Hierarchical energy monitoring for task mapping in many-core systems

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

Extending fireeRTOS to Support Dynamic and Distributed Mapping in Multiprocessor Systems

Multiprocessor embedded architectures are driven by power wall, performance scalability and reliability challenges. Aiming to effectively scale up system performance while meeting energy-efficiency constraints, multiprocessor architectures are dividing application workloads among multiple threads/tasks [1]. The way such tasks are mapped onto the processing elements (PEs) has a significant impact on system performance, energy-efficiency and reliability [2]. With 1000-processors platforms already available in the embedded community [3], grows the demand for distributed dynamic mapping techniques capable of allocating multi application tasks efficiently. Mapping techniques have been investigated over the last years, considering different optimization goals (e.g. energy consumption, latency, etc). Most of such mapping techniques are customized implementations, which are developed based on an in-house OS. While providing optimized and efficient means for the mapping techniques, in-house OS based implementations usually are processor-dependent. With the advance of embedded processors, at some point, it will become necessary to port such in-house implementations to a more performant or energy-efficient processor architecture. Underlying porting process is likely to lead to extra design, re-validation and, consequentially a hidden cost that may well be quite high. The goal of this work is to provide a version of FreeRTOS [4] that supports a set of distributed and dynamic task mapping heuristics, which can be easily employed by almost thirty different processor architectures. Due to the non-intrusive and flexible implementation, promoted extensions provide an efficient means not only to use and extend available heuristics but also to integrate new ones