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Optical coherence tomography (OCT) imaging is used in an increasing number of biomedical and industrial applications. A massively parallel simulator of OCT of inhomogeneous turbid media, e.g., biological tissue, could be used as a practical tool to expedite and expand the study of the physical phenomena involving such imaging technique, as well as, to design OCT systems with enhanced performance. Our work presents the open-source implementation of this massively parallel simulator of OCT to satisfy the ever-increasing need for prompt computation of OCT signals with accuracy and flexibility. Our Monte Carlo-based simulator uses graphic processing units (GPUs) to accelerate the intensive computation of processing tens of millions of photon packets undergoing a random walk through a sample. It provides computation of both Class I diffusive reflectance due to ballistic and quasi-ballistic scattered photons and Class II diffusive reflectance due to multiple scattered photons. Our implementation was tested by comparing results with previously validated OCT simulators in multilayered and inhomogeneous (arbitrary spatial distributions) turbid media configurations. It models the objects as a tetrahedron-based mesh and implements and advanced importance sampling technique. Our massively parallel simulator of OCT speeds up the simulation of OCT signals by a factor of 40 times when compared to it central processing unit (CPU)-based sequential implementation.

As network hardware has become faster, inefficient communication and synchronization mechanisms often have proven to be fast enough but better models are needed in order to support future systems. The aggregate function communication model, and the KAPERS design and implementation presented in this thesis, provide more efficient ways to implement a wide range of higher-level communication and synchronization operations. The main contributions of this work center on a new way to use FPGA-based memory in an aggregate function network (AFN). The basic functions were designed and implemented with modal encoding to create a global memory that allows variable length objects and object addresses. New and enhanced algorithms were written for use with the new AFN architecture. This thesis also details the KAPERS prototype hardware implementation.

This thesis presents the application of graphics processing unit (GPU) based parallel computing technique to speed up electromagnetic transients (EMT) simulation of large power systems. GPUs support extra computing capability to handle gaming and animation related applications in the desktop computers. GPUs can be used for general-purpose computations, such as EMT simulation. Traditionally, EMT simulation tools are implemented on the CPUs, where simulation is performed in a sequential manner. Hence, with the increase in network size, there is a drastic increase in simulation times. This research shows that the use of GPU computing considerably reduces the total simulation time. This thesis proposes parallelized algorithm for EMT simulations on the GPU, and demonstrates the algorithm by simulating large power systems. Total computation times for GPU computing, using 'compute unified device architecture' (CUDA)-based C programming are compared with the total computation times for the sequential implementations on the CPU using ANSI-C programming for systems of various sizes and types. Special parallel processing techniques are implemented to model various power system components such as transmission lines, generators, etc. An advanced technique to implement parallel matrix-vector multiplication on the GPU is implemented, which shows a significant performance gain in the simulation. A sparsity-based technique for the inverse admittance matrix is implemented in this simulation process to ignore the multiplications involving zeros. A typical power electronic subsystem is also implemented in this simulation process, which had not been implemented in the literature so far for the GPU platforms. GPU computing-based simulation of large power networks with many power electronic subsystems has shown a massive performance gain compared to conventional sequential simulations with and without the sparsity technique. Finally, in this research work, the effect of granularity on the speedup of simulation was investigated. Granularity is defined as the ratio of the number of transmission lines used to interconnect various subsystems to the total size of the network. It should be noted that dividing a network into smaller subsystems requires additional transmission lines. Simulation results show that there is a negative impact on the overall performance gain of simulation with the use of excessive transmission lines in the test systems.