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apeNEXT: Computational Challenges and First Physics Results Florence, 8-10 February, 2007

From Theories to Model Simulations A Computational Challenge. apeNEXT: Computational Challenges and First Physics Results Florence, 8-10 February, 2007. G.C. Rossi University of Rome Tor Vergata INFN - Sezione di Tor Vergata. Outline of the talk Introduction

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apeNEXT: Computational Challenges and First Physics Results Florence, 8-10 February, 2007

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  1. From Theories to Model Simulations A Computational Challenge apeNEXT:Computational Challenges and First Physics ResultsFlorence, 8-10 February, 2007 G.C. Rossi University of Rome Tor Vergata INFN - Sezione di Tor Vergata

  2. Outline of the talk • Introduction • New powerful computational tools • Cross-fertilization of nearby research fields • New challenges and opportunities • Applications • Field Theory and Statistical Mechanics • Dynamical Systems • Systems of biological interest • … • The case of biological systems • What was done with APE • What we are doing • What one may think of doing • A future for a biocomputer?

  3. NATURAL SCIENCE FROM PROGRESSIVE SEPARATION Biology Mathematics Physics TO OVERLAPPING AREAS OF INTEREST Introduction

  4. Similar Mathematical Description and Algorithms Cross-fertilization among nearby Research Fields Statistical Quantum Structure of Econo-physics Physics Field Theory Macro-molecules Stochastic Methods PDE & Stability Analysis Dealt with by Numerical Tools Advances in Computer Developments Numerical Simulations Dedicated Computers Meteorology Fluido- Metabolic Turbulence dynamics networks Chaos

  5. Parallel Platforms PC-Clusters GRID CPU Time Memory Storing Capacity NewArchitectures ExponentialIncreaseof Lattice QCD Computational Astrophysics Weather Forecasting Genome Project Computational Biology WideSpectrumofApplications

  6. No. 1 BlueGene/L at DOE’s LLNB with 280 Tflops (Yet another instance of Zipf’s law!)

  7. We are entering the era of Intelligent Computing Intelligent Exploitation of the available unprecedented Computational Power is driving us to conceive New Problems to more Realistic Model systems to promising New Computational Strategies

  8. Examples 1) Lattice QCD The plaquette expectation value The h’ mass Full QCD with Wilson fermions (either twisted or not) … 2) Dynamical Systems Weather forecasting Climate change scenarios Fluido-dynamics and turbulence … 3) Systems of Biological interest Bi-layers models of cell membranes models of proteins Eteropolymers models of bio-macromolecules recognition processes … models of …

  9. I’m not going to talk about Lattice QCD nor about Dynamical Systems

  10. Rather I will try to comment on Some computational issues in simulating Systems of Biological interest G. La Penna (CNR) F. Guerrieri (UTV) V. Minicozzi (UTV) S. Furlan (CNR) S. Morante (UTV) G. Salina (INFN)

  11. What was done (by us) with APE machines Machine System Results Methods Cubetto Gas/LiquidExtensive MD Torre Butane and H2Ostudies MC APE100 Lipidic Membranesof Phase HMC APEmille Diffusive systemsDiagram FFT APEnext Smooth portability Good use of parallelism Efficient codes Can one move on to more interesting systems?

  12. Model simulations of Phospholipid Bilayers B)512 (256+256) DMMC Molecules DMPC A)64 (32+32) DMMC Molecules DMMC • C)36 (18+18) DMPCMolecules + • 72 (36+36) H2O Molecules La Penna, Minicozzi, Morante, Rossi, Salina

  13. T 60 50 La La+ H2O 40 30 Pb’ 20 Pb’+ H2O 10 Lb’ Lb’+ H2O 0 L 10 20 30 40 50 60 % Water Phospholipid Bilayer Phase Diagram La Fluid Lb’  Gel L  Solid (Superficial) Density Important physiological parameter

  14. More interesting systems/problems • 3D structure of Macromolecules (folding and misfolding) • Antigen – Antibody recognition • Protein – DNA interaction • Protein docking and translocation • … by ab initio (Q.M.) methods • Signal transduction • Metabolic Networks • … by PDE’s and stochastic simulations

  15. 3D structure of Macromolecules Functionality of a protein requires correct folding a-helix b-sheet ... Misfolding leads to malfunctioning, often to severe pathologies Creutzfeldt-Jakob disease BSE (mad cow) Alzheimer disease Cystic fibrosis ... Question: Can we tell the 3D-Structure from the linear a.a. sequence? Answer: Very difficult: it looks like an NP-complete problem!

