Symmetry-Eliminating Design Space Exploration for Hybrid Application Mapping on Many-Core Architectures

TLDR: A novel meta-heuristic DSE approach that eliminates architectural symmetries by abstracting the problem to a clustering of tasks and their mapping to processor types and shows that a DSE equipped with the novel symmetry-eliminating search space and the proposed learning techniques clearly outperforms a state-of-the-art approach known from literature in terms of the quality of the gained implementation classes.

Abstract: Large scale many-core systems are able to execute concurrently changing mixes of different parallel applications. Hybrid application mapping combines the strengths of design-time exploration/analysis of resource constellations for task-to-core mappings with the flexibility of choosing concrete mappings at run time. However, state-of-the-art design space exploration (DSE) techniques so far ignore the problem of symmetries in modern heterogeneous architectures: not only recurring patterns in the architecture but the mapping of tasks to instances of the same processor type may unnecessarily increase the search space by redundant, symmetrical implementations which typically affects the quality of the DSE. As a remedy, we propose a novel meta-heuristic DSE approach that eliminates architectural symmetries by abstracting the problem to a clustering of tasks and their mapping to processor types. However, we demonstrate that simple task clustering and type mappings may again introduce encoding symmetries in our search space. Thus, we present a formulation of the task clustering and type mapping as a 0–1 integer linear program (ILP) which eliminates all architectural as well as encoding symmetries from the search space. We also contribute a formal feasibility check to ensure that only implementations with at least one feasible concrete mapping are considered. To further improve the search process for feasible solutions, we apply satisfiability modulo theories-like learning techniques: from each infeasible implementation, we extract conditions why the implementation is infeasible and enrich our 0–1 ILP by additional constraints continuously during the DSE. Experimental results show that a DSE equipped with the novel symmetry-eliminating search space and the proposed learning techniques clearly outperforms a state-of-the-art approach known from literature in terms of the quality of the gained implementation classes.