Efficient Worst-Case Temperature Evaluation for Thermal-Aware Assignment of Real-Time Applications on MPSoCs

The reliability of multiprocessor system-on-chips (MPSoCs) is nowadays threatened by high chip temperatures leading to long-term reliability concerns and short-term functional errors. High chip temperatures might not only cause potential deadline violations, but also increase cooling costs and leakage power. Pro-active thermal-aware allocation and scheduling techniques that avoid thermal emergencies are promising techniques to reduce the peak temperature of an MPSoC. However, calculating the peak temperature of hundreds of design alternatives during design space exploration is time-consuming, in particular for unknown input patterns and data. In this paper, we address this challenge and present a fast analytic method to calculate a non-trivial upper bound on the maximum temperature of a multi-core real-time system with non-deterministic workload. The considered thermal model is able to address various thermal effects like heat exchange between neighboring cores and temperature-dependent leakage power. Afterwards, we integrate the proposed thermal analysis method into a design-space exploration framework to optimize the task to processing component assignment. Finally, we apply the proposed method in various case studies to explore thermal hot spots and to optimize the task to processing component assignmen

A Survey and Comparative Study of Hard and Soft Real-time Dynamic Resource Allocation Strategies for Multi/Many-core Systems

Multi-/many-core systems are envisioned to satisfy the ever-increasing performance requirements of complex applications in various domains such as embedded and high-performance computing. Such systems need to cater to increasingly dynamic workloads, requiring efficient dynamic resource allocation strategies to satisfy hard or soft real-time constraints. This article provides an extensive survey of hard and soft real-time dynamic resource allocation strategies proposed since the mid-1990s and highlights the emerging trends for multi-/many-core systems. The survey covers a taxonomy of the resource allocation strategies and considers their various optimization objectives, which have been used to provide comprehensive comparison. The strategies employ various principles, such as market and biological concepts, to perform the optimizations. The trend followed by the resource allocation strategies, open research challenges, and likely emerging research directions have also been provided

Thermal Characterization of Next-Generation Workloads on Heterogeneous MPSoCs

Next-generation High-Performance Computing (HPC) applications need to tackle outstanding computational complexity while meeting latency and Quality-of-Service constraints. Heterogeneous Multi-Processor Systems-on-Chip (MPSoCs), equipped with a mix of general-purpose cores and reconfigurable fabric for custom acceleration of computational blocks, are key in providing the flexibility to meet the requirements of next-generation HPC. However, heterogeneity brings new challenges to efficient chip thermal management. In this context, accurate and fast thermal simulators are becoming crucial to understand and exploit the trade-offs brought by heterogeneous MPSoCs. In this paper, we first thermally characterize a next-generation HPC workload, the online video transcoding application, using a highly-accurate Infra-Red (IR) microscope. Second, we extend the 3D-ICE thermal simulation tool with a new generic heat spreader model capable of accurately reproducing package surface temperature, with an average error of 6.8% for the hot spots of the chip. Our model is used to characterize the thermal behaviour of the online transcoding application when running on a heterogeneous MPSoC. Moreover, by using our detailed thermal system characterization we are able to explore different application mappings as well as the thermal limits of such heterogeneous platforms

A survey on scheduling and mapping techniques in 3D Network-on-chip

Network-on-Chips (NoCs) have been widely employed in the design of multiprocessor system-on-chips (MPSoCs) as a scalable communication solution. NoCs enable communications between on-chip Intellectual Property (IP) cores and allow those cores to achieve higher performance by outsourcing their communication tasks. Mapping and Scheduling methodologies are key elements in assigning application tasks, allocating the tasks to the IPs, and organising communication among them to achieve some specified objectives. The goal of this paper is to present a detailed state-of-the-art of research in the field of mapping and scheduling of applications on 3D NoC, classifying the works based on several dimensions and giving some potential research directions

Combined on-line lifetime-energy optimization for asymmetric multicores

In this paper we present an architectural and on-line resource management solution to optimize lifetime reliability of asymmetric multicores while minimizing the system energy consumption, targeting both single nodes (multicores) as well as multiple ones (cluster of multicores). The solution exploits the different characteristics of the computing resources to achieve the desired performance while optimizing the lifetime/energy trade-off. The experimental results show that a combined optimization of energy and lifetime allows for achieving an extended lifetime (similar to the one pursued by lifetime-only optimization solutions) with a marginal energy consumption detriment (less than 2%) with respect to energy-aware but aging-unaware systems

