ﻻ يوجد ملخص باللغة العربية
Fractal image generation algorithms exhibit extreme parallelizability. Using general purpose graphics processing unit (GPU) programming to implement escape-time algorithms for Julia sets of functions,parallel methods generate visually attractive fractal images much faster than traditional methods. Vastly improved speeds are achieved using this method of computation, which allow real-time generation and display of images. A comparison is made between sequential and parallel implementations of the algorithm. An application created by the authors demonstrates using the increased speed to create dynamic imaging of fractals where the user may explore paths of parameter values corresponding to a given functions Mandelbrot set. Examples are given of artistic and mathematical insights gained by experiencing fractals interactively and from the ability to sample the parameter space quickly and comprehensively.
An algorithm to generate the locus of a circle using the intersection points of straight lines is proposed. The pixels on the circle are plotted independent of one another and the operations involved in finding the locus of the circle from the inters
Graphic Processing Units (GPUs) are getting increasingly important as target architectures in scientific High Performance Computing (HPC). NVIDIA established CUDA as a parallel computing architecture controlling and making use of the compute power of
In order to generate novel 3D shapes with machine learning, one must allow for interpolation. The typical approach for incorporating this creative process is to interpolate in a learned latent space so as to avoid the problem of generating unrealisti
In this paper, we extend our earlier polycube-based all-hexahedral mesh generation method to hexahedral-dominant mesh generation, and present the HexDom software package. Given the boundary representation of a solid model, HexDom creates a hex-domina
Generative Adversarial Networks (GANs) have received a great deal of attention due in part to recent success in generating original, high-quality samples from visual domains. However, most current methods only allow for users to guide this image gene