Executing PRAM Programs on GPUs

We present a framework to transform PRAM programs from the PRAM programming language Fork to CUDA C, so that they can be compiled and executed on a Graphics Processor (GPU). This allows to explore parallel algorithmics on a scale beyond toy problems, to which the previous, sequential PRAM simulator restricted practical use. We explain the design decisions and evaluate a prototype implementation consisting of a runtime library and a set of rules to transform simple Fork programs which we for now apply by hand. The resulting CUDA code is almost 100 times faster than the previous simulator for compiled Fork programs and allows to handle larger data sizes. Compared to a sequential program for the same problem, the GPU code might be faster or slower, depending on the Fork program structure, i.e. on the overhead incurred. We also give an outlook how future GPUs might notably reduce the overhead.