Multi-variate Finance Kernels in the Blue Gene Supercomputer

Computational finance is an important application area for high-performance computing today. Large computational resources are used for a variety of operations related to securities and asset portfolios. For online operations, the focus has been both on reducing latency and improving the quality of the algorithms. This focus on latency has forced a predominance of univariate analysis simply from a feasibility perspective. In this paper, we demonstrate that current supercomputers, and in particular the Blue Gene family of supercomputers, enables the move to online multivariate analysis of entire markets. We use a simple but representative example of multivariate analysis, namely the computation of the correlation matrix, to explore that space. We show how the computation can be parallelized and run as an online real-time operation at the scale of thousands of securities and millions of events per second.