Automatic Operating Point Distillation for Hybrid Mapping Methodologies

Behnaz Pourmohseni1,a, Michael Glaß2, Jürgen Teich1,b
1Friedrich-Alexander-Universität Erlangen-N\''urnberg (FAU), Germany.
2Ulm University, Germany.


Efficient execution of applications on heterogeneous many-core platforms requires mapping solutions that address different aspects of run-time dynamism like resource availability, energy budgets, and timing requirements. Hybrid mapping methodologies employ a static design space exploration (DSE) to obtain a set of mapping alternatives termed operating points that trade off quality properties (compute performance, energy consumption, etc.) and resource requirements (number of allocated resources of each type, etc.) among which one is selected at runtime by a run-time resource manager (RRM). Given multiple quality properties and the presence of heterogeneous resources, the DSE typically delivers a substantially large set of operating points handling of which may impose an intolerable run-time overhead to the RRM. This paper investigates the problem of truncation of operating points termed operating point distillation, such that (a) an acceptable run-time overhead is achieved, (b) online quality requirements are met, and (c) dynamic resource constraints are satisfied, i.e., application embeddability is preserved. We propose an automatic design-time distillation methodology that employs a hyper grid-based approach to retain diverse tradeoff options wrt. quality properties, while selecting representative operating points based on their resource requirements to achieve a high level of run-time embeddability. Experimental results for a variety of applications show that compared to existing truncation approaches, proposed methodology significantly enhances the run-time embeddability while achieving a competitive and often improved efficiency in the distilled quality properties.

Keywords: Operating point distillation, Hybrid application mapping, Design space exploration, Run-time management, Heterogeneous many-core systems.

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