The race toward performance increase and computing power has led to chips with heterogeneous and complex designs, integrating an ever-growing number of cores on the same monolithic chip or chiplet silicon die. Higher integration density, compounded with the slowdown of technology-driven power reduction, implies that power and thermal management become increasingly relevant. Unfortunately, existing research lacks a detailed analysis and modeling of thermal, power, and electrical coupling effects and how they have to be jointly considered to perform dynamic control of complex and heterogeneous Multi-Processor System on Chips (MPSoCs). To close the gap, in this work, we first provide a detailed thermal and power model targeting a modern High Performance Computing (HPC) MPSoC. We consider real-world coupling effects such as actuators’ non-idealities and the exponential relation between the dissipated power, the temperature state, and the voltage level in a single processing element. We analyze how these factors affect the control algorithm behavior and the type of challenges that they pose. Based on the analysis, we propose a thermal capping strategy inspired by Fuzzy control theory to replace the state-of-the-art PID controller, as well as a root-finding iterative method to optimally choose the shared voltage value among cores grouped in the same voltage domain. We evaluate the proposed controller with model-in-the-loop and hardware-in-the-loop co-simulations. We show an improvement over state-of-the-art methods of up to \(5\times\) the maximum exceeded temperature while providing an average of \(3.56\%\) faster application execution runtime across all the evaluation scenarios.

In novel mechatronics enabled by smart structures and materials, servomechanisms are becoming increasingly complex, requiring computationally intensive advanced control algorithms and diagnostic tools to fully exploit their potential. This calls for a significant increase in computational power while guaranteeing hard real-time features. In this work, we propose to address such an issue by adopting recently-emerged edge-computing solutions exploiting low-cost multicore that combine microcontrollers and microprocessors to boost the computational capability. However, such platforms are usually endowed with nonreal-time software infrastructure, assigning a dominant role to microprocessors and leading to large overheads and unpredictability. Therefore, to tame them for hard real time, we first lighten the infrastructure to enable one or more microprocessors to handle computations with minimal overhead and jitter. Then, we designate a microcontroller as the platform master of time and tasks, off-loading the heavy computations to the “relieved” microprocessor cores, acting now as computational slaves. We assess the potentials of this approach with a basic test using a demanding control algorithm as a benchmark, choosing the STM32MP157 as the reference platform and using the Jailhouse hypervisor to adapt one of its microprocessor cores for hard real-time tasks.

Complex computing platforms such as High Performance Computers and Data Centers are critical systems from the energy sustainability viewpoint, due to their high computational power and demanding thermal stability specifications. In this context, cooling is a crucial component to operate such systems efficiently. Adavanced solutions, based on liquid and hybrid topologies are available today, but they come with a twofold challenge. On one hand, as widely recognized in the literature, the cooling devices need to be operated in a coordinated and energy-efficient fashion. In addition, after design and deployment, the cooling system has to be dynamically managed to efficiently adapt to workload, and environmental conditions. On the other hand, at design time, the cooling hardware architecture has to be selected in order to fit in the best way the needs of the computing facility, also depending on the environmental conditions characterizing its location. This work presents a flexible, low-complexity modeling tool to describe the overall thermal behavior of complex computational platforms, as well as the effect of the diverse cooling components, and the corresponding energy consumption. Analytical modeling equations, stemming from physical first principles, are used, thus providing a compact and computationally manageable tool. This can be then exploited to explore the design space, choosing the correct cooling configuration, and/or define energy-optimal holistic cooling strategies, for complex, multidimensional, and hard constrained systems such as today SuperComputers. The proposed method is presented in general terms, then validated on a case study of a real-life HPC system with a hybrid cooling architecture.

In the last decade, high performance multi-core processor designs have followed an increase in number of cores, interfaces, heterogeneity and System-on-chip (SoC) complexity. HPC applications also require tailored chip designs with specific operating points and performance indexes. In this scenario, an advanced and configurable Power Controller System (PCS) is necessary to meet power and thermal constraints, without the necessity of static ultra-conservative margins on the operating points. In this paper, we propose an open-source PCS design, based on a parallel ultra-low power microcontroller with RISC-V cores, and an open-source software environment based on a Real-time operating system (RTOS) with a configurable Power-thermal control algorithm. Considering a 1ms control interval, the overhead of the RTOS is about 6% of the cycles in the nominal case. The control algorithm is able to limit temperature and power consumption within given bounds, while maximizing performance. The PCS is able to control up to 76 different cores/computing units with headroom for larger core counts.

A novel approach is proposed to compare Brushless DC control and Field Oriented Control performance in driving Permanent Magnet Synchronous Machines for multirotor UAVs, a particular class of small-size electrically-powered UAVs. Both power efficiency and output torque quality are analyzed in depth to carry out causes and consequences of control issues in both cases. Power losses and torque ripple contributions, due to driving techniques and converter non idealities, are decoupled and then exploited to show how large the torque oscillations are in case of Brushless DC control, and to highlight the higher efficiency given by Field Oriented Control, which can be exploited for enhancing the flight endurance.

