Évaluer et diminuer la consommation d énergie des applications scientifiques : quelles possibilités?

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1 Évaluer et diminuer la consommation d énergie des applications scientifiques : quelles possibilités? Jean-Marc Pierson Equipe SEPIA IRIT Institut de Recherche en Informatique de Toulouse UPS UniversitéToulouse 3 Paul Sabatier Journées SUCCESS - 13 Novembre 2013 (IRIT) Évaluer et diminuer la consommation d énergie des applications scientifiques 1 / : 30que

2 Outline 1 CloudMIP 2 Context 3 Power consumption on CloudMIP 4 Introduction Motivation 5 HPC Applications Characterization 6 HPC Applications Phases Results analysis: processor s only optimization Results analysis: processor, disk and network optimization 7 Energy Consumption Tools State of the art Energy Consumption Library: eclib Data Monitoring Tool: ectop 8 Words to take home (IRIT) Évaluer et diminuer la consommation d énergie des applications scientifiques 2 / : 30que

3 Facts, resources: Funded by France Grilles, installed in Who : Pr Jean-Marc Pierson (manager), Dr François Thiebolt (system), DTSI Network team, Where : hosted at the Paul Sabatier University s Data Center (along with Grid5000-Toulouse and GridMIP platforms), Fluids consumption annual cost (est.) : between 4keuros and 10keuros. Hardware, OS: 2 x Dell M1000e chassis each filled with 16 blades. 256 physical cores, 1TB RAM, 15To Disk System : Scientific Linux 6.4 x86_64, VM provisioning system : OpenNebula 4.2 with KVM hypervisor, Monitoring : Zabbix (http://www.zabbix.com) combines Nagios and Ganglia capabilities. (IRIT) Évaluer et diminuer la consommation d énergie des applications scientifiques 3 / : 30que

4 More details The front-end node : The monitoring system : cloudmip.univ-tlse3.fr/zabbix 32 blades (8 2.4Ghz, 32GB ram, 2 x 146GB SAS 15ktpm RAID0), means up to 256 Amazon EC2 M1 instance (1 physical dedicated CPU and 4GB ram) OpenNebula 4.2 (KVM) with Qcow2 delta images to speedup deployment (a hundred of VMs in just a few seconds) 1s resolution power and thermal monitoring of each node (Zabbix), A 24TB, 700MB/s NFS server shared with Grid5000 and GridMIP, Ways for the users to gain access to their VMs from the Internet : ssh, vpn, spice display forwarding, 1000 ports on the front-end node dedicated to routing, 60 dedicated public IPs with dynamic routing. (IRIT) Évaluer et diminuer la consommation d énergie des applications scientifiques 4 / : 30que

5 Global picture (IRIT) Évaluer et diminuer la consommation d énergie des applications scientifiques 5 / : 30que

6 Green IT ICT consumes a lot : Estimated to 4-5% of global consumption of electricity (930 TWh per year) Annual growth between 5 to 10% Data Centers themselves account for 2% of the global electrical consumption Reminder: 1 nuclear reactor is producing about 900 MWe. (7 TWh per year) (IRIT) Évaluer et diminuer la consommation d énergie des applications scientifiques 6 / : 30que

7 What is it all about? UNIVAC I (UNIVersal Automatic Computer) machine in the 1950 s was consuming 125 kw for 1905 operations per second. Tianhe-2 machine: 33.8 Petaflops at a cost of kw. (1898 Mflops / Watt). The Greenest supercomputer (Green500 list): 3208 Mflops / watt (installed at CINECA, Eurora, funded by PRACE 2) (IRIT) Évaluer et diminuer la consommation d énergie des applications scientifiques 7 / : 30que

8 Power consumption on CloudMIP 248 VM started: > onetemplate instantiate SL64 -m 248 Leads to 8 VMs on each of the nodes (IRIT) Évaluer et diminuer la consommation d énergie des applications scientifiques 8 / : 30que

9 Stress: > pdsh -w wn[1..32] openssl speed -multi 8 Power consumption is 6.6kw (IRIT) Évaluer et diminuer la consommation d énergie des applications scientifiques 9 / : 30que

