Adaptive runtime techniques for power and resource management on multi-core systems

Energy-related costs are among the major contributors to the total cost of ownership of data centers and high-performance computing (HPC) clusters. As a result, future data centers must be energy-efficient to meet the continuously increasing computational demand. Constraining the power consumption of the servers is a widely used approach for managing energy costs and complying with power delivery limitations. In tandem, virtualization has become a common practice, as virtualization reduces hardware and power requirements by enabling consolidation of multiple applications on to a smaller set of physical resources. However, administration and management of data center resources have become more complex due to the growing number of virtualized servers installed in data centers. Therefore, designing autonomous and adaptive energy efficiency approaches is crucial to achieve sustainable and cost-efficient operation in data centers. Many modern data centers running enterprise workloads successfully implement energy efficiency approaches today. However, the nature of multi-threaded applications, which are becoming more common in all computing domains, brings additional design and management challenges. Tackling these challenges requires a deeper understanding of the interactions between the applications and the underlying hardware nodes. Although cluster-level management techniques bring significant benefits, node-level techniques provide more visibility into application characteristics, which can then be used to further improve the overall energy efficiency of the data centers. This thesis proposes adaptive runtime power and resource management techniques on multi-core systems. It demonstrates that taking the multi-threaded workload characteristics into account during management significantly improves the energy efficiency of the server nodes, which are the basic building blocks of data centers. The key distinguishing features of this work are as follows: We implement the proposed runtime techniques on state-of-the-art commodity multi-core servers and show that their energy efficiency can be significantly improved by (1) taking multi-threaded application specific characteristics into account while making resource allocation decisions, (2) accurately tracking dynamically changing power constraints by using low-overhead application-aware runtime techniques, and (3) coordinating dynamic adaptive decisions at various layers of the computing stack, specifically at system and application levels. Our results show that efficient resource distribution under power constraints yields energy savings of up to 24% compared to existing approaches, along with the ability to meet power constraints 98% of the time for a diverse set of multi-threaded applications

Techniques to Improve Energy Efficiency on Heterogeneous Multiprocessors under Timing and Quality Constraints

Traditionally, applications are executed without the notion of a computational deadline and often use all available system resources, which leads to higher\ua0energy consumption. User specification of Quality of Service (QoS) constraints,\ua0in terms of completion time and solution quality, opens up for allocation of\ua0just enough resources to an application to finish just in time and thereby save\ua0energy. Modern heterogeneous multiprocessor (HMP) platforms provide a\ua0set of configurable resources, including a frequency range of dynamic voltage\ua0frequency scaling (DVFS), one among a set processor types, and one or a\ua0plurality of processors of each type. They can be configured at run-time to\ua0open up new opportunities for resource management.This thesis presents techniques to reduce energy consumption under QoS\ua0constraints by allocating resources at run-time on heterogeneous multiprocessor platforms targeting sequential and parallel iterative and task-parallel\ua0applications. The proposed techniques rely on a progress-tracking framework\ua0that monitors and predicts how much time is left until the application finishes.\ua0Furthermore, the proposed framework enables the prediction of computation\ua0demand and performance requirements for future iterations or tasks.\ua0The first contribution of this thesis is a resource management technique,\ua0called SLOOP, targeting single-threaded applications. SLOOP allocates resources, i.e., processor type and DVFS, for each iteration to meet deadlines\ua0while using the prediction of computational demand and execution time.The second contribution of this thesis is a resource-management scheme, called SaC, for multi-threaded applications executing on HMPs, where resources\ua0also include the number of processors besides DVFS and processor type. SaC\ua0first chooses the most energy-efficient configuration that meets the deadline.\ua0The proposed technique collects execution-time slack over subsequent iterations\ua0to select a configuration that can save energy.The third contribution of this thesis is a resource manager, called Task-RM, for task-parallel applications executing on HMPs under QoS constraints. Task-RM exploits the variance in task execution times and imbalance between\ua0sibling tasks to allocate just enough resources in terms of DVFS and processor type. It uses an innovative off-line analysis to avoid redoing scheduling analysis\ua0at run-time.Finally, the fourth contribution is a scheme, called Approx-RM, that can exploit accuracy-energy trade-offs in approximate iterative applications. Approx-RM allocates an appropriate amount of resources while guaranteeing timing\ua0and solution quality specifications. Approx-RM first predicts the iteration count required to meet the quality target and then allocates enough resources\ua0on an HMP in terms of DVFS, processor type, and processor count to save\ua0energy while meeting a performance target

Learning-based run-time power and energy management of multi/many-core systems: current and future trends

