"Existing mathematical frameworks for CDO (collateralized debt obligation) valuation are far from compelling - to put it mildly", stated Peter Cotton, CEO of Julius Finance. "This is not surprising, as rigorous evaluation of credit models is prohibitively time consuming in any conventional research setup. We've turned to Star-P to reduce prototyping time and facilitate memory intensive experiments - looking under more rocks, as it were, and finding very interesting things."

Star-P is an ideal solution for this challenge because it enables researchers to easily code algorithms and models on their desktops using popular high-level languages like MATLAB, Python and R, automatically transforming the programmes to run on parallel HPCs. Star-P eliminates the need to re-program the applications in complex languages such as C, FORTRAN or MPI (message passing interface) to run on Julius Finance's Linux based cluster - which otherwise requires arcane programming knowledge and months to complete.

"The computationally challenging analysis of credit factors such as spread, credit rating, foreign exchange and interest rates for a wide variety of corporate investments have made the credit derivative market a black art at best", stated Ilya Mirman, vice president of marketing at Interactive Supercomputing. "Julius Finance is bringing new ideas and technology to these central problems in institutional finance."