Temperature Regulation in Multicore Processors Using Adjustable-Gain Integral Controllers

This paper considers the problem of temperature regulation in multicore processors by dynamic voltage-frequency scaling. We propose a feedback law that is based on an integral controller with adjustable gain, designed for fast tracking convergence in the face of model uncertainties, time-varying plants, and tight computing-timing constraints. Moreover, unlike prior works we consider a nonlinear, time-varying plant model that trades off precision for simple and efficient on-line computations. Cycle-level, full system simulator implementation and evaluation illustrates fast and accurate tracking of given temperature reference values, and compares favorably with fixed-gain controllers.Comment: 8 pages, 6 figures, IEEE Conference on Control Applications 2015, Accepted Versio

2018 International Symposium on Computer Architecture influential paper award

The International Symposium on Computer Architecture (ISCA) recognizes every year the most influential paper published in this conference 15 years earlier, based on its impact on research, development, products or ideas. This award is sponsored by the IEEEComputer Society Technical Committee on Computer Architecture (IEEE-CS TCCA) and the ACM Special Interest Group on Computer Architecture (ACM SIGARCH). In this year’s edition, the candidate papers were those papers published in ISCA 2003 proceedings.The selection process was chaired by Antonio González. Candidate papers for the award were selected by the current year’s ISCA Pro-gram Committee. The final award selection was made by the Award Chair (Antonio González), the IEEE-CS TCCA Chair (Lieven Eeckhout) and the ACM SIGARCH Chair (Sarita Adve). The award includes an honorarium for the authors and a certificate.The 2018 award was presented to “Temperature-Aware Microarchitecture” by Kevin Skadron, Mircea R. Stan, Wei Huang, Sivakumar Velusamy, Karthik Sankaranarayanan and DavidTarjan.Peer ReviewedPostprint (author's final draft

Using MCD-DVS for dynamic thermal management performance improvement

With chip temperature being a major hurdle in microprocessor design, techniques to recover the performance loss due to thermal emergency mechanisms are crucial in order to sustain performance growth. Many techniques for power reduction in the past and some on thermal management more recently have contributed to alleviate this problem. Probably the most important thermal control technique is dynamic voltage and frequency scaling (DVS) which allows for almost cubic reduction in power with worst-case performance penalty only linear. So far, DVS techniques for temperature control have been studied at the chip level. Finer grain DVS is feasible if a globally-asynchronous locally-synchronous (GALS) design style is employed. GALS, also known as multiple-clock domain (MCD), allows for an independent voltage and frequency control for each one of the clock domains that are part of the chip. There are several studies on DVS for GALS that aim to improve energy and power efficiency but not temperature. This paper proposes and analyses the usage of DVS at the domain level to control temperature in a clustered MCD microarchitecture with the goal of improving the performance of applications that do not meet the thermal constraints imposed by the designers.Peer ReviewedPostprint (published version

A Control-Theoretic Design And Analysis Framework For Resilient Hard Real-Time Systems

We introduce a new design metric called system-resiliency which characterizes the maximum unpredictable external stresses that any hard-real-time performance mode can withstand. Our proposed systemresiliency framework addresses resiliency determination for real-time systems with physical and hardware limitations. Furthermore, our framework advises the system designer about the feasible trade-offs between external system resources for the system operating modes on a real-time system that operates in a multi-parametric resiliency environment. Modern multi-modal real-time systems degrade the system’s operational modes as a response to unpredictable external stimuli. During these mode transitions, real-time systems should demonstrate a reliable and graceful degradation of service. Many control-theoretic-based system design approaches exist. Although they permit real-time systems to operate under various physical constraints, none of them allows the system designer to predict the system-resiliency over multi-constrained operating environment. Our framework fills this gap; the proposed framework consists of two components: the design-phase and runtime control. With the design-phase analysis, the designer predicts the behavior of the real-time system for variable external conditions. Also, the runtime controller navigates the system to the best desired target using advanced control-theoretic techniques. Further, our framework addresses the system resiliency of both uniprocessor and multicore processor systems. As a proof of concept, we first introduce a design metric called thermal-resiliency, which characterizes the maximum external thermal stress that any hard-real-time performance mode can withstand. We verify the thermal-resiliency for the external thermal stresses on a uniprocessor system through a physical testbed. We show how to solve some of the issues and challenges of designing predictable real-time systems that guarantee hard deadlines even under transitions between modes in an unpredictable thermal environment where environmental temperature may dynamically change using our new metric. We extend the derivation of thermal-resiliency to multicore systems and determine the limitations of external thermal stress that any hard-real-time performance mode can withstand. Our control-theoretic framework allows the system designer to allocate asymmetric processing resources upon a multicore proiii cessor and still maintain thermal constraints. In addition, we develop real-time-scheduling sub-components that are necessary to fully implement our framework; toward this goal, we investigate the potential utility of parallelization for meeting real-time constraints and minimizing energy. Under malleable gang scheduling of implicit-deadline sporadic tasks upon multiprocessors, we show the non-necessity of dynamic voltage/frequency regarding optimality of our scheduling problem. We adapt the canonical schedule for DVFS multiprocessor platforms and propose a polynomial-time optimal processor/frequency-selection algorithm. Finally, we verify the correctness of our framework through multiple measurable physical and hardware constraints and complete our work on developing a generalized framework