Multi/Many-core systems are prevalent in several application domains targeting different scales of computing such as embedded and cloud computing. These systems are able to fulfil the everincreasing performance requirements by exploiting their parallel processing capabilities. However, effective power/energy management is required during system operations due to several reasons such as to increase the operational time of battery operated systems, reduce the energy cost of datacenters, and improve thermal efficiency and reliability. This article provides an extensive survey of learning-based run-time power/energy management approaches. The survey includes a taxonomy of the learning-based approaches. These approaches perform design-time and/or run-time power/energy management by employing some learning principles such as reinforcement learning. The survey also highlights the trends followed by the learning-based run-time power management approaches, their upcoming trends and open research challenges

Utilizing Criticality Stacks for Dynamic Voltage and Frequency Scaling

Thread imbalance is inevitable for multithreaded applications due to the necessity of synchronization primitives to coordinate access to memory and system resources. This imbalance leads to a bounding of application performance, but, more importantly for mobile devices, this imbalance also leads to energy inefficiencies. Recent works have begun to quantify this imbalance and look to leverage it not only for performance improvements, but for energy savings as well. All these works, though, test the theory through the use of simulators and power estimation tools. These results may show that the theory is sound, but the complexities of how a real machine handles synchronization may lead to diminished results by either having too large of a performance impact, or too little energy savings. In this work, we implement one such algorithm, PCSLB, and improve upon it in order to see if the results shown for this technique are feasible for use in real machines. With the improved algorithm, PCSLB-Max, and the CritScale Linux kernel module, we show that, in fact, there are energy saving available to us while mitigating the performance

RPPM : Rapid Performance Prediction of Multithreaded workloads on multicore processors

Analytical performance modeling is a useful complement to detailed cycle-level simulation to quickly explore the design space in an early design stage. Mechanistic analytical modeling is particularly interesting as it provides deep insight and does not require expensive offline profiling as empirical modeling. Previous work in mechanistic analytical modeling, unfortunately, is limited to single-threaded applications running on single-core processors. This work proposes RPPM, a mechanistic analytical performance model for multi-threaded applications on multicore hardware. RPPM collects microarchitecture-independent characteristics of a multi-threaded workload to predict performance on a previously unseen multicore architecture. The profile needs to be collected only once to predict a range of processor architectures. We evaluate RPPM's accuracy against simulation and report a performance prediction error of 11.2% on average (23% max). We demonstrate RPPM's usefulness for conducting design space exploration experiments as well as for analyzing parallel application performance

Pac-Sim: Simulation of Multi-threaded Workloads using Intelligent, Live Sampling

High-performance, multi-core processors are the key to accelerating workloads in several application domains. To continue to scale performance at the limit of Moore's Law and Dennard scaling, software and hardware designers have turned to dynamic solutions that adapt to the needs of applications in a transparent, automatic way. For example, modern hardware improves its performance and power efficiency by changing the hardware configuration, like the frequency and voltage of cores, according to a number of parameters such as the technology used, the workload running, etc. With this level of dynamism, it is essential to simulate next-generation multi-core processors in a way that can both respond to system changes and accurately determine system performance metrics. Currently, no sampled simulation platform can achieve these goals of dynamic, fast, and accurate simulation of multi-threaded workloads. In this work, we propose a solution that allows for fast, accurate simulation in the presence of both hardware and software dynamism. To accomplish this goal, we present Pac-Sim, a novel sampled simulation methodology for fast, accurate sampled simulation that requires no upfront analysis of the workload. With our proposed methodology, it is now possible to simulate long-running dynamically scheduled multi-threaded programs with significant simulation speedups even in the presence of dynamic hardware events. We evaluate Pac-Sim using the multi-threaded SPEC CPU2017, NPB, and PARSEC benchmarks with both static and dynamic thread scheduling. The experimental results show that Pac-Sim achieves a very low sampling error of 1.63% and 3.81% on average for statically and dynamically scheduled benchmarks, respectively. Pac-Sim also demonstrates significant simulation speedups as high as 523.5$\times$ (210.3$\times$ on average) for the train input set of SPEC CPU2017.Comment: 14 pages, 14 figure

