Energy Efficiency Tuning: READEX. Madhura Kumaraswamy Technische Universität München

Transcription

1 Energy Efficiency Tuning: READEX Madhura Kumaraswamy Technische Universität München

2 Project Overview READEX Starting date: 1. September 2015 Duration: 3 years Runtime Exploitation of Application Dynamism for Energy-efficient exascale computing Funding: European Commission Horizon 2020 grant agreement

3 Project Partners Technische Universität Dresden, Germany Norwegian University of Science and Technology, Norway Technische Universität München, Germany IT4 Innovations National Supercomputing Center, Czech Republic National University of Ireland, Ireland Intel Corporation, France GNS, Germany 3

4 Motivation Challenges Energy consumption Extreme scale Dynamism Awareness Ability Effort Problems Solution Automatic tuning Dynamic Design-time/Runtime 4

5 Systems Scenario Methodology Formalism for dynamic auto-tuning in embedded systems Detect and analyze dynamism at design-time, switch configurations at runtime Design-time Runtime 5

6 Periscope Tuning Framework (PTF) Automatic application analysis & tuning Tune performance and energy (statically) Plug-in-based architecture Evaluate alternatives online Scalable and distributed framework Support variety of parallel paradigms MPI, OpenMP, OpenCL, Parallel pattern Developed in the Autotune EU-FP7 project Plugins explore tuning space and run performance analysis strategies during a phase 6

7 Score-P Scalable Performance Measurement Infrastructure for Parallel Codes Common instrumentation and measurement infrastructure Periscope accesses Score-P infrastructure via online access interface 7

8 ENOPT Library Support for OpenMP, MPI, hybrid and sequential code Provides socket and node level counter measurements

9 Tuning Plugins DVFS Frequency tuning for energy delay product Region level tuning Uses governors and daemons to set static or dynamic power setting Parallelism capping Thread number tuning for energy delay product Exhaustive and curve fitting based prediction For OpenMP applications

10 Variation of Measurements Energy consumption of the SeisSol application at different compute nodes Normalized energy consumption of the SeisSol application at different compute nodes 10

11 Tuning with Periscope Tuning Framework 11

12 Dynamism Intra-phase Inter-phase 12

13 PEPC Benchmark of the DEISA Benchmark Suite MPI_Alltoall performance over 2048 phases 13

14 Scenario-Based Tuning Design Time Analysis Periscope Tuning Framework (PTF) Runtime Scenarios with Tuning Model Runtime Tuning READEX Runtime Library (RRL) 14

15 Terminology Significant Regions: Coarse-granular code regions Runtime Situations: Instances of significant regions Identifiers: Distinguish rts's with different characteristics Region identifiers, phase identifiers, input identifiers Scenarios: rts's with same characteristics Classifier: Groups rts s into scenarios Selector: Returns a best configuration for a scenario Tuning Model: Set of scenarios Classifier based on the identifiers Selector for each scenario Yury Oleynik oleynik@in.tum.de 15

16 Design Time Analysis with PTF Preparation steps Analysis Tuning Model 16

17 Runtime Tuning with the READEX Runtime Library Detect phase identifiers Calibration if needed? Post process region measurements Store objective measurements Detect scenarios based on region identifiers Detect configurations Switching decision making Switch configuration Initiate objective measurements

18 Validation and Project Goals Energy measurements using HDEEM infrastructure 18

19 Conclusion Energy-efficiency at exascale Application developers and users will have to care Lack of capabilities Awareness Expertise Resources Proposed solution READEX: Exploit dynamism Detect at design-, exploit at runtime Tools-aided auto-tuning methodology 19

20 Thank you! Questions? 20

21 Additional Slides 21

22 Predicted vs Measured Time for Seissol 22

23 Plugin Evaluation Status 23

24 Inter-phase Dynamism Indeed application of GNS Identifiers for adaptation strategy Valleys vs hills 24