Introduction to Parallel Rendering: Sorting, Chromium, and MPI

Mengxia Zhu

Spring 2006

Parallel Rendering Graphics rendering process is

computationally intensive

Parallel computation is a natural measure to leverage for higher performance

Two levels of parallelism: Functional parallelism – pipelining Data parallelism – multiple results computed at the

same time

Rendering Pipeline

Data Parallel Algorithms

A lot of taxonomies of categorizing parallel algorithms Image space vs. object space Shared memory architecture, distributed memory

architecture MPI, OpenMP, …

Need a uniform framework to study and understand parallel rendering

Sorting in Rendering

Rendering as a sorting process: Sort from object coordinates to screen coordinates Use this concept to study computational and

communication costs

The key procedure: calculating the effect of each primitive on each pixel

Use this concept to study computational and communication costs

Sorting Categories The location of this ‘sort’ determines the

structure of the parallel algorithm Sort-first

during geometry processing distributes “raw” primitives

Sort-middle between geom. processing and rasterization distributes screen-space primitives

Sort-last during rasterization distributes pixels/fragments

Sorting cont A landmark paper: “A sorting classification of parallel

rendering”, Molner, et. al., IEEE CG&A’94.

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Sort-First Sort-Middle Sort-Last

Sort First

Primitives initially assigned arbitrarily Pre-transformation is done to determine

which screen regions are covered Primitives are then redistributed over the

network to the correct renderer Renderer performs the work of the entire

pipeline for that primitive from that point on

Sort First cont

Sort First cont

Screen space is partitioned into non-overlapping 2D tiles, each is rendered independently by a tightly coupled pair of geometry and rasterization processors.

Sub-image of 2D tiles are composited without depth comparison.

Analysis Terms Assume a dataset containing nr raw primitives

with average size ar . We will call primitives that result from

tessellation display primitives. If T is the tessellation ratio, there are nd = Tnr of these, with average size ad = ar /T. If there is no tessellation, T = 1, nd = nr , and ad = ar .

Assume an image containing A pixels and need to compute S samples per pixel. Assume that all primitives within the viewing frustum.

Sort-first analysis

Pros:Low communication requirements when

tessellation or oversampling are high, or when inter-frame coherence exploited

Processors implement entire rendering pipeline for a given screen region

Cons:Susceptible to load imbalance (clumping)Exploiting coherence is difficult

Sort Middle

Primitives initially assigned arbitrarily Primitives fully transformed, lit, etc., by

the geometry processor to which they are initially assigned

Transformed primitives are distributed over the network to the rasterizer assigned to their region of the screen

Sort Middle

Sort Middle Analysis Pros:

Redistribution occurs at a “natural” place Cons:

High communication cost if T is highSusceptible to load imbalance in the same way

as sort-first Overhead:

Display primitive distribution costTessellation factor

Sort Last

Sort Last

Defers sorting until the end (imagine phase)

Renderers operate independently until the visibility stage

Fragments transmitted over network to compositing processors to resolve visibility

Sort Last Analysis

Pros:Renderers implement full pipeline and are

independent until pixel mergingLess prone to load imbalanceVery scalable

Cons:Pixel traffic can be extremely high

Image Composition

• A naïve approach is binary compositing.• Each disjoint pair of processors produces a new subimage.• N/2 subimages are left after the first stage.• Half the number of the original processors are paired up for the next level of compositing hence another half would be idle.• The binary-swap compositing method makes sure that every processor participates in all the stages of the process.• The key idea – at each compositing stage, the two processors involved in a composite operation split the image plane into two pieces.

Binary Swap Example• The binary-swap compositing algorithm for four processors:

Which to choose?

It depends. Which ones can be best matched to

hardware capabilities? Number of primitives, tessellation factor,

coherence, etc., are all considerations. Many tradeoffs.

Load Balancing For better load balancing,

Task queuing: the task queue can be ordered in decreasing task size, such that the concurrency gets finer until the queue is exhausted.

Load stealing: having nodes steal smaller tasks from other nodes, once they have completed their own tasks

Time stamp: timeout stamps used for each task, such that if the node can not finish its task before the timeout, it takes the remnant of the task, re-partitions it and re-distributes it.

Hierarchical data structures, such as octree, k-d tree, etc., are commonly used.

References

These slides reference contents fromJian Huang at University of Tennessee at

Knoxville

William Gropp and Ewing Lusk at Argonne National Laboratory