Parallelization of Vector Fitting Algorithm for GPU Platforms

With the continually increasing operating frequencies high-speed effects of modules such as multiconductor interconnect structures and packages are becoming increasingly influential in determining the performance of modern electronic designs. At higher frequencies, they are often characterized by electromagnetic tools yielding tabulated scattering parameter based multiport descriptions or directly using multiport measurements. However, integrating such tabulated data models in regular SPICE like tool environment is a challenge. This was handled by the Vector Fitting (VF) technique, however, it suffers in the presence of large number of ports or poles and becomes computationally slower. To address this problem, recently, parallel vector fitting using multi CPU environment was proposed in the literature. In this thesis, VF algorithm is advanced by proposing the use of the emerging computing platform of GPUs. Several parallel strategies are explored for optimal use of resources: CPUs, GPU and memory, for arriving at better computational performance.