Distributed rendering

Parallel rendering (or Distributed rendering) is the application of parallel programming to the computational domain of computer graphics. Rendering graphics can require massive computational resources for complex scenes that arise in scientific visualization, medical visualization, CAD applications, and virtual reality. Rendering is an embarrassingly parallel workload in multiple domains (e.g., pixels, objects, frames) and thus has been the subject of much research.

There are two, often competing, reasons for using parallel rendering. Performance scaling allows frames to be rendered more quickly while data scaling allows larger data sets to be visualized. Different methods of distributing the workload tend to favor one type of scaling over the other. There can also be other advantages and disadvantages such as latency and load balancing issues. The three main options for primitives to distribute are entire frames, pixels, or objects (e.g. triangle meshes).

Each processing unit can render an entire frame from a different point of view or moment in time. The frames rendered from different points of view can improve image quality with anti-aliasing or add effects like depth-of-field and three-dimensional display output. This approach allows for good performance scaling but no data scaling.

When rendering sequential frames in parallel there will be a lag for interactive sessions. The lag between user input and the action being displayed is proportional to the number of sequential frames being rendered in parallel.

Sets of pixels in the screen space can be distributed among processing units in what is often referred to as sort first rendering.

Distributing interlaced lines of pixels gives good load balancing but makes data scaling impossible. Distributing contiguous 2D tiles of pixels allows for data scaling by culling data with the view frustum. However, there is a data overhead from objects on frustum boundaries being replicated and data has to be loaded dynamically as the view point changes. Dynamic load balancing is also needed to maintain performance scaling.

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