introduction to parallel rendering: sorting, chromium, and mpi
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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: - PowerPoint PPT PresentationTRANSCRIPT
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