Performance comparison in simulation of Mandelbrot set fractals using Numba

Hungilo, Gilbert Gutabaga; Emmanuel, Gahizi; Pranowo, Pranowo

doi:10.1063/5.0000636

Cited by 3 publications

(2 citation statements)

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“…A variant of the generation of the Mandelbrot set using the two technologies is presented in [25], with this involving the use of an AMD Ryzen™ 5 2500U Quad-Core system analyzed in detail at the processor level. Other articles that use the MPI technology to speed up the Mandelbrot set are [14,26,27].…”

Section: The State Of the Art In Applications Of Fractals For Optimiz...mentioning

confidence: 99%

Possibilities of Use for Fractal Techniques as Parameters of Graphic Analysis

2022

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Image processing remains an area that has impact on the software industry and is a field that is permanently developing in both IT and industrial contexts. Nowadays, the demand for fast computing times is becoming increasingly difficult to fulfill in the case of massive computing systems. This article proposes a particular case of efficiency for a specifically developed model for fractal generations. From the point of view of graphic analysis, the application can generate a series of fractal images. This process is analyzed and compared in this study from a programming perspective in terms of both the results at the processor level and the graphical generation possibilities. This paper presents the structure of the software and its implementation for generating fractal images using the Mandelbrot set. Starting from the complex mathematical set, the component iterations of the Mandelbrot algorithm lead to optimization variants for the calculation. The article consists of a presentation of an optimization variant based on applying parallel calculations for fractal generation. The method used in the study assumes a high grade of accuracy regarding the selected mathematical model for fractal generation and does not characterize a method specially built for a certain kind of image. A series of scenarios are analyzed, and details related to differences in terms of calculation times, starting from the more efficient proposed variant, are presented. The developed software implementation is parallelization-based and is optimized for generating a wide variety of fractal images while also providing a test package for the generated environment. The influence of parallel programming is highlighted in terms of its difference to sequential programming to, in turn, highlight recent methods of speeding up computing times. The purpose of the article is to combine the complexity of the mathematical calculation behind the fractal sets with programming techniques to provides an analysis of the graphic results from the point of view of the use of computing resources and working time.

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Section: The State Of the Art In Applications Of Fractals For Optimiz...mentioning

confidence: 99%

Possibilities of Use for Fractal Techniques as Parameters of Graphic Analysis

2022

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“…The goal of Gilbert Gutabaga Hungilo(Hungilo et al, 2020) in 2020, was to explain the Mandelbrot Set construction simulation by comparing the results of the Python function generating the Mandelbrot set in three cases, using pure Python, Numba, and Numba CUDA running the Python function simultaneously.…”

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confidence: 99%

Implementation and Analysis of Fractals Shapes using GPU-CUDA Model

Sallow

2021

ACAD J NAWROZ UNIV

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The rapid evolution of floating-point computing capacity and memory in recent years has resulted graphics processing units (GPUs) an increasingly attractive platform to speed scientific applications and are popular rapidly due to the large amount of data that processes the data on time. Fractals have many implementations that involve faster computation and massive amounts of floating-point computation. In this paper, constructing the fractal image algorithm has been implemented both sequential and parallel versions using fractal Mandelbrot and Julia sets. CPU was used for the execution in sequential mode while GPUarray and CUDA kernel was used for the parallel mode. The evaluation of the performance of the constructed algorithms for sequential structure using CPUs (2.20 GHz and 2.60 GHz) and parallelism structure for various models of GPU (GeForce GTX 1060 and GeForce GTX 1660 Ti ) devices, calculated in terms of execution time and speedup to compare between CPU and GPU maximum ability. The results showed that the execution on GPU using GPUArray or GUDA kernel is faster than its sequential implementation using CPU. And the execution using the GUDA kernel is faster than the execution using GPUArray, and the execution time between GPU devices was different, GPU with (Ti) series execute faster than the other models.

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