10 Introduction Motivation Why measuring, monitoring, estimating power consumed by applications? (IRIT) Évaluer et diminuer la consommation d énergie des applications scientifiques 10 / : 30que

11 Introduction Motivation Why measuring, monitoring, estimating power consumed by applications? 1 Make applications users energy-aware CO2 labels. (IRIT) Évaluer et diminuer la consommation d énergie des applications scientifiques 10 / : 30que

12 Introduction Motivation Why measuring, monitoring, estimating power consumed by applications? 1 Make applications users energy-aware CO2 labels. 2 Make applications developers energy aware applications become energy-friendly (IRIT) Évaluer et diminuer la consommation d énergie des applications scientifiques 10 / : 30que

13 Introduction Motivation Why measuring, monitoring, estimating power consumed by applications? 1 Make applications users energy-aware CO2 labels. 2 Make applications developers energy aware applications become energy-friendly 3 Make the operating system energy-friendly: the OS can optimize its behavior according to the profiles of applications (IRIT) Évaluer et diminuer la consommation d énergie des applications scientifiques 10 / : 30que

14 Introduction Motivation Why measuring, monitoring, estimating power consumed by applications? 1 Make applications users energy-aware CO2 labels. 2 Make applications developers energy aware applications become energy-friendly 3 Make the operating system energy-friendly: the OS can optimize its behavior according to the profiles of applications 4 Make the middleware energy-aware and energy-friendly: energy-aware: it can redistribute costs to individual users (e.g. in commercial Clouds) energy-friendly: it can optimize dynamically the placement and scheduling of applications on machines (IRIT) Évaluer et diminuer la consommation d énergie des applications scientifiques 10 / : 30que

15 Introduction Motivation Power consumption on a node is application dependent. And applications are time dependent. (IRIT) Évaluer et diminuer la consommation d énergie des applications scientifiques 11 / : 30que

16 Introduction Motivation Power consumption on a node is application dependent. And applications are time dependent. 1 Resource Management of servers Workload classes highly differ (services, Clouds, HPC,...) Workload differ over time-periods (burst, night-and-day, batch,...) App Monitor App Profiler Near-optimal Resource Manager Energy Efficiency (IRIT) Évaluer et diminuer la consommation d énergie des applications scientifiques 11 / : 30que

17 Introduction Motivation Power consumption on a node is application dependent. And applications are time dependent. 1 Resource Management of servers Workload classes highly differ (services, Clouds, HPC,...) Workload differ over time-periods (burst, night-and-day, batch,...) App Monitor App Profiler Near-optimal Resource Manager Energy Efficiency 2 Software development Power consumption of a same functionality varies according to Libraries Coding patterns Compilers Computer Architecture (IRIT) Évaluer et diminuer la consommation d énergie des applications scientifiques 11 / : 30que

18 Existing Approaches to Energy Consumption Estimation Three possibilities for evaluating energy of an application Monitor the power with (internal or external) power meters, then distribute shares to each process / application based on their resources consumption (CPU, memory, disk, network,...) ; Monitor the usage of resources at the hardware and operating system level (e.g. hardware performance counters, CPU load,...), then use mathematical models to estimate power consumption share for each process / application ; Instrument the code of the application, then relate it to actual measures or modeled estimates. (IRIT) Évaluer et diminuer la consommation d énergie des applications scientifiques 12 / : 30que

19 The Energy Consumption Tools Pack 3 Energy consumption library (libec) Power sensors and estimators Core library used in ectools Data Acquisition tool (ecdaq) Collect data during benchmark execution Create new power estimators Data Monitoring tool (ectop/ganglia plugin) 1 Lightweight monitor of power estimators and sensors Monitor power consumption of application Compare power estimators in any environment Energy profiler (valgreen) 2 Software development 1 Cupertino, L. F., Da Costa, G., Sayah, A., and Pierson, J.-M. Energy Consumption Library. In Energy Efficiency in Large Scale Distributed Systems (EE-LSDS 2013), April 2013, Springer LNCS 2 Cupertino, L. F., DaCosta, G., Sayah, A., and Pierson, J.-M. Valgreen: an Application Energy Profiler. SCSE March Available under GPL3 licence. (IRIT) Évaluer et diminuer la consommation d énergie des applications scientifiques 13 / : 30que

