CACTUS & Rendering in

HPC Clusters

Stacey Simmons

Assistant Director

Laboratory for

Creative Arts & Technologies

Laboratory for Creative Arts & Technologies

•Artists always use the technology of their age

•21st Century is the Information Age

•Arts applications use & need advanced IT

Convergence of Media & Information Technologies

Scientific Visualization

Video & Computer Games

Web-delivered Information

Digital Animation

Video & Audio Production

New Media Art Works

Convergence

Software Far Behind

Hardware•In HPC, hardware developments are

much faster than application developments.

•CACTUS can help overcome this.

•Commercially viable.

What is rendering?•rendering - The conversion of a high-

level object-based description into a graphical image for display

For example, ray-tracing takes a mathematical model of a three-dimensional object or scene and converts it into a bitmap image. Another example is the process of converting HTML into an image for display to the user.

•“Reality begins at 80 million polygons” -Alvy Ray Smith

Some Problems in

Rendering Technologies

•Computing Power

•Task Farming

•Load Balancing

•Primitive Reflection

•Communication

•Scalability

Computing Power

•Parallelization has increased capacity, but communication and scalability are problems.

•Opportunities exist for advancement, but we’re still behind where we’d like to be

Primitives

•Primitives are course images sent from the client to the processor.

•These are used by the processor as references in the rendering process.

•The more complex the image, the more memory it takes up, this affects efficiency, correction, and thus, delivery time.

Primitives and Distributed

Rendering•Distributed Rendering refers to the distribution

of an image over a network of machines.

•The image is broken up into tiles.

•Each tile is assigned to a processor, with its

primitive.

•The computer then converts that numerical data

into a graphic image.

•The tile image is composited and sent back to

the client where the tiles are then composited

into a single image.

Task Farming•In a multi-processor environment, task farming is

essential but comes with inherent problems.

•Scalability is limited because of mirrored

primitives needing to be loaded on each processor.

• Some researchers have new techniques which are not

appropriate for every project, but which sometimes need to

be integrated in a greater project.

•Being able to assign individual groups of

processors is ideal, but difficult.

•Need to be able to track distributed components

as well as composite/reassembly.

Load Balancing

•One of the greatest obstacles is the need for a

reference primitive to be loaded on every tasked

machine.

•Scheduling becomes an issue without proper

checks and redundancies

•Having a thorn that could properly analyze and

distribute the tasks would be ideal.

•Having a thorn that would check, schedule, and

reassign simultaneously would be a great help.

Thorn Opportunities•Thorns that analyze the complexity of an image and distribute the tiles

based on complexity.

•Thorns that schedule and check the completed tasks over the system.

•Thorns that segregate groups of processors and tasks.

•Thorns that could help with efficiency of primitives on each processor.

•Thorns that could assign a different rendering program simultaneously

for a given project.

•Thorns that could manage compositing.

•Thorns for research purposes, assigning varying groups of processors

and testing experimental formulae simultaneously over clusters or grids.

Thorn Opportunities

•Thorns that can analyze the boundaries of 3D

data, and predict where data starvation may

occur.

•Thorns that can insert program algorithms as

needed.

•Thorns that can apply texture maps or schedule

secondary rendering or compositing processes.

Discovery Channel

QuickTime™ and aPhoto decompressor

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QuickTime™ and aPhoto decompressor

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Source: Wilhelmson, Jewett, Shaw