Created by Rob Golshan (uteid: rpg499)
Credit: main.cpp: GL functions / idea of the image loop taken from NVIDIA cuda sample code convolution.cu: Algorithms implemented based on convolutionSeparable.pdf in cuda sample code and a parallel implementation of algorithms in https://web.archive.org/web/20060718054020/https://www.acm.uiuc.edu/siggraph/workshops/wjarosz_convolution_2001.pdf
This is an implementation of several image processesing algorithms utilizing the parallelism of an NVIDIA GPU via CUDA. Algorithms implemented are:
- 2D Convolution in parallel that works with any kernel (i.e. filter matrix)
- O(radius^2) assuming all blocks run in parallel
- 2D Convolution in parallel similar to #1, but uses shared memory. Works with any kernel.
- This is faster than #1.
- Shared memory requirements are (BLOCK_SIZE x kernel radius) squared
- Could possibly be faster (but same time complexity) by loading the source image in a texture
- 2D Convolution in parallel with SEPARABLE kernels ONLY.
- Split into two functions that compute convolution of rows or convolution of columns
- O(radius) assuming all blocks run in parallel
- Boxfilter
- Similar to #3, but uses properties of box filters to keep time low when using a big radius
- O(width+height) assuming all blocks run in parallel
- Could possibly be faster (but same time complexity) by loading the source image in a texture
- Time taken independent of radius size
- Multiple iterations of this similate a Guassian filter
Filters I purposely did not implement:
- FFT filter
- Requires more math knowledge than I currently have
- Implementation would be padding kernel/image and using FFT library in cuda
- Slower than separable implementation
- Should only really be needed with using BIG kernels that are not separable
- Guassian filters
- We can either use a separable filter (#3) or a box filter several times (#4) to get the same result
Any other filters I didn't implement were either because I thought it was already a filter mentioned earlier, or I missed it in my research
Why use this over NVIDIA sample code? There is no reason. I doubt my implementations are any faster than the samples provided.
Build with make
Tested and built on a single GPU system with a GTX 980 (compute capability 5.2) Have Xwindow system enabled to visually see results
Must either use the sample lena.ppm or have your own ppm image file
./convolution --image [path to image]
While the program is running and the XWindow is in focus, press h for a help command.