On the support of task-parallel algorithmic skeletons for multi-GPU computing

An emerging trend in the field of Graphics Processing Unit (GPU) computing is the harnessing of multiple devices to cope with scalability and performance requirements. However, multi-GPU execution adds new challenges to the already complex world of General Purpose computing on GPUs (GPGPU), such as the efficient problem decomposition, and dealing with device heterogeneity. To this extent, we propose the use of the Marrow algorithmic skeleton framework (ASkF) to abstract most of the details intrinsic to the programming of such platforms. To the best of our knowledge, Marrow is the first ASkF to support skeleton nesting on single and (now) multiple GPU systems. In this paper we present how it can transparently distribute the execution of skeleton compositions among a set of, possibly, heterogeneous devices. An experimental evaluation assesses the proposal's effectiveness, from a scalability and performance perspective, with good results.