GPU Accelerated Lanczos Algorithm with Applications

Graphics Processing Units provide a large computational power at a very low price which position them as an ubiquitous accelerator. GPGPU is accelerating general purpose computations using GPU´s. GPU´s have been used to accelerate many Linear Algebra routines and Numerical Methods. Lanczos is an iterative method well suited for finding the extreme eigenvalues and the corresponding eigenvectors of large sparse symmetric matrices. In this paper, we present an implementation of Lanczos Algorithm on GPU using the CUDA programming model and apply it to two important problems : graph bisection using spectral methods, and image segmentation. Our GPU implementation of spectral bisection performs better when compared to both an Intel Math Kernel Library implementation and a Matlab implementation. Our GPU implementation shows a speedup up to 97.3 times over Matlab Implementation and 2.89 times over the Intel Math Kernel Library implementation on a Intel Core i7 920 Processor, which is a quad-core CPU. Similarly, our image segmentation implementation achieves a speed up of 3.27 compared to a multicore CPU based implementation using Intel Math Kernel Library and OpenMP. Through this work, we therefore wish to establish that the GPU may still be a better platform for also highly irregular and computationally intensive applications.