In this paper, the performance of wind turbine speed control under line voltage dips is under scope. The focus is put on the post-fault behavior of the turbine mechanics, when the system has to be quickly steered back to the pre-fault optimal point without violating the generator torque limits, and avoiding to trigger drivetrain oscillatory modes. To this aim, a low computational burden control solution is proposed, combining a high-bandwidth-feedback pole placement strategy with a suitable state reference trajectory planning. A minimal recovery time is pursued in the trajectory design. The computational burden of optimization is shifted offline, evaluating the minimum time trajectories for a reasonable set of post-fault scenarios, and relying on a look up table to handle generic runtime faulty conditions. Simulations assess the promising performance of the proposed strategy.

In this paper, the problem of controlling the stator currents in Doubly-Fed Induction Machines is considered under large symmetric and asymmetric variations of the line voltage. This point is crucial to use profitably such kind of machines in electric power generation by wind turbines, since their stator windings are connected directly to the line grid, and only the rotor ones are supplied by controlled voltage-source converters. Taking the cue from the issues affecting standard feedback linearizing solutions, an output controller is considered and a suitable trajectory planning is proposed for the marginally stable zero-dynamics. This allows preventing large oscillations in rotor fluxes and command voltages, usually triggered by line voltage variations. Simulations show the effectiveness of the proposed approach.

Advanced cooling systems and optimization strategies are critical to operate modern supercomputers and high-performance computing systems in an energy-efficient fashion. Hybrid architectures combining emerging liquid cooling with traditional air cooling are a promising solution. Standard management techniques maintain these systems at fixed operating points, typically without coordination between the diverse cooling knobs. In this paper, we propose an energy-aware optimization strategy exploiting heterogeneous cooling systems in a holistic fashion with the goal of minimizing the overall cooling system power consumption, while at the same time meeting the system thermal constraints. To this purpose, we developed a modeling approach to build a low-order analytical model, which captures the overall thermal behavior of the system. Then, this compact and computationally manageable model is exploited to set and solve a treatable optimization problem, leading to definition of an energy-optimal cooling strategy. The proposed method is presented taking Galileo as real-life case study. Galileo is a high-performance computing system with hybrid cooling architecture recently installed at CINECA (a supercomputing facility located in Italy). The cooling strategy resulting from the proposed approach is compared with common strategies in order to assess the efficiency advantages.

Today and future many-core systems are facing the utilization wall and dark silicon problems, for which not all the processing engines can be powered at the same time as this will lead to a power consumption higher than the Total Design Power (TDP) budget. Recently, computational sprinting approaches addressed the problem by exploiting the intrinsic thermal capacitance of the chip and the properties of common applications, which require intense, but temporary, use of resources. The thermal capacitance, possibly augmented with phase change materials, enables the temporary activation of all the resources simultaneously, although they largely exceed the steady-state thermal design power. In this article, we present an innovative and low-overhead hierarchical model-predictive controller for managing thermally safe sprinting with predictable resprinting rate, which ensures the correct execution of mixed-criticality tasks. Well-targeted simulations, also based on real workload benchmarks, show the applicability and the effectiveness of our solution.

Aggressive thermal management is a critical feature for high-end computing platforms, as worst-case thermal budgeting is becoming unaffordable. Reactive thermal management, which sets temperature thresholds to trigger thermal capping actions, is too “near-sighted,” and it may lead to severe performance degradation and thermal overshoots. More aggressive proactive thermal managements minimize performance penalty with smooth optimal control. These techniques require knowledge of thermal models, which have to be accurate and simple to make the controls effective, while keeping their complexity limited. In practice, these models are not provided by manufacturers, and in most cases, they strongly depend on the deployment environment. Hence, procedures to automatically derive thermal models in the field are needed. In this paper, we propose a gray-box procedure to learn a compact and physically consistent model for multicore chips. We leverage the physical consistency of the proposed model to tame the model complexity and to face large quantization noise in measurements. We exploit Output Error structures along with Levenberg–Marquardt and Least Squares optimization algorithms. We tackle the problem in a real-life contest: we developed a complete infrastructure for model building and thermal data collection in the Linux environment, and we tested it on an Intel Nehalem-based server CPU.

Compact thermal models and modeling strategies are today a cornerstone for advanced power management to counteract the emerging thermal crisis for many-core systems-on-chip. System identification techniques allow to extract models directly from the target device thermal response. Unfortunately, standard Least Squares techniques cannot effectively cope with both model approximation and measurement noise typical of real systems. In this work, we present a novel distributed identification strategy capable of coping with real-life temperature sensor noise and effectively extracting a set of low-order predictive thermal models for the tiles of Intel's Single-chip-Cloud-Computer (SCC) many-core prototype.

