Autonomic Coordination of Skeleton-Based Applications Over CPU/GPU Multi-Core Architectures

Widely adumbrated as patterns of parallel computation and communication, algorithmic skeletons introduce a viable solution for efficiently programming modern heterogeneous multi-core architectures equipped not only with traditional multi-core CPUs, but also with one or more programmable Graphics Processing Units (GPUs). By systematically applying algorithmic skeletons to address complex programming tasks, it is arguably possible to separate the coordination from the computation in a parallel program, and therefore subdivide a complex program into building blocks (modules, skids, or components) that can be independently created and then used in different systems to drive multiple functionalities. By exploiting such systematic division, it is feasible to automate coordination by addressing extra-functional and non-functional features such as application performance, portability, and resource utilisation from the component level in heterogeneous multi-core architectures. In this paper, we introduce a novel approach to exploit the inherent features of skeleton-based applications in order to automatically coordinate them over heterogeneous (CPU/GPU) multi-core architectures and improve their performance. Our systematic evaluation demonstrates up to one order of magnitude speed-up on heterogeneous multi-core architectures.