Best detection scheme achieves 100% hit detection with <5% false alarms Princeton University Runtime Power Monitoring and Phase Analysis Methods for Power Management Canturk Isci and Margaret Martonosi Motivation and Research Overview  Power is the primary design constraint for current systems  Power density  Cooling / Thermal constraints  Energy  Battery life  Workloads exhibit drastically different behavior both within applications and among different applications (Phases)  These can be exploited by workload directed dynamic management techniques  Dynamically reconfigurable hardware  Power balancing / Activity migration  Need methods to track application power behavior and identify different (repetitive) regions of operation  Live, real-system experiments:  Reflect behavior of real, modern processors  Observe long time periods  Guide on-the-fly adaptations Live, Runtime Power Monitoring and Estimation Power Phase Analysis on Real Systems Our Work: Real Measurements Dynamic Management Power Estimation & Phase Analysis Runtime Monitoring Hardware Performance Counters Dynamic Program Flow Application ▪ Monitor application Execution: - Performance behavior via performance monitoring counters (PMCs) - Control flow via dynamic instrumentation ▪ Employ real power measurements to provide feedback to runtime power estimations and to evaluate phase characterizations ▪ Use application phase information to guide dynamic/adaptive power management techniques ▪ Represent application execution as a stream of PMC and control flow samples ▪ Estimate power behavior from PMC information ▪ Apply phase tracking, detection and prediction strategies under real-system effects based on PMC and control flow features Counter Based Power Estimation:  Idealized view: For all components on a chip…. MaxPower[I] * ArchScaling[I] * AccessRate[I] Power of component I = CPU Performance Counters! From Microarch. Properties Die Area + Stressmarks  Realistic view: Handle non-linear scaling… … + NonGatedPower[I] Empirical Multimeter Measurements Gcc GzipVpr Vortex Gap Crafty Total Power Estimates and Measurement Validation: Per-Component Estimates: Ex. Equake  Initialization and computation phases  Initialization with high complex IA32 instructions  FP intensive mesh computation phase + Fast (Real-time) + Offers estimated view of on-chip detail for real systems + Real measurement validation  Phases: Distinct and often-recurring regions of program behavior  Ex: Vortex  Power can also exhibit phase behavior  Phase Tracking: By evaluating the similarity among PMC vectors (PVs):  Similarity Criterion: L1-Distance between PVs  PVs achieve < 5W within phase variations with <10 phases  Real-System Effects on Phases: Metric and time variability  Phase Detection Under Real-System Variability:  Problem Definition: Variability effects on phases  Long-Term Value and Duration Prediction of Memory Bound Phases for DVFS: ABCB Ideal Glitch Gradient Shift Mutation Time Dilation ABCBDB ABCBDEB ABCBDEB ABCBDEF ABCBDEF  Proposed Solution: Transition-guided phase detection framework:  Mutations  Transition based tracking  Glitches and gradients  Glitch/Gradient Filtering  Shifts  ~Binary cross correlations  Time Dilations  Near-neighbor blurring Applications of Power Phase Analysis t ABCB t ABCBDEF … run1 Match! t run t 1 00 Very high detect threshold  P{hit} = 0 P{false alarm} = 0 0 detect threshold  P{hit} = 1 P{false alarm} = 1 Desired operating point  P{hit} ~ 1 P{false alarm} ~ 0  Evaluating Control-Flow-Based and Event-Counter Based Approaches: Control flow (Basic Block Vectors / BBVs): Perfect repeatability Architectural independence Detail at program level Runtime applicability BBV phases ≢ power phases No physical binding to power Event counters (PMCs): Runtime monitoring Strong relation to power Imperfect repeatability Lack of detail Pintool Application Binary Application Performance Counter Hardware Power Meas.via Current Probe OS serial device file Dynamic Instrumentation via Pin OS Hardware  Experimentation:  Evaluation:  Both approaches bring significant insights to application power behavior Error Number of Phases  PMCs achieve (on average 40%) less errors than BBVs in power phase characterization  Can predict >90% of DVFS’able phases, with less than 5% prediction overshoots!  Power Balancing for Multiprocessor Systems / Activity Migration: Power Task1 Task2 Swap hot task Slow down! Speed up! Core/μP 1Core/μP 2 Conclusions  Certain compositions of event counters can provide reasonably accurate runtime estimates for processor power consumption and distribution of power among architectural components  Workloads exhibit phases in their performance as well as power behavior - Performance counter vectors help identify different (recurring) power phases of applications  Real system variability effects impose additional challenges for detecting recurrent phases - Phase transition guided approach, together with supporting methods such as glitch/gradient filtering and near-neighbor blurring enable detection of repetitive power phase behavior  Both control flow and event counter based application features provide insight to application power behavior - PMC based approaches generally provide a better proxy to application power phase behavior, due to their strong physical binding to processor power consumption  These phase oriented methods can be employed to guide range of applications in current and next generation systems