Financial software on GPUs
Cosmin E. Oancea, Christian Andreetta, Jost Berthold, Alain Frisch, Fritz Henglein · 2012
This paper presents a real-world pricing kernel for financial derivatives and evaluates the language and compiler tool chain that would allow expressive, hardware-neutral algorithm implementation and efficient execution on graphics-processing units (GPU). The language issues refer to preserving algorithmic invariants, e.g., inherent parallelism made explicit by map-reduce-scan functional combinators. Efficient execution is achieved by manually; applying a series of generally-applicable compiler transformations that allows the generated-OpenCL code to yield speedups as high as 70x and 540x on a commodity mobile and desktop GPU, respectively.