Platform Computing has reinforced its strategic partnership with Excelian, a leading Murex, OpenLink, grid and HPC consultancy, to deliver high performance analysis as a new service. Based on Platform Symphony SOA (service oriented architecture) grid middleware, Excelian’s service offering enables customers in the financial services to securely and seamlessly scale compute capabilities for risk and trading analysis without the cost and time required to acquire servers, network bandwidth or administrative staff.
With years of combined experience in the financial services markets, Excelian and Platform Computing have built a high performance, multi-purpose, computational grid infrastructure to enable on-demand, 24x7 analytical calculations for banks and funds of all sizes. The Excelian offering is particularly suited for running market and credit risk analysis, scenario simulation and strategy back-testing, and asset and portfolio pricing and modelling.
For firms with data centres operating at their space or power limits, the Excelian service is a cost-effective way to increase analytical capacity without incurring extra capital and operating expenses. Platform Symphony’s scheduling and workload management software provides Excelian customers with the correct amount of hardware resources when needed with a guaranteed service level at a lower cost than other service providers. As a result, Excelian customers do not over- or under-spend on servers, and are not constrained by their data centre power and space limits to obtain additional HPC capabilities.
Platform’s flagship financial industry solution, Platform Symphony operates core risk and analysis systems for many of the largest investment banks and financial institutions around the world. By managing grids composed of thousands of CPUs and optimising application workloads across shared resource pools, IT departments are better able to support multiple lines of business in response to business priority. By using Platform Symphony, organisations can model pricing algorithms with more fidelity against a broader set of risk factors. This increase in modelling capacity allows firms to have high confidence in their quantification of risk.
