HIGH PERFORMANCE COMPUTING FOR RECONNAISSANCE APPLICATIONS

Parallel programming is vital to fully utilize the multicore architectures that dominate the processor market. The market, however, is constantly evolving, with new processors and new architectures getting released annually. Using an open parallel processing language, such as OpenCL (Open Computing Language), enables the use of a single program across multiple architectures. It also enables a method of evaluation between multiple devices so the best choice can be made for a given application. In this research, OpenCL is used to evaluate the performance of two signal processing algorithms across two graphics processing units and one central processing unit. Experimental results show that for each algorithm, a specific device can clearly be shown to outperform the others.Ensign, United States NavyApproved for public release; distribution is unlimited

Distribution of load in heterogeneous computer systems

The thesis explores the formulation and implementation of an application that divides, computes and merges a parallel computing problem on a heterogeneous system. The application uses all available compute devices. The goal is to determine how to divide a computing problem between devices to maximise the system's utilisation. The thesis presents possible solutions to the problem, their strengths, and weaknesses. Some of the solutions are benchmarked and compared. For benchmarking the Mandelbrot set was generated. Processing units are accessed and managed using the OpenCL framework. We found out how best to divide and allocate work, and how to set the size of work groups to imporve device utilisation