20 Energy Consumption Library Application Power Estimators MinMax CPU Inverse CPU P pid = (P max P min ) P pid = P PM tpid cpu t cpu t pid cpu t cpu + t idl + P min PR t P pid = any formula combining software and hardware sensors library is easily extensible BUT in general, Power model should be auto-adaptive (parameterized statically, using heuristics or approached with machine learning), workload dependent, Power model: linear regression, neural networks, genetic programming (IRIT) Évaluer et diminuer la consommation d énergie des applications scientifiques 14 / : 30que

21 Data Monitoring Tool: ectop ectop command line tool (a-like top, and extensible), based on libec. It exists also in HTML format, with pan view, accumulated values,... Lightweight tool: 3 Kb of memory, 0.3% (unsorted columns) to 2% of CPU (when sorted/sum bar/html) Open source software. Deliverable 5.2 of the CoolEmAll project - October Leandro Fontoura-Cupertino Tested on RECS platform, Grid5000 and CloudMIP (IRIT) Évaluer et diminuer la consommation d énergie des applications scientifiques 15 / : 30que

22 Neural networks results High accuracy of the power estimation, compared to actual measurements with precise and reactive power meters. Power Model: Neural Network 115 Target Output Power (W) x 10 2 R = MAPE=0.49% Error (W) (IRIT) 115 Output ~= 0.98*Target Target Évaluer et diminuer la consommation d énergie des applications scientifiques 16 / :30que

23 Power Sampling Frequency Experiment Power (W) Model ACPI Time (s) Figure: Comparison between ACPI power meter and the linear model The power model is reacting must faster than the ACPI power meter. (IRIT) Évaluer et diminuer la consommation d énergie des applications scientifiques 17 / : 30que

24 Following the consumption of VMs VM are processes, hence we can follow their consumption. Example on CloudMIP 9 Power Monitoring of VMs running on the same PM Power (W) VM1 VM Time (s) (IRIT) Évaluer et diminuer la consommation d énergie des applications scientifiques 18 / : 30que

25 Compiler Optimization Experiment O0 O1 O2 O Energy (J) Squared matrix A size Figure: Energy dissipated to solve a linear system of equations using lapack (IRIT) Évaluer et diminuer la consommation d énergie des applications scientifiques 19 / : 30que

26 HPC Application characterization Knowing how much an application consumes is useful, knowing which application is better! and even better: application is not important, its behavior is! Two different apps can have the same energy profile System changes can have the same impact on them Run-time detection Behavioral pattern Extract information Fine grained : performance counters, system values Coarse grained : network, disk, power consumption Georges Da Costa, Jean-Marc Pierson. Characterizing applications from power consumption: A case study for HPC benchmarks. In: Int Conference on ICT as Key Technology for the Fight against Global Warming (ICT-GLOW 2011), Toulouse, Springer. (IRIT) Évaluer et diminuer la consommation d énergie des applications scientifiques 20 / : 30que

27 Finding patterns, NPB example, CG 1.2e+08 1e+08 Number of bytes sent per second 8e+07 6e+07 4e+07 2e time (s) (IRIT) Évaluer et diminuer la consommation d énergie des applications scientifiques 21 / : 30que

28 Finding patterns, NPB example, FT 1.2e+08 1e+08 Number of bytes sent per second 8e+07 6e+07 4e+07 2e time (s) (IRIT) Évaluer et diminuer la consommation d énergie des applications scientifiques 22 / : 30que

29 Finding patterns, NPB example, SP 1.2e+08 1e+08 Number of bytes sent per second 8e+07 6e+07 4e+07 2e time (s) (IRIT) Évaluer et diminuer la consommation d énergie des applications scientifiques 23 / : 30que

