Thermally-aware composite run-time CPU power models

Accurate and stable CPU power modelling is fundamental in modern system-on-chips (SoCs) for two main reasons: 1) they enable significant online energy savings by providing a run-time manager with reliable power consumption data for controlling CPU energy-saving techniques; 2) they can be used as accurate and trusted reference models for system design and exploration. We begin by showing the limitations in typical performance monitoring counter (PMC) based power modelling approaches and illustrate how an improved model formulation results in a more stable model that efficiently captures relationships between the input variables and the power consumption. Using this as a solid foundation, we present a methodology for adding thermal-awareness and analytically decomposing the power into its constituting parts. We develop and validate our methodology using data recorded from a quad-core ARM Cortex-A15 mobile CPU and we achieve an average prediction error of 3.7% across 39 diverse workloads, 8 Dynamic Voltage-Frequency Scaling (DVFS) levels and with a CPU temperature ranging from 31 degrees C to 91 degrees C. Moreover, we measure the effect of switching cores offline and decompose the existing power model to estimate the static power of each CPU and L2 cache, the dynamic power due to constant background (BG) switching, and the dynamic power caused by the activity of each CPU individually. Finally, we provide our model equations and software tools for implementing in a run-time manager or for using with an architectural simulator, such as gem5

Effective electrothermal analysis of electronic devices and systems with parameterized macromodeling

We propose a parameterized macromodeling methodology to effectively and accurately carry out dynamic electrothermal (ET) simulations of electronic components and systems, while taking into account the influence of key design parameters on the system behavior. In order to improve the accuracy and to reduce the number of computationally expensive thermal simulations needed for the macromodel generation, a decomposition of the frequency-domain data samples of the thermal impedance matrix is proposed. The approach is applied to study the impact of layout variations on the dynamic ET behavior of a state-of-the-art 8-finger AlGaN/GaN high-electron mobility transistor grown on a SiC substrate. The simulation results confirm the high accuracy and computational gain obtained using parameterized macromodels instead of a standard method based on iterative complete numerical analysis

Thermal aware task assignment for multicore processors using genetic algorithm

Microprocessor power and thermal density are increasing exponentially. The reliability of the processor declined, cooling costs rose, and the processor's lifespan was shortened due to an overheated processor and poor thermal management like thermally unbalanced processors. Thus, the thermal management and balancing of multi-core processors are extremely crucial. This work mostly focuses on a compact temperature model of multicore processors. In this paper, a novel task assignment is proposed using a genetic algorithm to maintain the thermal balance of the cores, by considering the energy expended by each task that the core performs. And expecting the cores’ temperature using the hotspot simulator. The algorithm assigns tasks to the processors depending on the task parameters and current cores’ temperature in such a way that none of the tasks’ deadlines are lost for the earliest deadline first (EDF) scheduling algorithm. The mathematical model was derived, and the simulation results showed that the highest temperature difference between the cores is 8 °C for approximately 14 seconds of simulation. These results validate the effectiveness of the proposed algorithm in managing the hotspot and reducing both temperature and energy consumption in multicore processors

A Service-Oriented Co-Simulation: Holistic Data Center Modelling Using Thermal, Power and Computational Simulations

Holistic modelling of a data center to include both thermodynamics and computational processes has the potential to revolutionize how data centers are designed and managed. Such a model is inherently multi-disciplinary, bringing together the computational elements studied by computer scientists; thermodynamics studied by mechanical engineers; and other aspects in the domain of electrical engineering. This paper proposes the use of the Internet of Simulation to allow engineers to build models of individual complex elements and deploy them as simulation services. These services can then be integrated as simulation system workflows. A proof of concept server simulation is presented, incorporating simulations of CPUs, heat sinks, and fans exposed using the SIMaaS paradigm. The integrated workflow of the server is then exposed as a service (WFaaS) to facilitate the building of an entire virtual data center. Unlike other data center simulations, this approach requires no direct characterisation of the hardware being simulated. Preliminary results are presented showing the effectiveness of the simulation technique and representative behaviour under various simulated cloud workloads. The benefits and future applications of this rapid prototyping approach extend to data center design and data center efficiency research

Improving processor efficiency through thermal modeling and runtime management of hybrid cooling strategies

