Computational fluid dynamics (CFD) plays an important role in many scientific applications, ranging from designing more effective boat engines or aircraft wings to predicting tomorrow's weather, but at the cost of requiring huge amounts of computing time. Also, traditional algorithms suffer from scalability limitations, making them hard to parallelize massively. As a relatively new and promising method for computational fluid dynamics, the Lattice Boltzmann algorithm tries to solve the scalability problems of conventional, but well-tested algorithms in computational fluid dynamics. Through its inherently local structure, it is well suited for parallel processing, and has been implemented on many different kinds of parallel platforms. Adapteva's Epiphany platform is a modern, low-power manycore architecture, which is designed to scale up to thousands of cores, and has even more ambitious plans for the future. Hardware support for floating-point calculations makes it a possible choice in scientific settings. The goal of this thesis is to analyze the performance of the Lattice Boltzmann algorithm on the Epiphany platform. This is done by implementing and testing the lid cavity test case in two and three dimensions. In real applications, high performance on large lattices with millions of nodes is very important. Although the tested Epiphany implementation scales very good, the hardware does not provide adequate amounts of local memory and external memory bandwidth, currently preventing widespread use in computational fluid dynamics.

The purpose of this study was to investigate the possibility to measure speed of a vehicle usingimage correlation. It was identified that a new solution of measuring the speed of a vehicle, astoday’s solution does not give the True Speed Over Ground, would open up possibilities of highprecision driving applications. It was also the intention to evaluate the performance of theproposed algorithm on a multicore platform. The study was commissioned by HalmstadUniversity.The investigation of image correlation as a method to measure speed of a vehicle was conductedby applying the proposed algorithm on a sequence of images. The result was compared toreference points in the image sequence to confirm the accuracy. The performance of the multicoreplatform was measured by counting the clock cycles it took to perform one measurement cycle ofthe algorithm.It was found out that using image correlation to measure speed has a positional accuracy of closeto a half percent. The results also revealed that one measurement cycle of the algorithm could beperformed in close to half a millisecond and the achieved parallel utilization of the multicoreplatform was close to eighty-seven percent.It was concluded that the algorithm performed well within the limit of acceptance. A conclusionabout the performance was that low execution time of a measurement cycle makes it possible toexecute the algorithm at a frequency of eighteen hundred Hertz. With a frequency that high, incombination with the camera settings proposed in the thesis, the algorithm would be able tomeasure speeds close to one thousand one hundred kilometers per hour.The authors recommend that future work should be focused on investigating the cameraparameters to be able to optimize both the memory and computational requirements of theapplication. It is also recommended to look closer at the algorithm and the possibilities ofdetecting transversal and angular changes as it would open up for other application areas,requiring more than just the speed.