general Purpose GPU computing

1. general Purpose GPU computing Image Processing & CUDA Programming Jared Barnes Chris Jackson

2. Graphics Processing Unit • Originally created to calculate pixel values • Each core executes the same set of instructions Mario projected onto several 2D planes

3. Graphics Processing Unit • Chip is designed and manufactured by NVIDIA and AMD • Card bought by consumer is manufactured by various other companies

4. Image Processing • What does image processing try to accomplish? • Edit an image • Detect faces in an image • Find object edges in an image • Anything you can imagine • Too computationally intensive for a CPU? • Use a GPU!

5. Image Processing: Brightness

6. Image Processing: Color

7. Image Processing • Simple changes • Crop an image • Recolor an image • Move an image • Harder changes • Scale an image • Compress an image • Filter an image

8. Image Processing Serial Example // Iterate over all pixels in the image for( int x = 0; x < image.width; x++ ) { for( int y = 0; y < image.height; y++ ) { // Get the value of the current pixel Pixel pixel = image.getPixel(x, y); // Check that Red is more intense than Green if( pixel.red > pixel.green ) { // Check that Red is more intense than Blue if( pixel.red > pixel.blue ) { pixel.red = 0; pixel.green = 0; pixel.blue = 255; } } } }

9. Image Processing Parallel Example // Use the ID of this thread to calculate which // pixel to operate on intx = thread.Id % image.width; inty = thread.Id/ image.height; // Get the value of this thread’s pixel Pixel pixel = image.getPixel(x, y); // Check that Red is more intense than Green if( pixel.red > pixel.green ) { // Check that Red is more intense than Blue if( pixel.red > pixel.blue ) { pixel.red = 0; pixel.green= 0; pixel.blue= 255; } }

10. CUDA • Compute Unified Device Architecture • GPU programming language developed by NVIDIA • GPUs have CUDA cores • more cores = more parallel performance

11. CUDA

12. How to write a CUDA program • Have a graphics card with an NVIDIA GPU capable of running CUDA programs • Install CUDA tools and compiler for Visual Studio 2008, 2010, or 2012 • Create a new CUDA project in Visual Studio

13. Vector Addition in CUDA

14. Vector Addition in CUDA

15. Vector Addition in CUDA

16. 3 Steps for Parallelization • Parallelize VectorAdd() • Allocate GPU memory & copy data to it • Modify call to VectorAdd() to launch on GPU

17. Parallelize VectorAdd()

18. GPU Memory: Allocate & Copy

19. GPU Memory: Copy & Free

20. Modify Call to VectorAdd()