Farming for Pixels – A teaching lab becomes a computational cluster

While working on my submission to this years SURF Research as Art Competition I realized that if I was to have any hope of rendering the final image at high resolution in a reasonable amount of time I would need more power. To do this I applied node parallelism in the form of a computer lab turned render farm.

The above image is the result of ~8 hours of rendering, at 4k resolution, over 18 machines (as described below). No colour correction or other post-processing (other than converting to jpeg for uploading) has been applied.

I try to keep the current generation of my rendering software nicely optimized but at it’s core it’s purpose is to be mathematically correct, capable of capturing a suite of internal statistics, and to be simple to extend. Speedup by cpu parallelism is only performed at the pixel (technically pixel tile) sampling level to reduce the amount of intrusion ray-packet tracing can bring to a renderer. In the future I plan to add a GPU work distributor using CUDA but for now this is quite low on my research priorities.

In order to get the speed boost I needed to render high resolution bi-directional path traced images I made use of Swansea Computer Sciences – Linux Lab, which has 30 or so i7, 8GB Ram, 256GB SSD machines running OpenSUSE. I wrote a bash script which for each ip address in a machine_file (containing all ip’s in the farm) ssh’s into each system and starts the renderer as a background process, and another to ssh into all machines and stop the current render.

The render job on each node outputs to a unique binary partials file every 10 samples per pixel (at 4k resolution) to a common network directory, overwriting the files previous values. This file contains three int’s containing width, height, and samples respectively; followed by (width * height * 3) double precision numbers representing the row-column pixel data stored in BGR format.

The data in the file represents the average luminance of each pixel in HDR. A second utility program can be run at a later time to process all compatible partials files in the same directory and turn them into a single image. Which is then properly tone mapped, gamma corrected, and saved as a .bmp file. To combine two partials, the utility program simply performs the following equation for each pixel:

P_1,2 = ((P_1 * S_1) + (P_2 * S_2)) / (S_1 + S_2)

By repeating this process one by one (each partial file can be > 500MB) all partials in a directory can be aggregated together into a single consistent and unbiased image.

Tags Bi-Directional Path Tracing, Concurrency, Depth of Field, Global Illumination, Maya, Path Tracing

Feels good to be back in the office

Today has been a pretty good day, both for me and for my work. For the last couple of weeks I’ve been working from home because all I am doing lately is background reading and coding my new renderer. At first it was nice to know that my workspace was only a commando roll (or a fall) out of bed away from me; but after 3 weeks it just got to be a bit much. Don’t take this to mean I didn’t leave the house for three weeks, I did, but working long hours from the comfort of my room did dramatically take its toll on me.

But enough about me going mad in the house! Today I came into the office (which I think I’ll start doing a lot more often) and have made great progress on my new C++ render!

Tags Concurrency, Global Illumination, Monte Carlo Integration, Path Tracing, Ray Tracing

More Bi-Directional Path Tracing

It’s still not perfect, far from it in fact, but it’s progress none the less. I’ve been reading a lot lately about Metropolis Light Transport, Manifold Exploration, Multiple Importance Sampling (they do love their M names) and it’s high time I started implementing some of them myself.

So it’s with great sadness that I am retiring my PRT project which began over a year ago, all the way back at the start of my dissertation. PRT is written in Java, for simplicity, and was designed in such a way that as I read new papers about more and more complex rendering techniques I could easily drop in a new class, add a call to the render loop, or even replace the main renderer all together with an alternative algorithm which still called upon the original framework.

I added many features over time from Ray Tracing, Photon Mapping, Phong and Blinn-Phong shading, DOF, Refraction, Glossy Surfaces, Texture Mapping, Spacial Trees, Meshes, Ambient-Occlusion, Area Lighting, Anti-Aliasing, Jitter Sampling, Adaptive Super-Sampling, Parallelization via both multi-threading and using the gpu with OpenCL, Path Tracing, all the way up top Bi-Directional Path Tracing.

But the time has taken it’s toll and too much has been added on top of what began as a very simple ray tracer. It’s time to start anew.

My plans for the new renderer is to build it entirely in C++ with the ability to easily add plugins over time like the original. Working in C++ gives a nice benefit that as time goes by I can choose to dedicate some parts of the code to the GPU via CUDA or OpenCL without too much overhead or hassle. For now though the plan is to rebuild the optimized maths library and get a generic framework for a render in place. Functioning renderers will then be built on top of the framework each implementing different feature sets and algorithms.

Tags Bi-Directional Path Tracing, Concurrency, CUDA, Depth of Field, Global Illumination, GPGPU, Monte Carlo Integration, OpenCL, Path Tracing, Photon Mapping, Ray Tracing