Fractal flames require lengthy renders because of the nature of the algorithm. It is both computationally intensive, and memory cache inefficient. This holds true for running on both the CPU and GPU. Many benchmarking tools focus on either computation or memory bandwidth. Few do both, and even less measure memory latency. An algorithm that heavily […]

When starting each sub batch, the first point value is chosen randomly. When a bad point is computed, a new point is also  chosen randomly. This field allows you to specify the range in the x and y dimensions that the new point is chosen from in both cases. For example, the default value of […]

Xforms have the most effect over the look of the final output image. Discussing every possible technique for using them is beyond the scope of this project. For such information, you should follow the numerous Apophysis tutorials that are available on Deviant Art and elsewhere. This article is limited to some basic performance considerations when using xforms. […]

At the beginning of each sub batch, a number of iterations are done which are not actually plotted to the histogram. This is known as fusing and helps the image converge more and get rid of extraneous points. Similar to sub batch, this field is also saved to the Xml file, but rarely makes much […]

Fractal flames are created by performing many iterations, the number of which is based on the quality, zoom and dimensions of the image. The first input point used to start the iteration process is random, then all subsequent iterations use the output of the last iteration as their input. This sequence does not continue uninterrupted […]