Using parallel programming to improve performance of image processing
Download as pptx, pdf1 like1,503 views
The document discusses a project by an undergraduate student that utilizes parallel programming to enhance the performance of image processing, particularly in biomedical research involving large images. By implementing anisotropic diffusion on the CUDA platform, the project demonstrates that GPUs can significantly improve processing times—potentially by 100-200 times compared to traditional serial algorithms. The findings indicate that partial differential equations are effective for designing parallel algorithms, though performance is restricted by GPU memory size.
Using parallel programming to improve performance of image processing
- 1. USING PARALLEL PROGRAMMING TO IMPROVE PERFORMANCE OF IMAGE PROCESSING Chan Le – KAIST ’13
- 2. INTRODUCTION Me: Chan Le – 3rd year undergraduate student Double major in Computer Science & Management Science A Vietnamese - KAIST ’13 Professor: Won-Ki Jeong GPU-accelerated large-scale biomedical image processing Project: Apply parallel programming to improve performance of image processing
- 3. MOTIVATION Biomedical researches work with images Really big images Take long time to process Raw images are hard to analyze & use for research Really noisy sometimes Need to preprocess before using Image preprocessing using serial algorithms are slow Nowadays, parallel computing are developing Thanks to the popularity of multi-core CPUs and GPUs
- 4. RELATED WORKS: USING PDE IN NOISE- REDUCTION IN (x,y+1 ΔW = IW – It ) ΔN IW ΔW It ΔE IE (x-1,y) (x,y) (x+1,y) ΔS IS (x,y- 1) Heat equation At pixel every (x,y) of the image at the time t: I =It+ΔI t+1 ΔI = (ΔW+ ΔN+ ΔE+ ΔS) / 4
- 5. RELATED WORKS: ANISOTROPIC DIFFUSION Paper: Scale-space and edge detection using anisotropic diffusion (Pietro Perona & Jitendra Malik, 1990) Basic idea: Adding coefficient to each ΔW,ΔN,ΔS,ΔE . /4 How to calculate each c? C= C=
- 7. NVIDIA CUDA Serial vs Parallel program Thread: unit of processing In the past: CPU has only 1 core -> 1 thread at a time Nowadays: multi-cores -> multiple thread at a time CUDA™ is a parallel computing platform and programming model invented by NVIDIA. http://www.nvidia.com/object/cuda_home_new.html How could it helps? CPU: 1-6 cores GPU: hundreds improve performance by the scale of 10 to 100, depends on the algorithm
- 8. MY IMPLEMENTATION Implement Anisotropic Diffusion on CUDA platform 1 thread handle 1 pixel Dividing the image to multiple sub-regions, process them parallely to exploit multiple cores
- 9. SOME RESULT – SMALL
- 10. SOME RESULT – SMALL
- 11. SOME RESULT – MEDIUM
- 12. SOME RESULT – MEDIUM
- 13. BENCHMARK 100 times iteration
- 14. CONCLUSION The result of this project could be use to help improving quality of images before using. Utilizing GPU computing power could improve the performance of your program by 100-200 times Partial Differential Equations are good choices when design parallel algorithm However, the performance is limited by the GPU’s memory size