A Heterogeneous Computing Framework for Computational Finance

This paper presents the Forward Financial Framework (F³), an application framework for describing and implementing forward looking financial computations on high performance, heterogeneous platforms. F³ allows the computational finance problem specification to be captured precisely yet succinctly, then automatically creates efficient implementations for heterogeneous platforms, utilising both multi-core CPUs and FPGAs. The automatic mapping of a high-level problem description to a low-level heterogeneous implementation is possible due to the domain-specific knowledge which is built in F³, along with a software architecture that allows for additional domain knowledge and rules to be added to the framework. Currently the system is able to utilise domain-knowledge of the run-time characteristics of pricing tasks to partition pricing problems and allocate them to appropriate compute resources, and to exploit relationships between financial instruments to balance computation against communication. The versatility of the framework is demonstrated using a benchmark of option pricing problems, where F³ achieves comparable speed and energy efficiency to external manual implementations. Further, the domain-knowledge guided partitioning scheme suggests a partitioning of subtasks that is 13% faster than the average, while exploiting domain dependencies to reduce redundant computations results in an average gain in efficiency of 27%.