Supercomputers: instruments for science or dinosaurs that haven t gone extinct yet? Thomas C. Schulthess

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1 Supercomputers: instruments for science or dinosaurs that haven t gone extinct yet? Thomas C. Schulthess 1

2 Do you really mean dinosaurs? We must be in the wrong movie 2

3 Not much has changed since the late 1980s pick up any book on HPC from the early 1990s, e.g. this on from 1993 Shared-memory multiprocessors cash coherency pipelining, SIMD, threading, Explicitly parallel languages (imperative programming) Fortran 90 High Performance Fortran Explicitly Parallel Programming Environment (PVM) No fundamental changes sind then! 3

4 Incremental and other changes Evolution of Fortran Evolution of C++ you can mention C++ at an HPC conference without being thrown out and template meta-programming is somewhat usable PVM to MPI OpenMP besides pthreads PGAS languages (not sure how broadly accepted) Only two potentially disruptive changes (in terms of broader acceptance) (1) Python but not in HPC please! (2) CUDA with GPU are you kidding me? 4

5 5

Pete s Top 5 HPC initiatives or technologies to watch in 2013 (www.hpcwire.

6 Pete s Top 5 HPC initiatives or technologies to watch in 2013 ( Big Data solutions Industrial use of petascale supercomputers Accelerator and Heterogeneous Computing The fusion of HPC technologies into big data computing applications Anything Cray is doing! source: 6

Raj s few worth considerations (www.hpcwire.

7 Raj s few worth considerations ( Heterogeneous computing becomes the next step, not a detour not just in highend, but mainstream HPC Big Data transforms HPC; First real business value propositions from use of analytics beyond niche using HPC Domain optimised HPC Clouds the promise of HPC with the value of the cloud model Integration driving fundamental changes in system architecture Everyone wants their own not just investing in having the best of class, but also in making the best of class source: 7

8 What is Big Data? It is not big storage, rather one should think of Data Science 8

9 So what is Data Science?? Forget about MapReduce, this is just one of many algorithmic motifs, HDFS is the key: performance sucks but the API is important 9

10 Data Science is not simply engineering of big databases Traditional database engineering focuses on database design, software engineering, data flow, data extraction, etc. Data science is about discovery: identify[ing] good data sources and metrics. Sometimes request[ing] the data to be created access: access the data, sometimes via an API, distilling: extract from the data the information that leads to source of quotes: Vincent Granville s Developing Analytic Talent, becoming a data scientists 10

11 Data science is more than statistic Analysis of NASA images to discover new planets, astroids, Computational chemistry to simulate new molecules for treatment Returning highly relevant results to any Google search Scoring all credit card transactions (fraud detection) Tax fraud detection and detection of terrorism Weather forecasts source: Vincent Granville s Developing Analytic Talent, becoming a data scientists So serious data science is just science! 11

12 I have studied all available charts of the planets and stars and none of them match the others. There are just as many measurements and methods as there are astronomers and all of them disagree. What is needed is a long-term project with the aim of mapping the heavens conducted from a single location over a period of several years. Tycho Brahe,

13 The first BigData project in history source: 13

14 Data given to Johannes Kepler source: 14

15 Kepler s modelling and simulations b r M = E - ε sin E" E M a φ! e=ℇa 1. Solve E(M)"(Numerics)! 2. Solve φ(e) "(Geometry) 15

16 After Kepler s analysis and Newton s theory source: 16

17 source: A. Fichtner, ETH Zurich 17

18 Data from many stations and earthquakes source: A. Fichtner, ETH Zurich 18

19 Very large simulations allow inverting large data sets in oder to generate high-resolution models of the earth s mantle v s [km/s] v s [km/s] source: A. Fichtner, ETH Zurich 19

20 Ice floats! first early science result on Piz Daint (Joost VandeVondele, Jan. 2014) high-accuracy quantum simulation produce correct results for water 20

21 Jacob s ladder to heaven of chemical accuracy (J. Perdew) Full many-body Schrödinger Equation: w( i, j) = e 2 r i r j (H E) ( 1,..., N )=0 = { r, } H = NX i=1 2 2m r2 i + v( i ) NX i6=j=1 w( i, j) Ice floats with new rung 5 simulations using MP2-based simulations with CP2K (VandeVondele & Hutter) Ice didn t float with previous simulations using rung 4 hybrid functionals Kohn-Sham Equation with Local Density Approximation: ~ 2 2m r2 + v LDA ( r) i ( r) = i i ( r) 21

Modelling interactions between water The challenge ofmolecules water requires quantum simulations with extreme accuracy Weak interactions dominate properties!

9999% Roughly 99.9999% accuracy (or error cancellation) needed!

