update: HPC, Data Center Infrastructure and Machine Learning HPC User Forum, February 28, 2017 Stuttgart

Transcription

1 update: HPC, Data Center Infrastructure and Machine Learning HPC User Forum, February 28, 2017 Stuttgart Arndt Bode Chairman of the Board, Leibniz-Rechenzentrum of the Bavarian Academy of Sciences and Humanities and Technische Universität München

2 Leibniz Supercomputing Centre (LRZ) LRZ IT Equipment Floor Space: m 2 ( ft 2 6 rooms on 3 floors) Infrastructure Floor Space: m 2 ( ft 2 ) Generic IT services to all Munich universities - Internet Access, Munich Scientific Network - IT Service & Support IT services to all Bavarian universities - Software License Management - Backup & Archive Services HPC resources to scientist in Europe - European Tier 0 level of HPC - PRACE Member 3/15/2017 HPC User Forum 2

3 SuperMUC The First High Temperature Direct Liquid Cooled Petascale Supercomputer System Lenovo/IBM NeXtScale nx360m5 Number of Cores: 86,016 Total Number of Nodes: 3072 Cores Per Node: 28 Shared memory per node: 64 Gbyte System IBM/Lenovo idataplex DX360M4 (Thin Nodes) Number of Cores: 147,456 Total Number of Nodes: 9216 Cores Per Node: 16 Shared memory per node: 32 Gbyte Processor Intel Haswell Xeon Processor E v3 PHASE 1 Turbo Frequency: Max Operating Frequency: Thermal Design Power: 3.1 GHz to 3.6 GHz 2.6 GHz 145 W Processor Intel Sandy Bridge-EP Xeon E C Turbo Frequency: 3.1 GHz to 3.5 GHz Max Operating Frequency: 2.7 GHz Thermal Design Power: 130 W Performance Theoretical Peak performance: HPL: Energy Efficiency: Typical Power Consumption: Power HPL: 3.57 PetaFLOPS 2.81 PetaFLOPS 1.9 GLOPS/W 1.1 MW 1.48 MW Performance Theoretical Peak performance: HPL: Energy Efficiency: Typical Power Consumption: Power HPL: 3.18 PetaFLOPS 2.89 PetaFLOPS 0.85 GLOPS/W 2.3 MW 3.4 MW Phase1 Blade Phase2 Blade 3/15/2017 HPC User Forum 3

4 HPC: New Procurements SuperMUC-NG: negotiations started in 2017, contract end 2017 Funding: project SiVeGCS Overview over current systems: 15/03/2017 HPC User Forum 4

5 Overview HPC (continued) 3/15/2017 HPC User Forum 5

6 Schematic overview of LRZ s cooling infrastrucutre 3/15/2017 HPC User Forum 6

7 1kWh (2000) kWh (2016) /15/2017 HPC User Forum 7

8 Energy-Efficiency Challenges 3/15/2017 HPC User Forum 8

9 Data Center Overview of Some EE R&D Pillar I Building Infrastructure Pillar II System Hardware Pillar III System Software Pillar IV Applications Neighboring Buildings Infrastructure Data Analyser (IDA) Global Optimization Framework Utility Providers Environment SIMOPEK Resource Management & Scheduling System Power Variation Aware Configuration Adviser Lightweight Adaptive Consumption Predictor (LACP) Power Data Aggregation Monitor (PowerDAM) GOAL: Improve Key Performance Indicators GOAL: Reduce Hardware Power Consumption GOAL: Optimize Resource Usage, Tune System GOAL: Optimize Application Performance 3/15/2017 HPC User Forum 9

10 Schematic overview of LRZ s chiller-less cooling infrastrucutre 3/15/2017 HPC User Forum 10

11 Schematic overview of the hydraulic connection to the KLT14 cooling tower Sensor measuring the inlet water temperature entering from the distribution bar Sensors measuring the currently generated heat and the water flow rate Sensors measuring the speed and the status of the pump Pumps three-way valve Hydraulic Gate A Heat Exchanger Water mixed with glycol Sensor measuring the return water temperature to the distribution bar subcircuit I subcircuit II 3/15/2017 HPC User Forum 11

12 Schematic overview of the KLT14 cooling tower Sprinkler Pipe Sensor measuring the inlet water temperature to the colling tower Sensor measuring the return water temperature from the cooling tower A Fans Sensors indicating the status and the speed of the fan 3/15/2017 HPC User Forum 12

13 The Efficiency of Cooling Infrastructure COP = Q CoolingCircuits P CoolingCircuits Q CoolingCircuits - aggregated amount of cold generated by the four cooling circuits P CoolingCircuits - aggregated amount of power consumed by the four cooling circuits 3/15/2017 HPC User Forum 13

14 Predicting the Efficiency of Cooling Infrastructure Using Recurrent Neural Networks Network Type: Software Package: LSTM (Long Short-Term Memory) Keras TensorFlow 12 Inputs: aggregated amount of cold generated by each cooling circuit aggregated amount of power consumed by the fans of each cooling circuit number of active cooling towers wet bulb temperature inlet water temperature (to the distribution bar) from each cooling circuit (x 4) return water temperature (to the distribution bar) to each cooling circuit (x 4) Output: COP of warm water cooling infrastructure 3/15/2017 HPC User Forum 14

15 COP Prediction Results (learned 2015 operational data predicted for 2016) Some of the data not available due to maintenances in building automation systems Some of the data not available due to maintenances in building automation systems 3/15/2017 HPC User Forum 15

16 summary New procurement(s) for HPC in the context of GCS General purpose system, embedded into general IT infrastructure visualization big data networking Research and Development in Energy Efficiency 15/03/2017 HPC User Forum 16