30 NPB example of HPC Application characterization (IRIT) Évaluer et diminuer la consommation d énergie des applications scientifiques 24 / : 30que

31 Detecting phases in applications Define a DNA-like structure for an application (or the system) based on combination of letters describing the application runtime Each letter represents monitored values over a time interval : represented by an integer along with its duration. Example: Idle(17692,21)(12988,41)Idle E (Joules) ( ) or IS ( ) or EP Time (seconds) Figure: Dividing NAS benchmarks IS and EP ran successively into two regions using their power usage, performance counters and sensors Ghislain Landry Tsafack, Laurent Lefèvre, Jean-Marc Pierson, Patricia Stolf, Georges Da Costa. DNA-inspired Scheme for Building the Energy Profile of HPC Systems. In: E2DC Workshop, Energy 2012 Conference, Madrid, May 2012 (IRIT) Évaluer et diminuer la consommation d énergie des applications scientifiques 25 / : 30que

32 Deciding: Acting according to the phases detected With each phase we attribute a system state minimizing energy while keeping performances When an application run, we predict the phase and trigger the related leverages to reduce energy Experiments: 25 nodes cluster of Intel Xeon X3440 set up on Grid5000 Linux kernel runs on each node, where sensors are collected each second high computation level corresponds to 2.53Ghz in CPU frequency, medium and low to 2 GHz and 1.2GHz respectively network interconnect speed scaled between 1GB and 10MB active and sleep states for the disk Consider two real-life applications (100 processes) Advance Research Weather Research Forecasting (WRF-AWR) Molecular Dynamics Simulation (MDS) Ghislain Landry Tsafack, Laurent Lefèvre, Jean-Marc Pierson, Patricia Stolf, Georges Da Costa. Beyond CPU Frequency Scaling for a Fine-grained Energy Control of HPC Systems. In: SBAC-PAD Conference, New-York, October 2012 (IRIT) Évaluer et diminuer la consommation d énergie des applications scientifiques 26 / : 30que

33 Results analysis: performance (energy and execution time) Comparison to Linux on-demand and performance governors In this experiment, only the CPU frequency is changed as a function of the phase. 100 % performance managed on-demand Normalized energy consumption 80 % 60 % 40 % 20 % Normalized execution time 100 % 80 % 60 % 40 % 20 % 0 % MDS WRF-ARW (a) Energy performance (b) Performance (execution time) Figure: Phase tracking and partial recognition guided CPU optimization results. 0 % MDS WRF-ARW (IRIT) Évaluer et diminuer la consommation d énergie des applications scientifiques 27 / : 30que

34 Energy performance: processor, disk and network In this experiment, CPU frequency is changed, network speed is adjusted, and disk is turn on/off as a function of the phase. 100 % performance managed on-demand 100 % Normalized energy consumption 80 % 60 % 40 % 20 % Normalized execution time 80 % 60 % 40 % 20 % 0 % MDS WRF-ARW (a) Energy performance 0 % MDS WRF-ARW (b) Performance (execution time) Figure: Phase tracking and partial recognition guided processor, disk and network interconnect optimization results: the chart shows average energy consumed by each application under different configurations. (IRIT) Évaluer et diminuer la consommation d énergie des applications scientifiques 28 / : 30que

35 Words to take home Tools exist to provide feedback on power consumption of applications, with a good accuracy (< 5% error) These tools need to be accepted by developers for optimizing codes, choosing libraries,... They could be used by final users for choosing software suites (ecolabel). They can be used for autonomic actions on the system without users noticing it, also in the HPC world. (IRIT) Évaluer et diminuer la consommation d énergie des applications scientifiques 29 / : 30que

36 Credits: Application Energy Profiling were done with Landry Tsafack, Georges Da Costa, Laurent Lefevre, Patricia Stolf Energy Consumption Tools Pack works were done with Leandro Fontoura Cupertino, Georges Da Costa, and Amal Sayah Questions? Jean-Marc Pierson (IRIT) Évaluer et diminuer la consommation d énergie des applications scientifiques 30 / :30que

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