Fractal Architect Render Engine

Open Source, Portable Flame Fractal GPU Rendering Engine


Filling a deep need for extremely fast GPU rendering and high quality output to the Flame fractal community.

Your favorite app can now support GPU rendering.

What is It?

Packaged as a dynamic linked library, so it can be embedded in commercial or open source flame fractal editors like Apophysis, JWildfire, Chaotica.


Written using portable C++ 14. Portable rewrite of commercial Fractal Architect app's render engine.

FA 5 is a commercial Apple Mac app.

Supported Platforms: Windows, Linux, MacOS

Author: Steven Brodhead

Project Home: Project Home

Blog: Blog


Core Rendering Library: licensed under GNU LGPL v 2.1.

Example apps' source code: licensed under MIT license.

News -- December 3, 2016


Jwildfire 3.0 uses this render engine for GPU accelerated rendering.

Windows 10
  • Tested on Windows 10 using Nvidia, AMD, and Intel GPUs
  • OpenCL/CUDA platform GPU rendering. Multiple GPU rendering.
  • Great performance.
  • Choice of OpenCL or CUDA (Nvidia GPUs) rendering mode.
Mac OS - Mavericks thru Sierra
  • Same performance as FA 4 app.
  • Dependent on Apple's OpenCL drivers, so Intel GPUs not supported on Mac OS El Capitan, but does work on Mac OS Sierra
  • CUDA rendering on Nvidia GPUs.

New Features in the Works

  • Linux Support
  • Support for new Jwildfire features

Performance on Windows 10

---- Gaming PC ----

Nvidia GeForce GTX 980 Ti     $469 on 7/1/2016

Intel i7-4790K @ 4.00 Ghz     $340 on 7/1/2016

electricsheep.244.01917.flam3 [1440X960] SS:2 Q:1000 Total:4.00 sec DE:0.41 sec Mips:385.67

Jwildfire  3:10.3  or 190.3 sec         47.6 X faster with FA

electricsheep.245.07662.flam3 [1440X960] SS:2 Q:1000 Total:2.52 sec DE:0.17 sec Mips:591.08

Jwildfire  1.44.5  or 104.5 sec         41.5 X faster with FA

---- Integrated Graphics PC ----

Intel i7-4790K @ 4.00 Ghz     $340 on 7/1/2016  --- Integrated GPU: Intel(R) HD Graphics 4600

electricsheep.244.01917.flam3 [1440X960] SS:2 Q:1000 Total:24.53 sec DE:1.64 sec Mips:60.55

Jwildfire  3:10.3  or 190.3 sec         7.8 X faster with FA

electricsheep.245.07662.flam3 [1440X960] SS:2 Q:1000 Total:16.27 sec DE:1.59 sec Mips:94.47

Jwildfire  1.44.5  or 104.5 sec         6.4 X faster with FA

---- High end Laptop ---- 2015 MacBook Pro

Intel i7-4870HQ @ 2.50 Ghz 
AMD Radeon M9 M370X

electricsheep.244.01917.png [1440X960] SS:2 Q:1000 Total:18.08 sec DE:1.92 sec Mips:85.79

Jwildfire  5:19  or 319. sec         17.6 X faster with FA

electricsheep.245.07662.png [1440X960] SS:1 Q:1000 Total:9.80 sec DE:0.30 sec Mips:145.90

Jwildfire  2:51.5  or 171.5 sec      17.5 X faster with FA

---- High end Gaming Laptop ---- 2016 Razer Blade using CUDA

Intel i7-6700HQ @ 2.60 Ghz 
Nvidia GeForce GTX 1060

electricsheep.244.01917.png [1440X960] SS:4 Q:500 Total:3.54 sec DE:0.14 sec Mips:191.70

Jwildfire v3.0  65.69 sec         18.6 X faster with FA

electricsheep.245.07662.png [1440X960] SS:1 Q:500 Total:1.09 sec DE:0.01 sec Mips:603.08

Jwildfire v3.0  35.94 sec         33.0 X faster with FA

For Developers

3rd Party Dependencies
  • Library itself has no 3rd party library dependencies. It is completely standalone.
    • Exception: OpenSSL on Mac OS
  • Unit Testing framework: uses CXXTest Unit Test Framework
  • Example app uses GraphicsMagick API to save images to file.
Build Instructions

See BuildingNotes.txt for complete instructions.

GraphicsMagick is a pain to build as its such a big API. The library itself does not use GraphicsMagick, it is only used by the example app.

So that means that you can use a different imaging API, like .Net, Cocoa, or Java. There are only about 5 lines of code in the example app, that would need to be replaced.