1. Lenovo - Eficiencia Energética en Sistemas de Supercomputación Miguel Terol Palencia Arquitecto HPC LENOVO

2. Enterprise is Key to Lenovo “Triple Plus” Strategy

3. Lenovo and High Performance Computing 77 supercomputers in the Top500 list (www.top500.org) are powered by Lenovo (or IBM/Lenovo) (IBM System x is now part of Lenovo)

4. Lenovo CAE solutions

6. Energy efficiency of HPC systems is one of the main goals of the HPC community. The world's most powerful HPC systems have been outperforming Moore's law for years. The power consumption of the leading edge supercomputers has reached a level of more than 10 MegaWatts (MW), yet it continues to grow. Due to rising energy prices, climate protection policies and technical challenges and limitations it is commonly accepted that Power consumption of sustainable many-Peta to Exascale computing needs to stay in a 1 to 20 MW range.

7. Lenovo focus on 3 main areas Efficient hardware design – More efficient power supply – More efficient fans Power and cooling – Reduce PUE – Reduce chillers – Reduce power consumption Energy Aware Scheduling – Monitor Power – Control Power and Energy

8. Efficient Hardware Design Use latest semiconductor technology Use energy saving processor and memory technologies Consider using special hardware or accelerators designed for specific scientific problems or numerical algorithms

9. Traditional Air Cooling

10. Rear Door Heat Exchangers

11. Direct Water Cooling

12. Leibniz Supercomputing Centre (LRZ) SuperMUC supercomputer More than 10,000 compute nodes Infiniband FDR10 Interconnect, 10 PB Storage (200 GB/s bandwidth) 5 MW power consumption (Max. 10 MW)

13. Energy Aware Scheduling (EAS): Optimize Power Consumption of Active Nodes Set a default cpu frequency on nodes Ability to set specified frequency on core/node level for a given job/application/queue Ability to use Energy Policies to automatically select optimal cpu frequency based on power and performance predication

14. Energy Aware Scheduling: Predicting Power Consumption at CPU Frequency f n

15. Energy Aware Scheduling: Predicting Runtime at CPU Frequency f n

16. Scheduler EAS Implementation

17. Results Overview (Worstcase Prediction Error) Quantum Espresso Nodes: 16 Parallelization: Hybrid (4 MPI Tasks, 4 OpenMP Threads) WPE Power: 1.4% WPE Runtime: 4.6% Gadget Nodes: 8 Parallelization: Hybrid (4 MPI Tasks, 4 OpenMP Threads) WPE Power: 2.7% WPE Runtime: 0.7% Seissol Nodes: 16 Parallelization: Hybrid (1 MPI Tasks, 16 OpenMP Threads) WPE Power: 2.6% WPE Runtime: 2.6% WaLBerla Nodes: 64 Parallelization: MPI only (1024 MPI Tasks) WPE Power: 2.4% WPE Runtime: 1.8% PMatMul Nodes: 64 Parallelization: MPI only (1024 MPI Tasks) WPE Power: 0.9% WPE Runtime: 6.7% STREAM Nodes: 1 Parallelization: OpenMP only (16 OpenMP Threads) WPE Power: 4.9% WPE Runtime: 6.3%

18. Result: Energy Savings LRZ presented this work at ISC14 and SC14 which shows EAS saved 6% of electricty without performance degradation

19. Thank you!!!