Jezelf Groen Rekenen met Supercomputers

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1 Jezelf Groen Rekenen met Supercomputers Symposium Groene ICT en duurzaamheid: Nieuwe energie in het hoger onderwijs Walter Lioen Groepsleider Supercomputing

2 About SURFsara SURFsara offers an integrated ICT research infrastructure and provides services in the areas of computing, data storage, visualization, networking, cloud and e-science. SARA was founded in 1971 as an Amsterdam computing center by the two Amsterdam universities (UvA and VU) and the current CWI. Independent as of Founded Vancis in 2008 offering ICT services and ICT products to enterprises, universities, and educational and healthcare institutions. As from 1 January 2013, SARA from then on SURFsara forms part of the SURF Foundation. First supercomputer in The Netherlands in 1984 (Control Data Cyber 205). Hosting the national supercomputer(s) ever since. January 30,

3 What is a Supercomputer? A supercomputer is a computer at the frontline of current processing capacity, particularly speed of calculation Consequently, the specification of a supercomputer is constantly changing Rule of thumb: a supercomputer is at least 1,000 10,000 up to 100,000 times faster than an average PC January 30,

4 Why supercomputing? Large scale scientific computing Simulation of processes tot are otherwise - Impossible in practice - Too expensive - Too dangerous - Too extended Examples - Astronomy - How did the universe begin? - How do stars form and evolve? - Weather Prediction, Climatology - Nuclear Physics - Aerodynamics (cars, planes, rockets) - Biology (proteins, DNA, drugs) - Medical sciences (bone formation, blood flow) January 30,

5 Top500: PFlop/s HPL, the High-Performance Linpack benchmark, solves a (random) dense linear system in double precision (64 bits) arithmetic on distributed-memory computers For Tianhe-2, the as of June 2013 nr. 1 (3,120,000 cores, 54.9 PFlop/s, 17.8 MW): - n = 9,960,000 Computational kernel: DGEMM (matrix multiply) Extremely efficient on all processors (in cache) Limiting factors: - Speed of interconnect - Speed to (local accelerator) memory (for e.g. GPGPU) However, far more important: application speed In Amsterdam a Ferrari is useless (speed-wise) January 30,

6 Top500 ipad 2 performance An A5 processor core of an ipad 2 is as fast as a four processor Cray 2 supercomputer (1.951 GFlop/s) In 1985 an eight processor Cray 2 was the fastest supercomputer in the world The ipad 2 would still have been listed in the Top500 of 1994 January 30,

7 Green500: MFlop/s / Watt November 2013 Green500 List observations: Rank 1 10: (Intel Xeon + NVIDIA K20) - commodity processors with GPGPUs (graphics processing units) Rank 1: TSUBAME-KFC (Japan, Ivy Bridge + NVIDIA K20x) - 4, MFlop/s / W (first time > 4 GFlop/s / W) - An exaflop system would require 222 MW (DARPA s target is > 1 EFlop/s using < 20 MW) Rank 4: Piz Daint (Switzerland, Cray XC30, Sandy Bridge + NVIDIA K20x) - 3, MFlop/s / W - the greenest petaflop supercomputer - the current Top500 #6 Rank 12: (USA, Blue Gene/Q) - 2, MFlop/s / W - highest ranked non-heterogeneous (CPU only) system Rank 40: Thianhe-2 (China, Ivy Bridge + Xeon Phi) - 1, MFlop/s / W - the current Top500 #1 January 30,

8 SURFsara National Supercomputing History Year Machine R peak GFlop/s kw GFlop/s / kw 1984 CDC Cyber pipe CDC Cyber pipe Cray Y-MP/ Cray C98/ Cray C916/ SGI Origin , SGI Origin Altix , IBM p575 Power5+ 14, IBM p575 Power6 62, IBM p575 Power6 64, Bull bullx B710 (DLC) + R , Bull bullx B515 (NVIDIA K40) >200,000 <60 > Bull bullx complete system >1,000,000 >520 >1923 January 30,

9 Moore s Law (1965) The number of transistors on an integrated circuit doubles every 2 years Because of faster transistors, the speed doubles every 18 months The clock speed stopped doubling a couple of years ago Nowadays the number of cores doubles Moore noted that if car manufacturers had something like this, cars would get 100,000 miles to the gallon and it would be cheaper to buy a Rolls Royce than park it. (Cars would also be only a half an inch long.) January 30,