  16. A test case: Prion Protein PrP (A bit of phenomenology) PrP is a cell glycoprotein (highly expressed in the central nervous system of many mammals), whose physiological role is still unclear It is, however, known to selectively bind copper, Cu Mature PrP has a flexible, disordered, N-terminal (23-120) and a globular C-terminal (121-231) The N-terminal domain contains four (in humans) copies (repeats) of the octa-peptide PHGGGWGQ, each capable of binding Cu Cu-octarepeat interaction is cooperative in nature and can possibly have a role in disease related PrP misfolding and aggregation Experiments more specifically indicate that the Cu binding site is located within the HGGG tetra-peptide An ideal case for Car-Parrinelloab initio simulations

  17. BoPrP (bovine) a-helices = green b-strands = cyan, segments with non-regular secondary structure = yellow flexibly disordered "tail" (23-121) = yellow dots HuPrP (human) a-helices = orange b-strands = cyan segments with non-regular secondary structure = yellow, flexibly disordered "tail" (23-121) = yellow dots

  18. Initial 2x[Cu(+2) HG(-)G(-)G]configuration [Cu(+2)]1 Cu(+2) O N C H [HG(-)G(-)G]1 [Cu(+2)]2 [HG(-)G(-)G]2 V = (15 A)3 Furlan, La Penna, Guerrieri, Morante, Rossi, in JBIC (2007)

  19. 1.8 ps trajectory @ 300K Cu O N C

  20. Final2x[Cu(+2) HG(-)G(-)G] configuration Cu(+2) O N C H [HG(-)G(-)G]1 [Cu(+2)]1 [Cu(+2)]2 [HG(-)G(-)G]2

  21. No Cu atoms  No binding O N C H

  22. Quantum – ESPRESSO (www.pwscf.org) freely available • P. Giannozzi, F. de Angelis, R. Car, J. Chem. Phys. 120, 5903 (2004) • Ultrasoft (Vanderbilt) potentials – PBE Functional • (Fortran 90 – FFTW - OpenMPI)

  23. Standard Benchmarks for CPMD - I S. Kumar, Y. Shi, L.V. Kale, M.E: Tuckerman and G.J. Martyna (2002) 32 molecules PSC Lemieux clusterwith water:256 electrons on a up to 3000 processors cutoff = 70 Ry QUAD Alpha @ 1 GHz 1 g/cm3 CPAIMDcode Very sophisticated implementation software (Charm++)

  24. Standard Benchmarks for CPMD - II J. Hutter and A. Curioni (2006) 32 molecules IBM BlueGene/Lwith water:256 electrons on up to 1024 processors cutoff= 100 Ry PowerPC 440 @ 0.7 GHz CPMD code IBM Res. Lab. (Zurich) M.P.I. Stuttgart Very sophisticated implementation software

  25. Standard Benchmarks for CPMD - III J. Hutter and A. Curioni (2006) 1000 atoms IBM clusterwith SiC:4000 electrons on up to 40 x 32 processors cutoff = 60 Ry Power4 @ 1.3 GHz CPMD code (Fortran 77) IBM Res. Zurich Lab. M.P.I. Stuttgart

  26. Standard Benchmarks for CPMD - IV S. Furlan, F. Guerrieri, G. La Penna, S. Morante and G.C. Rossi (2007) water: 32 molecules Fermi cluster with 256 electrons on up to 16 processors Intel/Pentium IV @ 1.7 GHz cutoff: small = 25 Ry large = 250 Ry V = (10 Å)3 ESPRESSO code Fortran 90 not worse that the other big platforms!

  27. CP computational burden fromJ. Hutter Typical for a long range problem on a non optimazed comm. network

  28. A Few (Tentative) Conclusions • If one so wishes, APE (like BlueGene/L) can be used for CPMD • (Small) PC-clusters almost as good as large parallel platforms • efficiency is limited by communication latency • scaling with number of nodes is limited (upper bound) • Question: Do we really need such a massive parallelism? • A(n intermediate a)nswer: a compact, fully connected, cluster

  29. Network topology:1D Fast Fourier Transform one way comm. ring P2 comm. steps of N/P lumps = NP Pr1 Pr1 PrP Pr2 PrP Pr2 P cyclically ordered processors (N/P lumps of data per processor) n.n. comm. P comm. steps of N/P lumps = N P procesors working on N/P lumps + non-local communication steps of N/P lumps

  30. Ideal communication network: all-to-all Pr1 Pr1 All-to-all comm. 1 comm step of N/P lumps  N/P PrP Pr2 PrP Pr2 Pr1 PrP Pr2

  31. Towards a biocomputer… • # physical links grows with P2 • Machine linear dimension grows with P • Very hard (impossible?) to maintain synchronization @ GHz over few 102 meters • Limited number of processors with 5-10 Tflops peak speed • Most possibly connected communication network

  32. Two (among many) options or 2x2x2 1) A 3x3x3 cube of Cell processors 27 x 200 Gflops= 5.4 Tflops 1-dimensional all-to-all comm. in each dimension = 81 links (full 3-dimesional = 351 links) y+2 Processor available Networking? y+3 y+1 y+4 or 4x4x4 x+1 x,y,z x+2 2) A 5x5x5 cube of APE(next)2 processors 125 x 5(?) Gflops= 6.25 Tflops x+3 x+4 z+1 z+2 z+3 z+4 1-dimensional all-to-all comm. in each dimension = 750 links (full 3-dimesional = 7750 links) Processor under development Networking know-how available

  33. Conclusions To attack “biological recognition events” • we need a factor 103 in simulation times (few nanoseconds) • system size (few 103 - 104 atoms/electrons) is almost OK • not too far from target with current technology! Very exciting perspectives

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