This paper presents semi-automatic software refactorings to introduce and tune structured parallelism in sequential Erlang code, as well as to generate code for running computations on GPUs and possibly other accelerators. Our refactorings are based on the lapedo framework for programming heterogeneous multi-core systems in Erlang. lapedo is based on the PaRTE refactoring tool and also contains (1) a set of hybrid skeletons that target both CPU and GPU processors, (2) novel refactorings for introducing and tuning parallelism, and (3) a tool to generate the GPU offloading and scheduling code in Erlang, which is used as a component of hybrid skeletons. We demonstrate, on four realistic use-case applications, that we are able to refactor sequential code and produce heterogeneous parallel versions that can achieve significant and scalable speedups of up to 220 over the original sequential Erlang program on a 24-core machine with a GPU.
|Number of pages||25|
|Journal||Concurrency and Computation: Practice and Experience|
|Early online date||24 Jun 2019|
|Publication status||Published - 25 Jul 2021|