One of the main challenges in building future high performance systems is the ability to maintain safe on-chip temperatures in presence of high power densities. Handling such high power densities necessitates novel cooling solutions that are significantly more efficient than their existing counterparts. A number of advanced cooling methods have been proposed to address the temperature problem in processors. However, tradeoffs exist between performance, cost, and efficiency of those cooling methods, and these tradeoffs depend on the target system properties. Hence, a single cooling solution satisfying optimum conditions for any arbitrary system does not exist. This thesis claims that in order to reach exascale computing, a dramatic improvement in energy efficiency is needed, and achieving this improvement requires a temperature-centric co-design of the cooling and computing subsystems. Such co-design requires detailed system-level thermal modeling, design-time optimization, and runtime management techniques that are aware of the underlying processor architecture and application requirements. To this end, this thesis first proposes compact thermal modeling methods to characterize the complex thermal behavior of cutting-edge cooling solutions, mainly Phase Change Material (PCM)-based cooling, liquid cooling, and thermoelectric cooling (TEC), as well as hybrid designs involving a combination of these. The proposed models are modular and they enable fast and accurate exploration of a large design space. Comparisons against multi-physics simulations and measurements on testbeds validate the accuracy of our models (resulting in less than 1C error on average) and demonstrate significant reductions in simulation time (up to four orders of magnitude shorter simulation times). This thesis then introduces temperature-aware optimization techniques to maximize energy efficiency of a given system as a whole (including computing and cooling energy). The proposed optimization techniques approach the temperature problem from various angles, tackling major sources of inefficiency. One important angle is to understand the application power and performance characteristics and to design management techniques to match them. For workloads that require short bursts of intense parallel computation, we propose using PCM-based cooling in cooperation with a novel Adaptive Sprinting technique. By tracking the PCM state and incorporating this information during runtime decisions, Adaptive Sprinting utilizes the PCM heat storage capability more efficiently, achieving 29\% performance improvement compared to existing sprinting policies. In addition to the application characteristics, high heterogeneity in on-chip heat distribution is an important factor affecting efficiency. Hot spots occur on different locations of the chip with varying intensities; thus, designing a uniform cooling solution to handle worst-case hot spots significantly reduces the cooling efficiency. The hybrid cooling techniques proposed as part of this thesis address this issue by combining the strengths of different cooling methods and localizing the cooling effort over hot spots. Specifically, the thesis introduces LoCool, a cooling system optimizer that minimizes cooling power under temperature constraints for hybrid-cooled systems using TECs and liquid cooling. Finally, the scope of this work is not limited to existing advanced cooling solutions, but it also extends to emerging technologies and their potential benefits and tradeoffs. One such technology is integrated flow cell array, where fuel cells are pumped through microchannels, providing both cooling and on-chip power generation. This thesis explores a broad range of design parameters including maximum chip temperature, leakage power, and generated power for flow cell arrays in order to maximize the benefits of integrating this technology with computing systems. Through thermal modeling and runtime management techniques, and by exploring the design space of emerging cooling solutions, this thesis provides significant improvements in processor energy efficiency.2018-07-09T00:00:00

An Intelligent Framework for Energy-Aware Mobile Computing Subject to Stochastic System Dynamics

abstract: User satisfaction is pivotal to the success of mobile applications. At the same time, it is imperative to maximize the energy efficiency of the mobile device to ensure optimal usage of the limited energy source available to mobile devices while maintaining the necessary levels of user satisfaction. However, this is complicated due to user interactions, numerous shared resources, and network conditions that produce substantial uncertainty to the mobile device's performance and power characteristics. In this dissertation, a new approach is presented to characterize and control mobile devices that accurately models these uncertainties. The proposed modeling framework is a completely data-driven approach to predicting power and performance. The approach makes no assumptions on the distributions of the underlying sources of uncertainty and is capable of predicting power and performance with over 93% accuracy. Using this data-driven prediction framework, a closed-loop solution to the DEM problem is derived to maximize the energy efficiency of the mobile device subject to various thermal, reliability and deadline constraints. The design of the controller imposes minimal operational overhead and is able to tune the performance and power prediction models to changing system conditions. The proposed controller is implemented on a real mobile platform, the Google Pixel smartphone, and demonstrates a 19% improvement in energy efficiency over the standard frequency governor implemented on all Android devices.Dissertation/ThesisDoctoral Dissertation Computer Engineering 201