Machine Learning for Resource-Constrained Computing Systems

Die verfügbaren Ressourcen in Informationsverarbeitungssystemen wie Prozessoren sind in der Regel eingeschränkt. Das umfasst z. B. die elektrische Leistungsaufnahme, den Energieverbrauch, die Wärmeabgabe oder die Chipfläche. Daher ist die Optimierung der Verwaltung der verfügbaren Ressourcen von größter Bedeutung, um Ziele wie maximale Performanz zu erreichen. Insbesondere die Ressourcenverwaltung auf der Systemebene hat über die (dynamische) Zuweisung von Anwendungen zu Prozessorkernen und über die Skalierung der Spannung und Frequenz (dynamic voltage and frequency scaling, DVFS) einen großen Einfluss auf die Performanz, die elektrische Leistung und die Temperatur während der Ausführung von Anwendungen. Die wichtigsten Herausforderungen bei der Ressourcenverwaltung sind die hohe Komplexität von Anwendungen und Plattformen, unvorhergesehene (zur Entwurfszeit nicht bekannte) Anwendungen oder Plattformkonfigurationen, proaktive Optimierung und die Minimierung des Laufzeit-Overheads. Bestehende Techniken, die auf einfachen Heuristiken oder analytischen Modellen basieren, gehen diese Herausforderungen nur unzureichend an. Aus diesem Grund ist der Hauptbeitrag dieser Dissertation der Einsatz maschinellen Lernens (ML) für Ressourcenverwaltung. ML-basierte Lösungen ermöglichen die Bewältigung dieser Herausforderungen durch die Vorhersage der Auswirkungen potenzieller Entscheidungen in der Ressourcenverwaltung, durch Schätzung verborgener (unbeobachtbarer) Eigenschaften von Anwendungen oder durch direktes Lernen einer Ressourcenverwaltungs-Strategie. Diese Dissertation entwickelt mehrere neuartige ML-basierte Ressourcenverwaltung-Techniken für verschiedene Plattformen, Ziele und Randbedingungen. Zunächst wird eine auf Vorhersagen basierende Technik zur Maximierung der Performanz von Mehrkernprozessoren mit verteiltem Last-Level Cache und limitierter Maximaltemperatur vorgestellt. Diese verwendet ein neuronales Netzwerk (NN) zur Vorhersage der Auswirkungen potenzieller Migrationen von Anwendungen zwischen Prozessorkernen auf die Performanz. Diese Vorhersagen erlauben die Bestimmung der bestmöglichen Migration und ermöglichen eine proaktive Verwaltung. Das NN ist so trainiert, dass es mit unbekannten Anwendungen und verschiedenen Temperaturlimits zurechtkommt. Zweitens wird ein Boosting-Verfahren zur Maximierung der Performanz homogener Mehrkernprozessoren mit limitierter Maximaltemperatur mithilfe von DVFS vorgestellt. Dieses basiert auf einer neuartigen {Boostability}-Metrik, die die Abhängigkeiten von Performanz, elektrischer Leistung und Temperatur auf Spannungs/Frequenz-Änderungen in einer Metrik vereint. % ignorerepeated Die Abhängigkeiten von Performanz und elektrischer Leistung hängen von der Anwendung ab und können zur Laufzeit nicht direkt beobachtet (gemessen) werden. Daher wird ein NN verwendet, um diese Werte für unbekannte Anwendungen zu schätzen und so die Komplexität der Boosting-Optimierung zu bewältigen. Drittens wird eine Technik zur Temperaturminimierung von heterogenen Mehrkernprozessoren mit Quality of Service-Zielen vorgestellt. Diese verwendet Imitationslernen, um eine Migrationsstrategie von Anwendungen aus optimalen Orakel-Demonstrationen zu lernen. Dafür wird ein NN eingesetzt, um die Komplexität der Plattform und des Anwendungsverhaltens zu bewältigen. Die Inferenz des NNs wird mit Hilfe eines vorhandenen generischen Beschleunigers, einer Neural Processing Unit (NPU), beschleunigt. Auch die ML Algorithmen selbst müssen auch mit begrenzten Ressourcen ausgeführt werden. Zuletzt wird eine Technik für ressourcenorientiertes Training auf verteilten Geräten vorgestellt, um einen konstanten Trainingsdurchsatz bei sich schnell ändernder Verfügbarkeit von Rechenressourcen aufrechtzuerhalten, wie es z.~B.~aufgrund von Konflikten bei gemeinsam genutzten Ressourcen der Fall ist. Diese Technik verwendet Structured Dropout, welches beim Training zufällige Teile des NNs auslässt. Dadurch können die erforderlichen Ressourcen für das Training dynamisch angepasst werden -- mit vernachlässigbarem Overhead, aber auf Kosten einer langsameren Trainingskonvergenz. Die Pareto-optimalen Dropout-Parameter pro Schicht des NNs werden durch eine Design Space Exploration bestimmt. Evaluierungen dieser Techniken werden sowohl in Simulationen als auch auf realer Hardware durchgeführt und zeigen signifikante Verbesserungen gegenüber dem Stand der Technik, bei vernachlässigbarem Laufzeit-Overhead. Zusammenfassend zeigt diese Dissertation, dass ML eine Schlüsseltechnologie zur Optimierung der Verwaltung der limitierten Ressourcen auf Systemebene ist, indem die damit verbundenen Herausforderungen angegangen werden