Heurísticas bioinspiradas para el problema de Floorplanning 3D térmico de dispositivos MPSoCs

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Arquitectura de Computadores y Automática, leída el 20-06-2013Depto. de Arquitectura de Computadores y AutomáticaFac. de InformáticaTRUEunpu

Multiprocessor System-on-Chips based Wireless Sensor Network Energy Optimization

Wireless Sensor Network (WSN) is an integrated part of the Internet-of-Things (IoT) used to monitor the physical or environmental conditions without human intervention. In WSN one of the major challenges is energy consumption reduction both at the sensor nodes and network levels. High energy consumption not only causes an increased carbon footprint but also limits the lifetime (LT) of the network. Network-on-Chip (NoC) based Multiprocessor System-on-Chips (MPSoCs) are becoming the de-facto computing platform for computationally extensive real-time applications in IoT due to their high performance and exceptional quality-of-service. In this thesis a task scheduling problem is investigated using MPSoCs architecture for tasks with precedence and deadline constraints in order to minimize the processing energy consumption while guaranteeing the timing constraints. Moreover, energy-aware nodes clustering is also performed to reduce the transmission energy consumption of the sensor nodes. Three distinct problems for energy optimization are investigated given as follows: First, a contention-aware energy-efficient static scheduling using NoC based heterogeneous MPSoC is performed for real-time tasks with an individual deadline and precedence constraints. An offline meta-heuristic based contention-aware energy-efficient task scheduling is developed that performs task ordering, mapping, and voltage assignment in an integrated manner. Compared to state-of-the-art scheduling our proposed algorithm significantly improves the energy-efficiency. Second, an energy-aware scheduling is investigated for a set of tasks with precedence constraints deploying Voltage Frequency Island (VFI) based heterogeneous NoC-MPSoCs. A novel population based algorithm called ARSH-FATI is developed that can dynamically switch between explorative and exploitative search modes at run-time. ARSH-FATI performance is superior to the existing task schedulers developed for homogeneous VFI-NoC-MPSoCs. Third, the transmission energy consumption of the sensor nodes in WSN is reduced by developing ARSH-FATI based Cluster Head Selection (ARSH-FATI-CHS) algorithm integrated with a heuristic called Novel Ranked Based Clustering (NRC). In cluster formation parameters such as residual energy, distance parameters, and workload on CHs are considered to improve LT of the network. The results prove that ARSH-FATI-CHS outperforms other state-of-the-art clustering algorithms in terms of LT.University of Derby, Derby, U

Design Space Exploration and Resource Management of Multi/Many-Core Systems

The increasing demand of processing a higher number of applications and related data on computing platforms has resulted in reliance on multi-/many-core chips as they facilitate parallel processing. However, there is a desire for these platforms to be energy-efficient and reliable, and they need to perform secure computations for the interest of the whole community. This book provides perspectives on the aforementioned aspects from leading researchers in terms of state-of-the-art contributions and upcoming trends

Online Thermal Control Methods for Multi-Processor Systems

With technological advances, the number of cores integrated on a chip is increasing. This, in turn is leading to thermal constraints and thermal design challenges. Temperature gradients and hot-spots not only affect the performance of the system, but also lead to unreliable circuit operation and affect the life-time of the chip. Meeting temperature constraints and reducing hot-spots are critical for achieving reliable and efficient operation of complex multi-core systems. In this article we analyze the use of four of the most promising families of online control techniques for thermal management of multi-processors system-on-chip (MPSoC). In particular, in our exploration we aim at achieving an online smooth thermal control action that minimizes the performance loss as well as the computational and hardware overhead of embedding a thermal management system inside the MPSoC. The definition of the optimization problem to tackle in this work considers the thermal profile of the system, its evolution over time and current time-varying workload requirements. Thus, this problem is formulated as a finite-horizon optimal control problem and we analyze the control features of different on-line thermal control approaches. In addition, we implemented the policies on an MPSoC hardware simulation platform and performed experiments on a cycle-accurate model of the 8-core Niagara multi-core architecture using benchmarks ranging from web-accessing to playing multimedia. Results show different trade-offs among the analyzed techniques regarding the thermal profile, the frequency setting, the power consumption and the implementation complexity