| Abstract [eng] |
Data analysis and the ability to quickly adapt to rapidly changing market conditions is the key if you want to have success in the current financial markets. Additionally, the amount of data you have to analyze is huge and fast, but precise, data analysis methods are required. In this Master thesis, I am analyzing the possibilities to use NVIDIA CUDA parallel computing architecture to increase the data analysis speed. Additionally, I am using genetic algorithms as a search technique to further increase the computational performance. During the course of this thesis, a high frequency trading modeling system was created. It is used to compare the time it takes to generate trading results using a GPU parallel architecture and using a standard computer CPU. Analysis of a several different GPUs is done, comparing the time needed for computations in comparison to the CUDA cores and other card specifications. A detailed research of possible optimization techniques is done, providing detailed data of the calculation performance increase for each of them. At the end, after all described optimization methods are applied, a total speed-up of the computations using GPU, while compared to the regular CPU, is more than 27 times. |
