Towards the Transparent Execution of Compound OpenCL Computations in Multi-CPU/Multi-GPU Environments

Abstract

Current computational systems are heterogeneous by nature, featuring a combination of CPUs and GPUs. As the latter are becoming an established platform for high-performance computing, the focus is shifting towards the seamless programming of the heterogeneous systems as a whole. The distinct nature of the architectural and execution models in place raise several challenges, as the best hardware configuration is behavior and data-set dependent. In this paper, we focus the execution of compound computations in multi-CPU/multi-GPU environments, in the scope of Marrow algorithmic skeleton framework, the only, to the best of our knowledge, to support skeleton nesting in GPU computing. We address how these computations may be efficiently scheduled onto the target hardware, and how the system may adapt itself to changes in the CPU's load and in the input data-set.

Distributed Calculations with Algorithmic Skeletons for Heterogeneous Computing Environments

“…FastFlow [10], SkelCL [12], Musket [13]). Hybrid execution of programs on CPUs and accelerators has been the topic in multiple approaches such as SkePU [11], Marrow [14] and Qilin [15]. SkePU and Marrow distribute the load statically between the CPU threads and the GPUs, while Qilin dynamically distributes the working packages.…”

Execution of compound multi‐kernel OpenCL computations in multi‐CPU/multi‐GPU environments

“…We claim that these characteristics can be used to: (a) hide the heterogeneity of the underlying hardware and, (b) provide tools to cope with such heterogeneity, enabling device-specific problem decompositions and optimizations. To that extent, we have been developing the Marrow algorithmic skeleton framework [8,9,10] for the orchestration of OpenCL computations. Marrow offers both data and task-parallel skeletons and is the first framework on the GPU computing field to support skeleton composition, through nesting.…”