Implementing method of moments
on a GPGPU using Nvidia CUDA

This thesis concentrates on the algorithmic aspects of Method of Moments (MoM) and Locally Corrected Nyström (LCN) numerical methods in electromagnetics. The data dependency in each step of the algorithm is analyzed to implement a parallel version that can harness the powerful processing power of a General Purpose Graphics Processing Unit (GPGPU). The GPGPU programming model provided by NVIDIA's Compute Unified Device Architecture (CUDA) is described to learn the software tools at hand enabling us to implement C code on the GPGPU. Various optimizations such as the partial update at every iteration, inter-block synchronization and using shared memory enable us to achieve an overall speedup of approximately 10. The study also brings out the strengths and weaknesses in implementing different methods such as Crout's LU decomposition and triangular matrix inversion on a GPGPU architecture. The results suggest future directions of study in different algorithms and their effectiveness on a parallel processor environment. The performance data collected show how different features of the GPGPU architecture can be enhanced to yield higher speedup.

Date

2010-04-12

Resource Type

Text

Resource Subtype

Thesis

Full item page