22 Modelling interactions between water The challenge ofmolecules water requires quantum simulations with extreme accuracy Weak interactions dominate properties! Hydrogen-bonding most prominent. Energy scales total energy: a.u. Total energy: a.u.. hydrogen bonds: ~ a.u. Needed accuracy a.u.! The density of water and ice : rung required accuracy: % Roughly % accuracy (or error cancellation) needed! If you don t get this right, then BLYP liquid water: ~800 ab initio simulation prior to Joost s MP2basedBLYP runs ice: with Est.CP2K ~890 g/l ( , Lugano, May 6, 2014 DoesCUG ice float? No! 22

23 Pillars of the scientific method Mathematics / Simulation (1) Synthesis of models and data: recognising characteristic features of complex systems with calculations of limited accuracy (e.g. inverse problems) (2) Solving theoretical problems with high precision: complex structures emerge from simple rules (natural laws), more accurate predictions from beautiful theory (in the Penrose sense) Theory (models) Experiment (data) 23

24 Pillars of the scientific method Mathematics / Simulation (1) Synthesis of models and data: recognising characteristic features of complex systems with calculations of limited accuracy (e.g. inverse problems) (2) Solving theoretical problems with high precision: complex structures emerge from simple rules (natural laws), more accurate predictions from beautiful theory Note (in the changing Penrose sense) role of high-performance computing: HPC is now an essential tool for science, used by all scientists Theory (models) Experiment (data) (for better or worse), rather than being limited to the domain of applied mathematics and providing numerical solution to theoretical problems only few understand 24

25 Given all this, here is a bold prediction for what Cray s roadmap might look like the essence of BigData vs. HPC BigData version: > HDFS > Python and other dynamic languages HPC version: > Fortran > MPI+OpenMP 25

26 We need fundamentally new programming models, but won t accept anything other than Fortran MPI+OpenMP the HPC community discussing exascale challenges 26

to supercomputers > vectorize > parallelize >

27 Mathematical description Physical model Discretisation / algorithm Domain science (incl. applied mathematics) Code / implementation Code compilation A given supercomputer Port serial code to supercomputers > vectorize > parallelize > petascaling > exascaling >... Computer engineering (& computer science) 27

velocities pressure temperature water Mathematical description Physical model

Code / implementation Code compilation Architectural options / design A given

28 velocities pressure temperature water Mathematical description Physical model turbulence Discretisation / algorithm Domain science (incl. applied mathematics) lap(i,j,k) = 4.0 * data(i,j,k) + data(i+1,j,k) + data(i-1,j,k) + data(i,j+1,k) + data(i,j-1,k); Code / implementation Code compilation Architectural options / design A given supercomputer Port serial code to supercomputers > vectorize > parallelize > petascaling > exascaling >... Computer engineering (& computer science) 28

29 source: David Leutwyler 29

30 Speedup of COSMO-2 production problem apples to apples comparison with 33h forecast of Meteo Swiss 4x Monte Rosa Cray XE6 (Nov. 2011) Tödi Cray XK7 (Nov. 2012) Piz Daint Cray XC30 (Nov. 2012) Piz Daint Cray XC30 hybrid (GPU) (Nov. 2013) 4x 3x 3x 1.67x 3.36x 2x New HP2C funded code 1.77x 1.49x 2.5x 2x 1x 1.4x 1.35x 1x Current production code 30

31 Energy to solution (kwh / ensemble member) 6.0 Cray XE6 (Nov. 2011) Cray XK7 (Nov. 2012) Cray XC30 (Nov. 2012) Cray XC30 hybrid (GPU) (Nov. 2013) Current production code x 1.75x New HP2C funded code x 3.93x 6.89x 2.51x 2.64x

32 COSMO: current and new (HP2C developed) code main (current / Fortran) main (new / Fortran) dynamics (C++) physics (Fortran) dynamics (Fortran) physics (Fortran) with OpenMP / OpenACC stencil library X86 GPU Shared Infrastructure boundary conditions & halo exchg. Generic Comm. Library MPI MPI or whatever system system 32

33 velocities pressure temperature water Mathematical description Physical model turbulence Discretization / algorithm Domain science (incl. applied mathematics) lap(i,j,k) = 4.0 * data(i,j,k) + data(i+1,j,k) + data(i-1,j,k) + data(i,j+1,k) + data(i,j-1,k); Code / implementation Tools & Libraries Code compilation Architectural options / design Optimal algorithm Auto tuning Computer engineering (& computer science) 33

34 velocities pressure temperature water Mathematical description Physical model Model development based on Python or equivalent dynamic language turbulence Domain science lap(i,j,k) = 4.0 * data(i,j,k) + data(i+1,j,k) + data(i-1,j,k) + data(i,j+1,k) + data(i,j-1,k); Code / implementation Discretization / algorithm Tools & Libraries Computer engineering (& computer science) Code compilation Architectural options / design Optimal algorithm Auto tuning co-design 34

35 COSMO in five year: current and new (2019) code main (current / Fortran) main (new / Python ) physics ( Python ) dynamics (C++ or Python ) physics (Fortran) dynamics (Fortran) system MPI some tools (Fortan / C++) other tools BE.. grid tools BE1 MPI or whatever system BE Shared Infrastructure boundary conditions & halo exchg. Generic Comm. Library 35

36 Software is getting slower more rapidly than hardware becomes faster Niklaus Wirth,

37 Thank You! 37

Piz Daint & Piz Kesch : from general purpose supercomputing to an appliance for weather forecasting. Thomas C. Schulthess

Piz Daint & Piz Kesch : from general purpose supercomputing to an appliance for weather forecasting Thomas C. Schulthess 1 Cray XC30 with 5272 hybrid, GPU accelerated compute nodes Piz Daint Compute node: