Implementation and Analysis of Fractals Shapes using GPU-CUDA Model


  • Amira Bibo Sallow Department of Computer Science, Nawroz University, Duhok, Kurdistan Region - Iraq



Fractal, Fractal Shapes, Mandelbrot & Julia Set, Self-Similar, GPU, Speedup.


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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How to Cite

Sallow, A. B. (2021). Implementation and Analysis of Fractals Shapes using GPU-CUDA Model. Academic Journal of Nawroz University, 10(2), 1–10.




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