Research experiments using / against the CUDA rasterizer. Initial commit is the 1.1 release, downloaded from http://code.google.com/p/cudaraster/

No scenes are included.

Original README follows:

Implementation by Tero Karras and Samuli Laine Copyright 2010-2012 NVIDIA Corporation

This package contains full source code for the fast GPU-based software rasterizer described in the following paper:

"High-Performance Software Rasterization on GPUs",
Samuli Laine and Tero Karras,
Proc. High-Performance Graphics 2011
http://www.tml.tkk.fi/~samuli/publications/laine2011hpg_paper.pdf

The source code is licensed under New BSD License (see LICENSE), and hosted by Google Code:

http://code.google.com/p/cudaraster/

In this paper, we implement an efficient, completely software-based graphics pipeline on a GPU. Unlike previous approaches, we obey ordering constraints imposed by current graphics APIs, guarantee hole-free rasterization, and support multisample antialiasing. Our goal is to examine the performance implications of not exploiting the fixed-function graphics pipeline, and to discern which additional hardware support would benefit software-based graphics the most.

We present significant improvements over previous work in terms of scalability, performance, and capabilities. Our pipeline is malleable and easy to extend, and we demonstrate that in a wide variety of test cases its performance is within a factor of 2-8x compared to the hardware graphics pipeline on a top of the line GPU.

• Microsoft Windows XP, Vista, or 7.

• At least 1GB of system memory.

• NVIDIA CUDA-compatible GPU with compute capability 2.0 and at least 512 megabytes of RAM. GeForce GTX 480 is recommended.

• NVIDIA CUDA 4.0 or later.

• Microsoft Visual Studio 2010. Required even if you do not plan to build the source code, as the runtime CUDA compilation mechanism depends on it.

1. Install Visual Studio 2010. The Express edition can be downloaded from: http://www.microsoft.com/visualstudio/en-us/products/2010-editions/visual-cpp-express

2. Install the latest NVIDIA GPU drivers and CUDA Toolkit. http://developer.nvidia.com/object/cuda_archive.html

3. Run crsample.exe to start the application in interactive mode. The first run executes certain initialization tasks that may take a while to complete.

4. If you get an error during initialization, the most probable explanation is that the application is unable to launch nvcc.exe contained in the CUDA Toolkit. In this case, you should:

   • Set CUDA_BIN_PATH to point to the CUDA Toolkit "bin" directory, e.g. "set CUDA_BIN_PATH=C:\Program Files (x86)\NVIDIA GPU Computing Toolkit\CUDA\v4.2\bin".

   • Set CUDA_INC_PATH to point to the CUDA Toolkit "include" directory, e.g. "set CUDA_INC_PATH=C:\Program Files (x86)\NVIDIA GPU Computing Toolkit\CUDA\v4.2\include".

   • Run vcvars32.bat to setup Visual Studio paths, e.g. "C:\Program Files (x86)\Microsoft Visual Studio 10.0\VC\bin\vcvars32.bat".

5. Run benchmark.cmd to measure the performance of CudaRaster and OpenGL on both test scenes with the same settings that were used in the paper. The script may take roughly 15 minutes to complete. The results are written into "benchmark.log", and are organized according to the Table and Figure numbering in the paper. The files "benchmark-gtxXXX-cudaYY.log", included in the package, contain reference results for different GPUs and CUDA versions.

6. Optional: Build the application manually.

   • Open cudaraster.sln in Visual Studio 2010.
   • Right-click the "crsample" project and select "Set as StartUp Project".
   • Build and run. Release/Win32 is recommended.

/crsample.exe Pre-built binary for the sample app. /cudaraster.sln Visual Studio 2010 solution file.

/benchmark.cmd Script to run the benchmarks. /benchmark.log Benchmark results. /cam.png Result images. /benchmark-gtxXXX-cudaYY.log Reference results (GTX XXX, CUDA Y.Y). /state_crsample.dat State files, exported with Alt-. /screenshot_crsample_.png Screenshots, exported with PrtScn.