speedup calculations. In this paper, a comparative...
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SubjectTerms GPU
Point reactor kinetics equations
Python
Runge-Kutta Method
Title Comparative study of CUDA-based parallel programming in C and Python for GPU acceleration of the 4th order Runge-Kutta method
URI https://dx.doi.org/10.1016/j.nucengdes.2024.113050
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