10/21/20091 Protein Explorer: A Petaflops Special-Purpose Computer System for Molecular Dynamics Simulations Makoto Taiji, Tetsu Narumi, Yousuke Ohno,

Slides:



Advertisements
Similar presentations
Learning Computer Simulation with an MDGRAPE Accelerator Tetsu Narumi 1, Fumikazu Konishi 2, Ryo Umetsu 2, Makoto Taiji 2, Toshikazu Ebisuzaki 3, Kenji.
Advertisements

Instructor Notes Lecture discusses parallel implementation of a simple embarrassingly parallel nbody algorithm We aim to provide some correspondence between.
Homework 2 (due We, Feb. 5): Reading: Van Holde, Chapter 1 Van Holde Chapter 3.1 to 3.3 Van Holde Chapter 2 (we’ll go through Chapters 1 and 3 first. 1.Van.
N-Body I CS 170: Computing for the Sciences and Mathematics.
Bare Surface Tension and Surface Fluctuations of Clusters with Long–Range Interaction D.I. Zhukhovitskii Joint Institute for High Temperatures, RAS.
New Architectures for a New Biology Martin M. Deneroff D. E. Shaw Research, LLC.
1 NAMD - Scalable Molecular Dynamics Gengbin Zheng 9/1/01.
A Digital Laboratory “In the real world, this could eventually mean that most chemical experiments are conducted inside the silicon of chips instead of.
Petaflops Special-Purpose Computer for Molecular Dynamics Simulations Makoto Taiji High-Performance Molecular Simulation Team Computational & Experimental.
GravitySimulator Beyond the Million Body Problem Collaborators:Rainer Spurzem (Heidelberg) Peter Berczik (Heidelberg/Kiev) Simon Portegies Zwart (Amsterdam)
Special-purpose computers for scientific simulations Makoto Taiji Processor Research Team RIKEN Advanced Institute for Computational Science Computational.
Abhinav Bhatele, Laxmikant V. Kale University of Illinois at Urbana-Champaign Sameer Kumar IBM T. J. Watson Research Center.
Molecular Dynamics, Monte Carlo and Docking Lecture 21 Introduction to Bioinformatics MNW2.
Molecular Dynamics Simulation (a brief introduction)
The Protein Folding Problem David van der Spoel Dept. of Cell & Mol. Biology Uppsala, Sweden
Electrical Potential Energy Chapter Electrical Potential Energy Electrical Potential Energy – Potential energy associated with an object due to.
Bioinf. Data Analysis & Tools Molecular Simulations & Sampling Techniques117 Jan 2006 Bioinformatics Data Analysis & Tools Molecular simulations & sampling.
Molecular Modeling Fundamentals: Modus in Silico C372 Introduction to Cheminformatics II Kelsey Forsythe.
Deformation of Nanotubes Yang Xu and Kenny Higa MatSE 385
Algorithms and Software for Large-Scale Simulation of Reactive Systems _______________________________ Ananth Grama Coordinated Systems Lab Purdue University.
ANTON D.E Shaw Research. Force Fields: Typical Energy Functions Bond stretches Angle bending Torsional rotation Improper torsion (sp2) Electrostatic interaction.
02/03/10 CSCE 769 Dihedral Angles Homayoun Valafar Department of Computer Science and Engineering, USC.
Molecular Dynamics Sathish Vadhiyar Courtesy: Dr. David Walker, Cardiff University.
Computational issues in Carbon nanotube simulation Ashok Srinivasan Department of Computer Science Florida State University.
Molecular Dynamics Collection of [charged] atoms, with bonds – Newtonian mechanics – Relatively small #of atoms (100K – 10M) At each time-step – Calculate.
Molecular Dynamics A brief overview. 2 Notes - Websites "A Molecular Dynamics Primer", F. Ercolessi
CZ5225 Methods in Computational Biology Lecture 4-5: Protein Structure and Structural Modeling Prof. Chen Yu Zong Tel:
Scheduling Many-Body Short Range MD Simulations on a Cluster of Workstations and Custom VLSI Hardware Sumanth J.V, David R. Swanson and Hong Jiang University.
Force Fields and Numerical Solutions Christian Hedegaard Jensen - within Molecular Dynamics.
E-science grid facility for Europe and Latin America E2GRIS1 André A. S. T. Ribeiro – UFRJ (Brazil) Itacuruça (Brazil), 2-15 November 2008.
P ARALLELIZATION IN M OLECULAR D YNAMICS By Aditya Mittal For CME346A by Professor Eric Darve Stanford University.
Computational Chemistry Molecular Mechanics/Dynamics F = Ma Quantum Chemistry Schr Ö dinger Equation H  = E 
Anton Supercomputer Brandon Dean 4/28/15. History Named after Antonie van Leeuwenhoek – “father of microbiology” Molecular Dynamics (MD) simulations were.