Passive thermal management using phase change materials

The trend of enhanced functionality and reducing thickness of mobile devices has led to a rapid increase in power density and a potential thermal bottleneck since thermal limits of components remain unchanged. Active cooling mechanisms are not feasible due to size, weight and cost constraints. This work explores the feasibility of a passive cooling system based on Phase Change Materials (PCMs) for thermal management of mobile devices. PCMs stabilize temperatures due to the latent heat of phase change thus increasing the operating time of the device before threshold temperatures are exceeded. The primary contribution of this work is the identification of key parameters which influence the design of a PCM based thermal management system from both the experiments and the numerical models. This work first identifies strategies for integrating PCMs in an electronic device. A detailed review of past research, including experimental techniques and computational models, yields key material properties and metrics to evaluate the performance of PCMs. Subsequently, a miniaturized version of a conventional thermal conductivity measurement technique is developed to characterize thermal resistance of PCMs. Further, latent heat and transition temperatures are also characterized for a wide range of PCMs. In-situ measurements with PCMs placed on the processor indicate that some PCMs can extend the operating time of the device by as much as a factor of 2.48 relative to baseline tests (with no PCMs). This increase in operating time is investigated by computational thermal models that explore various integration locations, both at the package and device level

POWER AND PERFORMANCE STUDIES OF THE EXPLICIT MULTI-THREADING (XMT) ARCHITECTURE

Power and thermal constraints gained critical importance in the design of microprocessors over the past decade. Chipmakers failed to keep power at bay while sustaining the performance growth of serial computers at the rate expected by consumers. As an alternative, they turned to fitting an increasing number of simpler cores on a single die. While this is a step forward for relaxing the constraints, the issue of power is far from resolved and it is joined by new challenges which we explain next. As we move into the era of many-cores, processors consisting of 100s, even 1000s of cores, single-task parallelism is the natural path for building faster general-purpose computers. Alas, the introduction of parallelism to the mainstream general-purpose domain brings another long elusive problem to focus: ease of parallel programming. The result is the dual challenge where power efficiency and ease-of-programming are vital for the prevalence of up and coming many-core architectures. The observations above led to the lead goal of this dissertation: a first order validation of the claim that even under power/thermal constraints, ease-of-programming and competitive performance need not be conflicting objectives for a massively-parallel general-purpose processor. As our platform, we choose the eXplicit Multi-Threading (XMT) many-core architecture for fine grained parallel programs developed at the University of Maryland. We hope that our findings will be a trailblazer for future commercial products. XMT scales up to thousand or more lightweight cores and aims at improving single task execution time while making the task for the programmer as easy as possible. Performance advantages and ease-of-programming of XMT have been shown in a number of publications, including a study that we present in this dissertation. Feasibility of the hardware concept has been exhibited via FPGA and ASIC (per our partial involvement) prototypes. Our contributions target the study of power and thermal envelopes of an envisioned 1024-core XMT chip (XMT1024) under programs that exist in popular parallel benchmark suites. First, we compare XMT against an area and power equivalent commercial high-end many-core GPU. We demonstrate that XMT can provide an average speedup of 8.8x in irregular parallel programs that are common and important in general purpose computing. Even under the worst-case power estimation assumptions for XMT, average speedup is only reduced by half. We further this study by experimentally evaluating the performance advantages of Dynamic Thermal Management (DTM), when applied to XMT1024. DTM techniques are frequently used in current single and multi-core processors, however until now their effects on single-tasked many-cores have not been examined in detail. It is our purpose to explore how existing techniques can be tailored for XMT to improve performance. Performance improvements up to 46% over a generic global management technique has been demonstrated. The insights we provide can guide designers of other similar many-core architectures. A significant infrastructure contribution of this dissertation is a highly configurable cycle-accurate simulator, XMTSim. To our knowledge, XMTSim is currently the only publicly-available shared-memory many-core simulator with extensive capabilities for estimating power and temperature, as well as evaluating dynamic power and thermal management algorithms. As a major component of the XMT programming toolchain, it is not only used as the infrastructure in this work but also contributed to other publications and dissertations