A Scenario-Based DVFS-Aware Hybrid Application Mapping Methodology for MPSoCs

Abstract

Sound techniques for mapping soft real-time applications to resources are indispensable for meeting the application deadlines and minimizing objectives such as energy consumption, particularly on heterogeneous MPSoC architectures. For applications with input-dependent workload variations, static mappings are not able to sufficiently cope with the run-time variation, which can lead to deadline misses or unnecessary energy consumption. As a remedy, hybrid application mapping (HAM) techniques combine a design-time optimization with run-time management that adapts the mappings dynamically to the changes of the arriving input. This paper focuses on scenario-based HAM techniques. Here, the application input space is systematically clustered such that data inside the same scenario exhibit similar characteristics concerning workload when being processed under the same operating points. This static clustering of the input space into data scenarios has proven to be a good abstraction layer for simplifying the design and employment of high-quality run-time managers. However, existing state-of-the-art scenario-based HAM approaches neglect or underutilize the synergistic interplay between mapping selection and the usage of dynamic voltage/frequency scaling (DVFS) when adapting to workload variation. By combining mapping and DVFS selection, variations in the input can be either compensated by a complete re-mapping of the application, evoking a potential high reconfiguration overhead or by just changing the DVFS settings of the resources, offering a low-overhead adaptation alternative and thus significantly reducing the necessary overhead compared to DVFS-agnostic HAM. Furthermore, DVFS enables a fine-grained adaptation of a mapped application to the input data variation, e.g., by slowing down tasks with no impact on the end-to-end latency for the current input using low-frequency DVFS settings. It is shown that this combined approach can save even more energy than a pure mapping adaptation scheme, especially in the presence of data scenarios. In particular, scenario-based design operates as a catalyst for eliciting the synergies between a combined DVFS and mapping optimization and the peculiarities inside a data scenario, i.e., exploiting the commonalities inside a data scenario by perfectly tailored DVFS settings and task mapping. In this scope, this paper proposes two supplementary scenario-based DVFS-aware HAM approaches that consistently outperform existing state-of-the-art mapping approaches in terms of the number of deadline misses and energy consumption as we demonstrate in an empirical study on the basis of four different applications and three different architectures. It is also shown that these benefits still apply to target architectures with increasing mapping migration overheads, thwarting frequent mapping reconfigurations.