Hierarchical energy monitoring for task mapping in many-core systems

Abstract

Executed at runtime. The proposed approach can better manage time-varying workloads and system changes.Hierarchical mapping approach. The proposed approach is implemented in a many-core managed in a hierarchical way. Such hierarchical system management improves system scalability by dividing the system into regions, each one with a manager responsible for actions inside it. Further, it reduces mapping decision computational effort, not compromising the system performance.Induces to a better system reliability. The proposed approach aims to improve energy balancing, which are directly related to a better system reliability.Hierarchical energy monitoring. The proposed approach does not employ physical sensors in the mapping decision, which increases area and energy costs. The energy data is obtained at runtime using a hierarchical monitoring approach.Clock-cycle model for validation. The proposed mapping approach is validated in a large many-core system (up to 256 processing elements), modeled in SystemC. This work addresses a research subject with a rich literature: task mapping in NoC-based systems. Task mapping is the process of selecting a processing element to execute a given task. The number of cores in many-core systems increases the complexity of the task mapping. The main concerns in task mapping in large systems include (i) scalability; (ii) dynamic workload; and (iii) reliability. It is necessary to distribute the mapping decision across the system to ensure scalability. The workload of emerging many-core systems may be dynamic, i.e., new applications may start at any moment, leading to different mapping scenarios. Therefore, it is necessary to execute the mapping process at runtime to support a dynamic workload assignment. The workload assignment plays an important role in the many-core system reliability. Load imbalance may generate hotspots zones and consequently thermal implications, which may generate hotspots zones and consequently thermal implications. More recently, task mapping techniques aiming at improving system reliability have been proposed in the literature. However, such approaches rely on centralized mapping decisions, which are not scalable. To address these challenges, the main goal of this work is to propose a hierarchical runtime mapping heuristic, which provides scalability and a fair workload distribution. Distributing the workload inside the system increases the system reliability in long-term, due to the reduction of hotspot regions. The proposed mapping heuristic considers the application workload as a function of the consumed energy in the processors and NoC routers. The proposal adopts a hierarchical energy monitoring scheme, able to estimate at runtime the consumption at each processing element. The mapping uses the energy estimated by the monitoring scheme to guide the mapping decision. Results compare the proposal against a mapping heuristic whose main cost function minimizes the communication energy. Results obtained in large systems, up to 256 cores, show improvements in the workload distribution (average value 59.2%) and a reduction in the maximum energy values spent by the processors (average value 32.2%). Such results demonstrate the effectiveness of the proposal.