DocumentCode :
3086930
Title :
GPU Accelerated Lanczos Algorithm with Applications
Author :
Matam, Kiran Kumar ; Kothapalli, Kishore
Author_Institution :
Int. Inst. of Inf. Technol., Hyderabad Gachibowli, Hyderabad, India
fYear :
2011
fDate :
22-25 March 2011
Firstpage :
71
Lastpage :
76
Abstract :
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.
Keywords :
coprocessors; eigenvalues and eigenfunctions; graph theory; image segmentation; iterative methods; ubiquitous computing; CUDA programming; GPGPU; Intel Math Kernel Library; OpenMP; accelerated Lanczos algorithm; eigenvalues; eigenvectors; general purpose computations; graph bisection; graphics processing units; image segmentation; iterative method; linear algebra; multicore CPU; ubiquitous accelerator; Eigenvalues and eigenfunctions; Graphics processing unit; Image segmentation; Instruction sets; Kernel; Sparse matrices; Symmetric matrices; GPGPU; Lanczos; graph partitioning; image segmentation; spectral methods;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced Information Networking and Applications (WAINA), 2011 IEEE Workshops of International Conference on
Conference_Location :
Biopolis
Print_ISBN :
978-1-61284-829-7
Electronic_ISBN :
978-0-7695-4338-3
Type :
conf
DOI :
10.1109/WAINA.2011.70
Filename :
5763440
Link To Document :
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