NIH Resource for Biomolecular Modeling and Bioinformatics Beckman Institute, UIUC NAMD Development Goals L.V. (Sanjay) Kale Professor.
NIH Resource for Biomolecular Modeling and Bioinformatics Beckman Institute, UIUC NAMD Development Goals L.V. (Sanjay) Kale Professor.
Protein Explorer: A Petaflops Special Purpose Computer System for Molecular Dynamics Simulations David Gobaud Computational Drug Discovery Stanford University.
Molecular Dynamics Simulations of Proteins with Petascale Special-Purpose Computer MDGRAPE-3 Makoto Taiji Deputy Project Director Computational & Experimental.
An FPGA Implementation of the Ewald Direct Space and Lennard-Jones Compute Engines By: David Chui Supervisor: Professor P. Chow.
Overcoming Scaling Challenges in Bio-molecular Simulations Abhinav Bhatelé Sameer Kumar Chao Mei James C. Phillips Gengbin Zheng Laxmikant V. Kalé.
A Technical Introduction to the MD-OPEP Simulation Tools
Applied Bioinformatics Week 12. Bioinformatics & Functional Proteomics How to classify proteins into functional classes? How to compare one proteome with.
Molecular Dynamics simulations
Molecular simulation methods Ab-initio methods (Few approximations but slow) DFT CPMD Electron and nuclei treated explicitly. Classical atomistic methods.
Molecular Dynamics Inter-atomic interactions. Through-bond versus Through-space. Or they are Covalent versus Non-covalent.
Molecular Mechanics Studies involving covalent interactions (enzyme reaction): quantum mechanics; extremely slow Studies involving noncovalent interactions.
Molecular Simulation of Reactive Systems. _______________________________ Sagar Pandit, Hasan Aktulga, Ananth Grama Coordinated Systems Lab Purdue University.
Parallel & Cluster Computing N-Body Simulation and Collective Communications Henry Neeman, Director OU Supercomputing Center for Education & Research University.
ANTON D.E Shaw Research.
1 Statistical Mechanics and Multi- Scale Simulation Methods ChBE Prof. C. Heath Turner Lecture 18 Some materials adapted from Prof. Keith E. Gubbins:
Parallel Programming & Cluster Computing N-Body Simulation and Collective Communications Henry Neeman, University of Oklahoma Paul Gray, University of.
Anton, a Special-Purpose Machine for Molecular Dynamics Simulation By David E. Shaw et al Presented by Bob Koutsoyannis.
© 2010 Pittsburgh Supercomputing Center Pittsburgh Supercomputing Center RP Update July 1, 2010 Bob Stock Associate Director
Review Session BS123A/MB223 UC-Irvine Ray Luo, MBB, BS.
MSc in High Performance Computing Computational Chemistry Module Parallel Molecular Dynamics (i) Bill Smith CCLRC Daresbury Laboratory
Molecular Mechanics (Molecular Force Fields). Each atom moves by Newton’s 2 nd Law: F = ma E = … x Y Principles of M olecular Dynamics (MD): F =
A Pattern Language for Parallel Programming Beverly Sanders University of Florida.
Dynameomics: Protein Mechanics, Folding and Unfolding through Large Scale All-Atom Molecular Dynamics Simulations INCITE 6 David A. C. Beck Valerie Daggett.
Molecular dynamics (MD) simulations  A deterministic method based on the solution of Newton’s equation of motion F i = m i a i for the ith particle; the.
1 Calculation of Radial Distribution Function (g(r)) by Molecular Dynamic.
Parallel Molecular Dynamics A case study : Programming for performance Laxmikant Kale
Fermi National Accelerator Laboratory & Thomas Jefferson National Accelerator Facility SciDAC LQCD Software The Department of Energy (DOE) Office of Science.
Introduction. News you can use Hardware –Multicore chips (2009: mostly 2 cores and 4 cores, but doubling) (cores=processors) –Servers (often.
Flexibility and Interoperability in a Parallel MD code Robert Brunner, Laxmikant Kale, Jim Phillips University of Illinois at Urbana-Champaign.
QUANTUM COMPUTING: Quantum computing is an attempt to unite Quantum mechanics and information science together to achieve next generation computation.
Computational Techniques for Efficient Carbon Nanotube Simulation
Introduction to Molecular Simulation
Algorithms and Software for Large-Scale Simulation of Reactive Systems
Molecular simulation methods
Computational Techniques for Efficient Carbon Nanotube Simulation
Algorithms and Software for Large-Scale Simulation of Reactive Systems
Presentation transcript:

