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Ultimate Performance by using Distributed Computing

A highly detailed fluid simulation requires an immense amount of memory and tedious computation time. To improve these issues, FluX was designed to support the data parallel distributed processing technology for any aspects of operations such as solver, surfacing, file I/O, output visualizing etc, so the memory and computation loads can be distributed to networked computers as many as available. The multi-thread processing has limitation of usable memory and the number of cores, but on the other hand, the distributed data processing system can easily share resources through network and will make you free from the restriction of memory size and the number of cores on a single main-board.

Save Your Time and Increase the Quality

The computation speed of FluX is absolutely superb according to the test which used a different number of cores. The reason why FluX can work at a rapid speed is that the computation of every node of FluX is distributed without exception and secondly it takes much less time to synchronize data among computers by using the state-of-the art numerical methods.

Have You Ever Suffered from Insufficient Memory?

FluX doesn’t have any limitation of the number of computers to share and distribute data over the network, so users can access unlimited memory for fluid simulation with extremely detailed resolution. It makes users possible to effectively enhance the quality of the fluid motions without adding more simulation layers or tweaking extra details on the primary simulation. If you have been in difficulty when you use other softwares because of limited memory and CPUs, why don’t you switch to FluX? You can be free from those issues and get much more effective result as soon as you use FluX.

You Can Experience the Rapid Speed for Surfacing

A fluid surface generated from particles is one of the most important process for fluid simulation. Distributing the memory and the computation enables FluX to generate extremely fine fluid surfaces at a rapid speed which can be differed from other softwares.

Have You Ever Seen a Rendered Image with Billions of Particles?

FluX supports the parallel rendering technology for the distributed output data, so it pre-visualizes the results of simulation such as meshes and particles in the blink of an eye. Therefore computing particles and polygons on FluX is not restricted by memory of a graphic card and they can be rendered at a very fast speed.

Memory-Efficient Node-based Architecture

The flexible node-graph workflow allows users to comfortably design the desired visual effects by assembling nodes. FluX also allows users to create new operating nodes with FluX API and easily develop a new algorithm by combining the new node with the existing nodes. In addition, FluX’s node-based architecture is designed to minimize the waste of memory by sharing memory space as much as possible among all operating nodes.

Compatibility with Other Softwares

FluX provides a number of built-in file operators which can export data into various file formats that can be rendered from existing 3D tools and renderers such as Maya, Max and Renderman etc. You can export FluX particles, meshes and fields(3D volumetric data) straight into Maya, Max, Softimage, RenderMan, Arnold, Krakatoa, V-Ray, Realflow and Houdini. FluX supports the Alembic file format as well.

Supported OS

Windows 7/8 (64 bit) and Linux

FluX provides a number of built-in file operators which can export data into various file formats that can be rendered from existing 3D tools and renderers such as Maya, Max and Renderman etc. You can export FluX particles, meshes and fields(3D volumetric data) straight into Maya, Max, Softimage, RenderMan, Arnold, Krakatoa, V-Ray, Realflow and Houdini. FluX supports the Alembic file format as well.

Simply you can add much more particles to create more beautiful and detailed fluid effects as you imagine with FluX, but other softwares have had limitation due to lack of distributed computing technology.

FluX’s node-graph architecture is designed to minimize the waste of memory by sharing memory space as much as possible among all operating nodes.