"Twisted" Final scene from The Rest of Your Life book, rendered using Optix 6.5 version.

Started from C++ v2 code from Peter Shirley's Ray Tracing In One Weekend Book Series.

There are multiple implementations in this repository.

• Cpp (Single-threaded C++ CPU)
• Cuda (C++ GPU)
• OptiX (C++ Nvidia Raytracing SDK GPU, Optix 6.5)

Multi-platform CMake builds

• Cpp tested to work on Linux, macOS
• CUDA, Optix 6.5 tested on Ubuntu amd64 19.10 (with Nvidia RTX graphics card). Other version information below

Build

Using cmake as it supports multiple platforms and language environments.

The general flow:

cmake -B build src    # create build dir and generate
cmake --build build   # build all targets

# developer generation
cmake -DCMAKE_EXPORT_COMPILE_COMMANDS=ON \
    -B build src

# parallel builds
cmake --build build --parallel 7 \
    --target theNextWeekOptix

Using -DCMAKE_EXPORT_COMPILE_COMMANDS=ON generates compile_commands.json file so that editors and other tools can know the compiler flags, etc. I use emacs irony-mode and flycheck works, which is very helpful to me using C++.

Typically the number of parallel jobs (7 in example above) is the number of CPU cores in your system, plus one. On linux, to get the number of cores in your system:

grep "^cpu\\scores" /proc/cpuinfo | uniq |  awk '{print $4}'

For debugging CMake itself: -Wdev -Wdeprecated --warn-unused-vars --warn-uninitialized on the -B build generation step

Run

build target(s), then

# bang is used here for my zsh setup to clobber existing file
time ( build/program >! output/iname.ppm )

The time shell wrapper is obviated when program itself outputs its duration.

A .ppm image file is a non-binary (text) format.

Advanced example(s) with additional command line parameters

build/restOfLifeOptix -v -s 0 -dx 1120 -dy 1120 -ns 1024 >! output/test1.ppm

output

INFO: Display driver version: 435.21
INFO: OptiX RTX execution mode is ON.
INFO: Output image dimensions: 1120x1120
INFO: Number of rays sent per pixel: 1024
INFO: Scene number selected: 0
INFO: Scene description: Cornell box
INFO: Took 16.1509 seconds.

In this example

• -v sets verbose output
• -s 0 selects scene zero
• -dx 1120 -dy 1120 sets image to be of width and height 1120x1120
• -ns 1024 collect 1024 sampled rays per pixel

Not all executables have the same options, or possible have none at all.

Possible Future Implementations

• C++ Multi-threaded CPU
• Rust language
• OptiX 7
• Vulcan
• DXR 12

Build C++ (Cpp)

• Source code needs c++11 compatible compiler (e.g., g++-8, g++-9, clang)

cmake -DCMAKE_EXPORT_COMPILE_COMMANDS=ON -B build src

• specify the target with the --target <program> option

  cmake --build build --target inOneWeekendCpp
  cmake --build build --target theNextWeekCpp
  cmake --build build --target restOfLifeCpp

Cpp example images

Build CUDA C++ (Cuda)

Code based on https://github.com/rogerallen/raytracinginoneweekendincuda

cmake -DCMAKE_EXPORT_COMPILE_COMMANDS=ON  -B build src

# target specific SM
cmake -DCMAKE_EXPORT_COMPILE_COMMANDS=ON \
      -DCMAKE_CUDA_FLAGS="-arch=sm_75" \
      -B build src

the CUDA targets

cmake --build build --target inOneWeekendCuda
cmake --build build --target theNextWeekCuda

Implementation stopped at the point of creating a BVH structure in CUDA. The qsort from the book was problematic to adapt since there is no direct equivalent in the thrust library that I could find.

CUDA example images

Build OptiX 6.5 (Optix)

Code based on

• https://github.com/trevordblack/OptixInOneWeekend

and then on earlier versions of

• https://github.com/joaovbs96/OptiX-Path-Tracer

# or set other flags
cmake -DCMAKE_EXPORT_COMPILE_COMMANDS=ON \
    -DCMAKE_CUDA_FLAGS="--use_fast_math --generate-line-info" \
    -B build src

# Other CMAKE_CUDA_FLAGS
#   --relocatable-device-code=true
#   --verbose

build specific targets

cmake --build build --target inOneWeekendOptix --clean-first
cmake --build build --target theNextWeekOptix
cmake --build build --target restOfLifeOptix --parallel 7

Optix example images

Tested on

• Ubuntu Linux 19.10 amd64
• cmake version 3.13.4 (3.13.4-1build1) - 3.8 and 3.11 add incremental features for CUDA
• g++-8 version 8.3.0 (Ubuntu 8.3.0-23ubuntu2) - alternative to g++-9 that is default in Ubuntu 19.10

CUDA Tested on

• CUDA toolkit 10.1 V10.1.168 (from nvidia-cuda-toolkit installer)
  • it installs dependency gcc-8 (gcc-9 is not yet supported in CUDA toolchain)
  • nsight compute 2019.5 (manual download and install from nvidia dev site)
  • nvcc version: Cuda compilation tools, release 10.1, V10.1.168
• Nvidia RTX 2070 Super (Supports SM 7.5)

OptiX Tested on

Same as CUDA above.

Optix 6.5.0 SDK installed at /usr/local/nvidia/NVIDIA-OptiX-SDK-6.5.0-linux64. See notes/ subdirectory for other details and hints.

Early performance comparisons

Ray Tracing In One Weekend final scene

• Single thread CPU: Image took about 12.3 minutes, without BVH. (Intel 4960X Extreme Edition circa 2013)
• Single thread CPU: When generating same scene with BVH partitioning, took about 3 minutes.
• CUDA GPU version: When generating same scene without BVH partitioning, less than 4 seconds
• OptiX GPU version: When generating similar scene at 1K samples per pixel, less than 2.5 seconds

Image Render examples

Image Renders (Optix GPU)

In One Weekend

1200 x 600 pixels, 1K rays launched per pixel. Render time about 2.5 seconds using OptiX on an RTX card.

The Next Week

2400 x 800 pixels with 9000 samples per pixel. took 67.9 seconds

3840 x 1080 pixels with 10240 samples per pixel. took 400 seconds

convert tnw-final_scene.png -resize 50% half_tnw-final_scene.png

Rest Of Life

convert output/rol-final-alum_1k.ppm -resize 50% rol-final-alum_1k.png

• 2240 x 2240 pixels with 10240 samples per pixel, took 541 seconds or 9 minutes. most of noise is gone. PNG scaled to 50% after.
• 2240 x 2240 pixels with 1024 samples per pixel, 55 seconds
• A version 1000x1000 pixels with 500 rays launched per pixel (apples to apples comparison with C++ Rest Of Life final scene): 7.3 seconds (versus 1hr 8min)

Image Renders (C++ Single Thread CPU)

In One Weekend

1200x800 pixels, 20 rays per pixel. Image took about 12.3 minutes, without BVH partitioning. When generating same scene with BVH, took about 3 minutes.

The Next Week

1000x1000 pixels with 2500 rays per pixel. took over 18 hours

Rest Of Life

1000x1000 pixels with 500 rays per pixel. Took 1 hour, 8 minutes

Image Renders (CUDA)

Above: inOneWeekendCuda output, around four seconds to render.