Temperature-Aware Leakage Minimization Techniques for Real-Time Systems

In this paper, we study the interdependencies between system's leakage and on-chip temperature. We show that the temperature variation caused by on-chip heat accumulation has a large impact in estimating the system's leakage energy. More importantly, we propose an online temperature-aware leakage minimization technique to demonstrate how to incorporate the temperature information to reduce energy consumption at real time. The basic idea is to run when the system is cool and the workload is high and to put the system to sleep when it is hot and the workload is light. The online algorithm has low run-time complexity and achieves significant leakage energy saving. In fact, we are able to get about 25% leakage reduction on both real life and artificial benchmarks. Comparing to our optimal offline algorithm, the above online algorithm provides similar energy savings with similar decisions on how to put the system to sleep and how to wake it up. Finally, our temperature-aware leakage minimization techniques can be combined with existing DVS methods to improve the total energy efficiency by further saving on leakage

Thermal analysis and modeling of embedded processors

This paper presents a complete modeling approach to analyze the thermal behavior of microprocessor-based systems. While most compact modeling approaches require a deep knowledge of the implementation details, our method defines a black box technique which can be applied to different target processors when this detailed information is unknown. The obtained results show high accuracy, applicability and can be easily automated. The proposed methodology has been used to study the impact of code transformations in the thermal behavior of the chip. Finally, the analysis of the thermal effect of the source code modifications can be included in a temperature-aware compiler which minimizes the total temperature of the chip, as well as the temperature gradients, according to these guidelines

Dynamic Thermal and Power Management: From Computers to Buildings

Thermal and power management have become increasingly important for both computing and physical systems. Computing systems from real-time embedded systems to data centers require effective thermal and power management to prevent overheating and save energy. In the mean time, as a major consumer of energy buildings face challenges to reduce the energy consumption for air conditioning while maintaining comfort of occupants. In this dissertation we investigate dynamic thermal and power management for computer systems and buildings. (1) We present thermal control under utilization bound (TCUB), a novel control-theoretic thermal management algorithm designed for single core real-time embedded systems. A salient feature of TCUB is to maintain both desired processor temperature and real-time performance. (2) To address unique challenges posed by multicore processors, we develop the real-time multicore thermal control (RT-MTC) algorithm. RT-MTC employs a feedback control loop to enforce the desired temperature and CPU utilization of the multicore platform via dynamic frequency and voltage scaling. (3) We research dynamic thermal management for real-time services running on server clusters. We develop the control-theoretic thermal balancing (CTB) to dynamically balance temperature of servers via distributing clients\u27 service requests to servers. Next, (4) we propose CloudPowerCap, a power cap management system for virtualized cloud computing infrastructure. The novelty of CloudPowerCap lies in an integrated approach to coordinate power budget management and resource management in a cloud computing environment. Finally we expand our research to physical environment by exploring several fundamental problems of thermal and power management on buildings. We analyze spatial and temporal data acquired from an real-world auditorium instrumented by a multi-modal sensor network. We propose a data mining technique to determine the appropriate number and location of temperature sensors for estimating the spatiotemporal temperature distribution of the auditorium. Furthermore, we explore the potential energy savings that can be achieved through occupancy-based HVAC scheduling based on real occupancy data of the auditorium