10/21/20091 Protein Explorer: A Petaflops Special-Purpose Computer System for Molecular Dynamics Simulations Makoto Taiji, Tetsu Narumi, Yousuke Ohno, Noriyuki Futatsugi, Atsushi Suenaga, Naoki Takada and Akihiko Konagya High-Performance Biocomputing Research Team, Bioinformatics Group, Genomic Sciences Center, Institute of Physical and Chemical Research (RIKEN) ACM/IEEE SC2003 Conference Presented by Jing Xue For Computer Architecture Reading Group

10/21/20092 Protein Explorer (PE) System What is PE system ? –special-purpose computer system for molecular dynamics simulations particularly protein simulations –PC cluster equipped with special-purpose engines that calculate non-bonded interactions between atoms What is MDGRAPE-3 Chip? –A dedicated LSI performs these force calculations Why is specialized? –Most time is spent on the calculation of long-range forces Gravitational, Coulomb, and Van der Waals forces –Communication-to-computation ratio is 0.25 Gbytes/sec*Tflops What is GRAPE? –GRAPE (GRAvity PipE) is a project to develop high-performance competitive special purpose systems –Classical particles (e.g. gravitational N-body problems, Molecular dynamics (MD) simulations) What is pioneered? –Delft Molecular Dynamics Processor (DMDP) and FASTRUN processor What is the targets of PE system? –High-precision screening for drug design and large scale simulations –Funded by Protein 3000 project started in 2002

10/21/20093 MDGRAPE-3 System

10/21/20094 What Protein Explorer Calculates Two-body forces on i-th particle Fi Where,. The vectors ri, rj are the position vectors of the i,j-th particles and is an arbitrary smooth function. Coulomb forces: Lennard-Jones potential:

10/21/20095 MDGRAPE-3 chip

10/21/20096 Software Cost and Performance Estimation 1.Call pe_start_calc 2.Perform other force calculations 3.Call pe_wait_calc (force) 4.Add other forces such as bonding force to ‘force’, then calculates orbits of particles, and increments a time 1.Get command sequence from host memory 2.Write positions, charges, and species of j- particles to the chip 3.Broadcast positions of 40 i-particles to all the chip 4.Issue the calculation command 5.Do calculation 6.Read results out from the chip 7.Transfer result to host memory 8.Repeat from 3-7 for all i-particles 9.Tell the end of the calculation to the host

10/21/20097 Anton, a Special-Purpose Machine for Molecular Dynamics Simulation David E. Shaw et. al. D. E. Shaw Research ISCA 2007 Presented by Alok Garg

10/21/20098 Force Calculation Mechanics force fields on 200,000 molecules/time step Total energy –Bonded (linear time complexity) –Non-bonded - O(n 2 ) Non-bonded interactions –Range limited interactions –Long-range interactions Expresses as a convolution Computed by: FFT, multiplication, and inverse FFT 73% computation is range limited interactions

10/21/20099 Range-Limited Pair-wise Interactions

10/21/ Parallelization Algorithm

10/21/ Computation e.g. A cubical system of 25,000 atoms Atoms in tower: 220 Atoms in plate: 430 Total pairs: 94,600 23,000 satisfy the selection rule Each node must import 550 particles

10/21/ Specialized Hardware

10/21/ Rest of the Machine

10/21/ Major Computational Tasks

10/21/ System Architecture Features –Multicast support –Compressed transfer of sparse data structures Push based communication Memory accumulate forces for each particle

10/21/ Performance Results