ARM Vision for Thermal Management and Energy Aware Scheduling on Linux

Transcription

1 ARM Vision for Management and Energy Aware Scheduling on Linux Charles Garcia-Tobin, Software Power Architect, ARM Thomas Molgaard, Director of Product Management, ARM ARM Tech Symposia China 2015 November 2015

2 Agenda Intelligent Power Allocation (IPA) - update Energy Aware Scheduling (EAS) Future vision of IPA and EAS 2

3 Power Power Power Mobile Application Workloads Web browsing Bursty workload Mobile users spend time on a range of mobile applications*: 38% on web browsing and Facebook 32% on gaming 16% on audio, video and utility 3 main workload categories: Short bursts of high intensity Long periods of sustained high intensity Low intensity Goal: optimal performance and thermal control for each category Time Time Gaming Sustained workload Audio Playback * Source: Flurry Analytics Time 3 Measured on a Quad Cortex-A7 Symmetric Multiprocessing platform

4 ARM Intelligent Power Allocation (IPA) Power to Heat Tdie Tskin Performance Requests big LITTLE Real-time CPU & Performance requests SoC SoC IPA (PID control algorithm) big LITTLE Allocated Performance Dynamic Allocation by: Performance required headroom 4

5 ARM IPA Advancements Proactive vs. Reactive Management Continuously adapting response based on power consumption and thermal headroom Closed-loop control uses PID algorithm for accurate temperature control Dynamic Partitioning vs. Fixed Optimally allocates power to CPU or depending on current workload Merged in Linux-4.2 Will benefit all operating systems based on Linux, no patches required Temp IPA Time 5

6 Agenda Intelligent Power Allocation (IPA) - update Energy Aware Scheduling (EAS) Future vision of IPA and EAS 6

7 Challenges in Scheduling, Performance and Power SoC CPU topologies are becoming more varied, accommodating different power/performance budgets: SMP, multi-cluster SMP, ARM big.little technology. Per core/per cluster (Dynamic Voltage & Frequency Scaling) Linux power management frameworks have evolved separately so are uncoordinated and hard to tune We are developing a flexible unified upstream solution. _All_ policy, all metrics, all averaging should happen at the scheduler power saving level, in a single place, and then the scheduler should directly drive the new low level idle state driver mechanism. Ingo Molnar (31 Mar 2013) 7

8 How are we Solving This? Introduce generic energy awareness in upstream Linux: 1. Integrate Idle,, scheduler big.little support 2. Clean design rather than short-cuts 3. Based on measureable energy model data rather than magic tunables 4. Support future CPU topologies 5. Maintained in upstream Linux, reduced software maintenance costs. Analysis & Tuning flows Tools Simple tunability Energy model driven scheduling EAS Performance enhancements Scheduler driven Idle CPU improvements Linaro development ARM development 8

9 What is EAS the Energy Model Max capacity big Capacity Waking task? Utilization Power Placing task on cpu3: No P-state changes. CPU 2, 3 Big core Max capacity little Current capacity CPU 0, 1 Little core Placing task on cpu1: P-state change for cpu0 and cpu1. 9 CPU cluster little big Compute capacity (Performance)

10 Scheduler-driven With scheduler task utilization tracking can be notified immediately when CPU utilization changes = improved responsiveness. cpu 1 utilization Capacity Now Next sample Current Utilization Time Max capacity big Max capacity little Current capacity Capacity CPU Waking task? cluster little big Utilization 10

11 SchedTune Current: A set of governor-specific tunables. Goal: cpu utilization Single tunable to bias the energy/performance trade-off. Prototypes: Global boost tunable: /proc/sys/kernel/sched_cfs_boost Task group (cgroup) based tuning: /sys/fs/cgroup/stune/<group>/schedtune.boost Capacity Current Utilization Time Performance margin 11

12 Results ARM TC2 Energy 120 Performance Mainline +capacity awareness 20 0 rt-app 6% rt-app 13% rt-app 19% rt-app 25% sysbench +EAS 0 sysbench Lower is better. Higher is better. 12

13 Agenda Intelligent Power Allocation (IPA) - update Energy Aware Scheduling (EAS) Future vision of IPA and EAS 13

14 Building a Platform for Power and Performance Management Improving Power Efficiency, Performance, and Time to Market big.little LITTLE big CPU 2014: 14

15 Building a Platform for Power and Performance Management Improving Power Efficiency, Performance, and Time to Market big.little LITTLE big.little LITTLE big CPU big IPA Gen 1 System 2014: 2015:, IPA 15

16 Building a Platform for Power and Performance Management Improving Power Efficiency, Performance, and Time to Market big.little LITTLE big.little LITTLE EAS Gen 1 big.little CPU IPA Gen 1 System big CPU big IPA Gen 1 System 2014: 2015:, IPA 2016: EAS Gen 1, IPA 16

17 Building a Platform for Power and Performance Management Improving Power Efficiency, Performance, and Time to Market big.little big LITTLE CPU big.little big LITTLE IPA Gen 1 System EAS Gen 1 big.little CPU IPA Gen 1 System EAS Gen 2 big.little CPU Advanced Performance control IPA Gen 2 System 2014: 2015:, IPA 2016: EAS Gen 1, IPA Future: EAS+IPA Gen 2 17

18 EAS Generation 1, IPA in Development CPU Othe r EAS brings together control and CPU selection + Simpler to tune EAS adds power and performance models to the Scheduler + Supports wide range of topologies + Core and OPP selection based on thread load and models + Minimal power for required throughput - Thread performance requirement is inferred only from its load Perf. Limiter Perf Limiter Perf Limiter Power Allocator IPA Gen1 Power Model Power Model Power Model CPU Selection 18 ARM Intelligent Power Allocation (IPA) + Maximizes performance within thermal envelope + IPA Generation 1 available in Linux 4.2 kernel - Only loosely coupled with EAS Thread load Perf Model Power Model EAS Gen1

19 CPU Othe r EAS + IPA generation 2 - Future Tight integration with thermal management IPA provides scheduler with aggregate CPU power budget Advanced performance control Aims to introduce quality of service per thread controls Interface would be exposed to middleware Guided by application performance hints Perf. Limiter Perf Limiter Perf Limiter Ad Perf Control Thread load EAS + IPA Gen 2 Power Allocator CPU Selection Perf Model Power Model Power Model Power Model 19

20 20 Summary ARM has been driving innovation in Linux Intelligent Power Allocation. Upstreamed in Linux 4.2 (30-Aug-2015) Energy Aware Scheduling for advanced SoC s ARM Intelligent Power Allocation is designed to maximise performance: Proactively adjusts available power budget, based on device temperature Allocates power dynamically between CPU/, based on workload Energy Aware Scheduling Integrates CPU capacity awareness, Energy model, & Idle into mainline Linux scheduler Designed to support a wide range of topologies Prototypes running today Further integration and improvements being investigated

21 Thank You The trademarks featured in this presentation are registered and/or unregistered trademarks of ARM Limited (or its subsidiaries) in the EU and/or elsewhere. All rights reserved. All other marks featured may be trademarks of their respective owners. Copyright 2015 ARM Limited