Refactoring for introducing and tuning parallelism for heterogeneous multicore machines in Erlang

Abstract

Summary 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.

Designing a Modern Skeleton Programming Framework for Parallel and Heterogeneous Systems

“…Recent work in hybrid CPU-GPU execution of skeleton-like programming constructs include Lapedo [36], an extension of the Skel Erlang library for stream-based skeleton programming, specifically providing hybrid variants of the Farm and Cluster skeletons where the workload partitioning is tuned by models built through performance benchmarking; and Vilches' et al [52] TBB-based heterogeneous parallel for template, which actively monitors the load balance and adjusts the partitioning during the execution of the for loop. Both approaches exclusively use OpenCL for GPU-based computation.…”