A generalized software framework for accurate and efficient management of performance goals

A number of techniques have been proposed to provide runtime performance guarantees while minimizing power consumption. One drawback of existing approaches is that they work only on a fixed set of components (or actuators) that must be specified at design time. If new components become available, these management systems must be redesigned and reimplemented. In this paper, we propose PTRADE, a novel performance management framework that is general with respect to the components it manages. PTRADE can be deployed to work on a new system with different components without redesign and reimplementation. PTRADE's generality is demonstrated through the management of performance goals for a variety of benchmarks on two different Linux/x86 systems and a simulated 128-core system, each with different components governing power and performance tradeoffs. Our experimental results show that PTRADE provides generality while meeting performance goals with low error and close to optimal power consumption.United States. Defense Advanced Research Projects Agency. The Ubiquitous High Performance Computing Progra

Maximizing Performance in Power-Constrained Computing Systems

Power constraint has become arguably the biggest obstacle for the performance scaling of computing machines. No matter what scale of computing system is – A mobile phone or supercomputer – they are all power restricted in one way or another to ensure normal operation. While various computing systems may require different power management technique, the goal of such systems is invariant and contains two folds of requirement: (1) guarantee computing system operating under a certain power budget/cap, and (2) make use of the limited power efficiently to deliver high performance. Thus, the challenge can be formalized to a classic constrained optimization problem – Given power consumption constraints, maximize the performance of computing systems. In this dissertation, we focus on solving this problem for server systems from single-node level to large-scale. More specifically, this dissertation contains 3 projects addressing power capping challenge at different spectrum. First, we propose PUPiL, a hardware/software hybrid power control system to address the power challenge at the node level. It makes the key observations of tradeoffs between existing software-based and hardware-based approach:(1) hardware techniques provide significantly faster response time – quickly enforcing power limits and, (2) software provide much greater flexibility – by tailoring resource usage to the current application workload – leading to high performance efficiency. PUPiL combines the best of software and hard- ware approach, achieves significantly higher performance with nearly same response time as hardware approach. Second, we propose PowerShift, a distributed power management system to address the emerging challenge of power capping dependent applications in large-scale system. Pow- erShift, to our knowledge, is the first work to identify the unique challenge of dependent distributed workloads and presents a family of three techniques for this scenario, demonstrating improved performance, reduced energy, and dynamic adjustment to tail behavior and system noise. Last, PoDD, a hierarchical distributed power control system inspired by both PUPiL and PowerShift, is proposed to further overcome major limitations in power capping dependent applications. It incorporates learning/hardware hybrid node-level power capping with system-level power shifting to deliver significantly higher performance than prior works and no longer requires offline application profiles by build power model online, greatly improving practicality and performance efficiency. The 3 power management framework systematically studied the problem of maximizing performance in power constrained systems. The key ideas and insights are highly general to guide design of real world power control system for wide range of workloads and platform. All implemented systems are open-sourced and evaluated to be practical, scalable, reliable and also not limited to particular applications and systems, which hopefully will serve as a base model/system to future research on power capping

A data-driven study of operating system energy-performance trade-offs towards system self optimization

This dissertation is motivated by an intersection of changes occurring in modern software and hardware; driven by increasing application performance and energy requirements while Moore's Law and Dennard Scaling are facing challenges of diminishing returns. To address these challenging requirements, new features are increasingly being packed into hardware to support new offloading capabilities, as well as more complex software policies to manage these features. This is leading to an exponential explosion in the number of possible configurations of both software and hardware to meet these requirements. For network-based applications, this thesis demonstrates how these complexities can be tamed by identifying and exploiting the characteristics of the underlying system through a rigorous and novel experimental study. This thesis demonstrates how one can simplify this control strategy problem in practical settings by cutting across the complexity through the use of mechanisms that exploit two fundamental properties of network processing. Using the common request-response network processing model, this thesis finds that controlling 1) the speed of network interrupts and 2) the speed at which the request is then executed, enables the characterization of the software and hardware in a stable and well-structured manner. Specifically, a network device's interrupt delay feature is used to control the rate of incoming and outgoing network requests and a processor's frequency setting was used to control the speed of instruction execution. This experimental study, conducted using 340 unique combinations of the two mechanisms, across 2 OSes and 4 applications, finds that optimizing these settings in an application-specific way can result in characteristic performance improvements over 2X while improving energy efficiency by over 2X