10 Cartesius specs Phase 1 (production June 2013, total peak performance 271 TFlop/s) Direct Liquid Cooled thin node islands thin nodes, 2 12-core 2.4 GHz Intel Ivy Bridge CPUs/node, 64 GB/node thin nodes, 2 12-core 2.4 GHz Intel Ivy Bridge CPUs/node, 64 GB/node Fat node island - 32 fat nodes, 4 8-core Intel Sandy Bridge CPUs/node, 256 GB/node Total - 13,968 cores, TB memory, 2.4 PB disk - Interconnect: InfiniBand 56 Gbit/s bandwidth, 3 µs latency - Top 500 November 2013: # 184 Phase 1.5 (scheduled production 2014 Q2, total peak performance ~ 470 TFlop/s) Addition of accelerator island - 66 nodes, 2 Intel Ivy Bridge CPUs/node, 2 NVIDIA Tesla K40 GPGPUs/node Phase 2 (scheduled production 2014 H2, total peak performance > 1 PFlop/s) On-demand addition of thin node islands with latest Intel Haswell CPUs January 30,

11 Cartesius Greenness All thin compute nodes use Direct Liquid Cooling - inlet temperature 30ºC: warm water cooling - free cooling if outdoor temperature < 30ºC in Amsterdam: 99.1% of days - (Cartesius System) Power Usage Effectiveness 1.2 (typical PUE for cold water cooling: 1.4; air cooling: 1.6) System requirements based on detailed usage analysis - which user applications - actual memory usage - I/O profiles Optimized price/performance - TCO: total budget =investment + energy + cooling + housing + ups (storage only) - performance: application throughput using the 7 most relevant applications (# jobs / lifetime) - maximization of application throughput / TCO (optimization of power related costs vs. investment costs) left as an exercise for the vendor during the procurement - result: using slower processors (lower clock frequency) January 30,

12 Cartesius Greenness On demand growth - minimizes idle time - use latest technology maximizes value for money - higher performance - lower energy - (less good for Top500 ranking) On demand growth: accelerator island (NVIDIA K40) - Phase 1 and Phase 2 (both CPU only) are general purpose - accelerators are more special purpose - can deliver more MFlop/s / Watt - efficient use of accelerators requires - suitable applications - investment in programming effort - proven interest of more than 10 research groups January 30,

13 Scalable Hybrid Architecture PRACE-2IP prototype: Bull CSC, Finland EU collaboration: CSC, SURFsara, CSCS 44 nodes with two Intel Xeon Phi 7120X co-processors 37 nodes with two NVIDIA K40 GPGPUs SURFsara research topics: Programming paradigms - Application porting to accelerator + MPI Energy policies - Dynamic Voltage and Frequency Scaling (DVFS) Adjust frequency and voltage of the CPU. The actual workload determines which frequency/voltage is chosen. - Dynamic Power Management (DPM) Power off when device becomes idle. Activation uses temporarily more energy. - Maybe a hybrid policy, e.g. a mix of DPM and DVFS, is preferable. January 30,

14 Measuring Energy Consumption of Applications MRA Cluster Green Software - SEFLab Software Energy Footprint Lab (founded by SIG and HvA) R&D project - SURFsara one of the seven partners Provide insight in energy consumption - Total consumption after run - Consumption during run (time curve) Using sensors in modern CPUs (RAPL) - CPU cores - Memory controller - PAPI to read hardware counters - Correlate with performance measurements (Flop/s/Watt) Using sensors on node (IPMI) - Memory - Disk drives - Network card Use SLURM (Cartesius batch system) - Link with resource manager - Energy consumption in job report January 30,

15 Energy Technology Prof. dr. ir. Bendiks Jan Boersma (TU Delft) Studies - conversion of heat into work or movement - conversion of movement into electricity - interaction between liquids and their environment - fluid mechanics Lower resistance in pipe networks using agents, polymers or chemicals - Gasunie during cold winters - Oil companies Trans-Alaska pipeline - Drilling of oil wells - Fire fighting in situations where the water must be sprayed twice as high or far Two images of axial velocity in a crosssection of a pipe flow. The pictures show the friction Reynolds number January 30,

16 Sustainable Energy Dr. Evgeny Pidko (TU/e assistant professor) Field of study: computational catalysis for sustainable energy technologies Combining theory and experiment to understand mechanisms of catalytic reactions on a molecular level Computational studies using state-of-the-art quantum chemical methods Used to formulate design rules for new and improved catalytic systems Studying different processes related to the conversion of biomass and carbon dioxide to value-added chemicals and fuel components (fuels) Research also focuses on more classical chemical systems in order to make technologies greener January 30,

17 Thank you for listening! January 30,

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