As result of technology scaling, single-chip multicore power density increases and its spatial and temporal workload variation leads to temperature hot-spots, which may cause nonuniform ageing and accelerated chip failure. These critical issues can be tackled by closed-loop thermal and reliability management policies. Model predictive controllers (MPC) outperform classic feedback controllers since they are capable of minimizing performance loss while enforcing safe working temperature. Unfortunately, MPC controllers rely on a priori knowledge of thermal models and their complexity exponentially grows with the number of controlled cores. In this paper, we present a scalable, fully distributed, energy-aware thermal management solution for single-chip multicore platforms. The model-predictive controller complexity is drastically reduced by splitting it in a set of simpler interacting controllers, each one allocated to a core in the system. Locally, each node selects the optimal frequency to meet temperature constraints while minimizing the performance penalty and system energy. Comparable performance with state-of-the-art MPC controllers is achieved by letting controllers exchange a limited amount of information at runtime on a neighborhood basis. In addition, we address model uncertainty by supporting learning of the thermal model with a novel distributed self-calibration approach that matches well the controller architecture.

High-end multicore processors are characterized by high power density with significant spatial and temporal variability. This leads to power and temperature hot-spots, which may cause non-uniform ageing and accelerated chip failure. These critical issues can be tackled on-line by closed-loop thermal and reliability management policies. Model predictive controllers (MPC) outperform classic feedback controllers since they are capable of minimizing a cost function while enforcing safe working temperature. Unfortunately basic MPC controllers rely on a-priori knowledge of multicore thermal model and their complexity exponentially grows with the number of controlled cores. In this paper we present a scalable, fully-distributed, energy-aware thermal management solution. The model-predictive controller complexity is drastically reduced by splitting it in a set of simpler interacting controllers, each allocated to a core in the system. Locally, each node selects the optimal frequency to meet temperature constraints while minimizing the performance penalty and system energy. Global optimality is achieved by letting controllers exchange a limited amount of information at run-time on a neighbourhood basis. We address model uncertainty by supporting learning of the thermal model with a novel distributed self-calibration approach that matches well the controller architecture.

Control system design is a crucial point in modern automation systems, involving an increasingly broader set of activities being performed by software. Despite the availability of several domain-specific facilities, designers daily face kindred issues which actually lack reference patterns, in particular as regards the architectural organization of the control logic. In this paper we present an approach that, focusing on cross-cutting domain problems and suitably abstracting from application-specific details, leads to an effective decoupling of high-level control policies from low-level actuation and sensing mechanisms. Logic Device Agents emerge as generally applicable active components modeling the behavior of commonly used field devices, which can be profitably exploited to enhance software design quality and productivity.

The use of high-end multicore processors today can incur high power density with significant variability in spatial and temporal usage of resources by workloads. This situation leads to power and temperature hotspots, which in turn may lead to non-uniform ageing and accelerated chip failure. These drawbacks can be mitigated by online tuning of system performance and adopting closed-loop thermal and reliability management policies. The development and evaluation of these policies cannot be performed solely on real hardware - due to observability and flexibility limitations or just by relying on trace-driven simulation, due to dependencies present among power, thermal effects, reliability and performance. We present a complete and virtual platform to develop, simulate and evaluate power, temperature and reliability management control strategies for high-performance multicores. The accuracy and effectiveness of our solution are ensured by integrating a established system simulator (Simics) with models for power consumption, temperature distribution and aging. The models are based on characterization on real hardware. Control strategies exploration and design are carried out in the MATLAB/Simulink framework allowing the use of control theory tools. Fast prototyping is achieved by developing a suitable interface between Simics and MATLAB/Simulink, enabling co-simulation of hardware platforms and controllers.

In this work a general partitioning of logic control design strategies into two main approaches, cooperative and hierarchical, is proposed and some lines for a comparison are drawn. In the authors' opinion, the cooperative approach basically collects methods inspired by IEC 61499 and agent paradigms while, the hierarchical approach, generally collects design procedures inspired by IEC 61131. Among the elements of the latter category, particular attention is devoted to the design methodology based on the Generalized Actuator framework, recently proposed by the authors. A case-study is considered to derive some starting considerations on the properties of the considered approaches.

Aim of this work is to present a rapid-prototyping procedure for logic control of automated manufacturing systems. The presented approach relies on the Generalized Actuator concept, by which, during the design phase, it is possible to keep policies separated from actuation mechanisms. This approach is helpful in rapid prototyping because it allows the design of modular/hierarchical control algorithms, leading also to the possibility of validating the controller following a modular and hierarchical strategy. The effectiveness of the proposed approach has been shown on a micro flexible manufacturing system.