high performance computing on graphics processing units: hgpu.org

During times of stock market turbulence and crises, monitoring the clustering behaviour of financial instruments allows one to better understand the behaviour of the stock market and the associated systemic risks. In the study undertaken, I apply an effective and performant approach to classify data clusters in order to better understand correlations between stocks. The novel methods aim to address the lack of effective algorithms to deal with high-performance cluster analysis in the context of large complex real-time low-latency data-sets. I apply an efficient and novel data clustering approach, namely the Giada and Marsili log-likelihood function derived from the Noh model and use a Parallel Genetic Algorithm in order to isolate residual data clusters. Genetic Algorithms (GAs) are a very versatile methodology for scientific computing, while the application of Parallel Genetic Algorithms (PGAs) further increases the computational efficiency. They are an effective vehicle to mine data sets for information and traits. However, the traditional parallel computing environment can be expensive. I focused on adopting NVIDIAs Compute Unified Device Architecture (CUDA) programming model in order to develop a PGA framework for my computation solution, where I aim to efficiently filter out residual clusters. The results show that the application of the PGA with the novel clustering function on the CUDA platform is quite effective to improve the computational efficiency of parallel data cluster analysis.