high performance computing on graphics processing units: hgpu.org

The parallel FDTD method as used in computational electromagnetics is implemented on a variety of different high performance computing platforms. These parallel FDTD implementations have regularly been compared in terms of performance or purchase cost, but very little systematic consideration has been given to how much effort has been used to create the parallel FDTD for a specific computing architecture. The deployment effort for these platforms has changed dramatically with time, the deployment time span used to create FDTD implementations in 1980 ranging from months, to the contemporary scenario where parallel FDTD methods can be implemented on a supercomputer in a matter of hours. This thesis compares the effort required to deploy the parallel FDTD on selected computing platforms from the constituents that make up the deployment effort, such as coding complexity and time of coding. It uses the deployment and performance of the serial FDTD method on a single personal computer as a benchmark and examines the deployments of the parallel FDTD using different parallelisation techniques. These FDTD deployments are then analysed and compared against one another in order to determine the common characteristics between the FDTD implementations on various computing platforms with differing parallelisation techniques. Although subjective in some instances, these characteristics are quantified and compared in tabular form, by using the research information created by the parallel FDTD implementations. The deployment effort is of interest to scientists and engineers considering the creation or purchase of an FDTD-like solution on a high performance computing platform. Although the FDTD method has been considered to be a brute force approach to solving computational electromagnetic problems in the past, this was very probably a factor of the relatively weak computing platforms which took very long periods to process small model sizes. This thesis will describe the current implementations of the parallel FDTD method, made up of a combination of several techniques. These techniques can be easily deployed in a relatively quick time frame on computing architectures ranging from IBM’s Bluegene/P to the amalgamation of multicore processor and graphics processing unit, known as an accelerated processing unit.