Computational Aspects of Multi-scale Modeling Ahmed Sameh, Ananth Grama Computing Research Institute Purdue University.

Slides:



Advertisements
Similar presentations
Multiscale Dynamics of Bio-Systems: Molecules to Continuum February 2005.
Advertisements

PERFORMANCE EVALUATION OF USER-REAXC PACKAGE Hasan Metin Aktulga Postdoctoral Researcher Scientific Computing Group Lawrence Berkeley National Laboratory.
Modelling of Defects DFT and complementary methods
Aug 9-10, 2011 Nuclear Energy University Programs Materials: NEAMS Perspective James Peltz, Program Manager, NEAMS Crosscutting Methods and Tools.
Numerical Parallel Algorithms for Large-Scale Nanoelectronics Simulations using NESSIE Eric Polizzi, Ahmed Sameh Department of Computer Sciences, Purdue.
 Product design optimization Process optimization Reduced experimentation Physical system Process model Product model Product Market need Multiscale Modeling.
Modern iterative methods For basic iterative methods, converge linearly Modern iterative methods, converge faster –Krylov subspace method Steepest descent.
Peyman Mostaghimi, Martin Blunt, Branko Bijeljic 11 th January 2010, Pore-scale project meeting Direct Numerical Simulation of Transport Phenomena on Pore-space.
Nazgol Haghighat Supervisor: Prof. Dr. Ir. Daniel J. Rixen
MA5233: Computational Mathematics
MCE 561 Computational Methods in Solid Mechanics
1 Parallel Simulations of Underground Flow in Porous and Fractured Media H. Mustapha 1,2, A. Beaudoin 1, J. Erhel 1 and J.R. De Dreuzy IRISA – INRIA.
NUS CS5247 A dimensionality reduction approach to modeling protein flexibility By, By Miguel L. Teodoro, George N. Phillips J* and Lydia E. Kavraki Rice.
Parallel Performance of Hierarchical Multipole Algorithms for Inductance Extraction Ananth Grama, Purdue University Vivek Sarin, Texas A&M University Hemant.
Computational Science jsusciencesimulation Principles of Scientific Simulation Spring Semester 2005 Geoffrey Fox Community.
Algorithms and Software for Large-Scale Simulation of Reactive Systems _______________________________ Ananth Grama Coordinated Systems Lab Purdue University.
1 A Domain Decomposition Analysis of a Nonlinear Magnetostatic Problem with 100 Million Degrees of Freedom H.KANAYAMA *, M.Ogino *, S.Sugimoto ** and J.Zhao.
Simulating Quarks and Gluons with Quantum Chromodynamics February 10, CS635 Parallel Computer Architecture. Mahantesh Halappanavar.
Parallel Processing CS453 Lecture 2.  The role of parallelism in accelerating computing speeds has been recognized for several decades.  Its role in.
PuReMD: Purdue Reactive Molecular Dynamics Package Hasan Metin Aktulga and Ananth Grama Purdue University TST Meeting,May 13-14, 2010.
Evaluating Sparse Linear System Solvers on Scalable Parallel Architectures Ahmed Sameh and Ananth Grama Computer Science Department Purdue University.
1 Using the PETSc Parallel Software library in Developing MPP Software for Calculating Exact Cumulative Reaction Probabilities for Large Systems (M. Minkoff.
Fast Low-Frequency Impedance Extraction using a Volumetric 3D Integral Formulation A.MAFFUCCI, A. TAMBURRINO, S. VENTRE, F. VILLONE EURATOM/ENEA/CREATE.
Computational issues in Carbon nanotube simulation Ashok Srinivasan Department of Computer Science Florida State University.
Fast Thermal Analysis on GPU for 3D-ICs with Integrated Microchannel Cooling Zhuo Fen and Peng Li Department of Electrical and Computer Engineering, {Michigan.
Chicago, July 22-23, 2002DARPA Simbiosys Review 1 Monte Carlo Particle Simulation of Ionic Channels Trudy van der Straaten Umberto Ravaioli Beckman Institute.
2005 Materials Computation Center External Board Meeting The Materials Computation Center Duane D. Johnson and Richard M. Martin (PIs) Funded by NSF DMR.
ParCFD Parallel computation of pollutant dispersion in industrial sites Julien Montagnier Marc Buffat David Guibert.
Monte Carlo Methods Versatile methods for analyzing the behavior of some activity, plan or process that involves uncertainty.
On the Use of Sparse Direct Solver in a Projection Method for Generalized Eigenvalue Problems Using Numerical Integration Takamitsu Watanabe and Yusaku.
Molecular Simulation of Reactive Systems. _______________________________ Sagar Pandit, Hasan Aktulga, Ananth Grama Coordinated Systems Lab Purdue University.
1 1 What does Performance Across the Software Stack mean?  High level view: Providing performance for physics simulations meaningful to applications 
Algorithms and Software for Large-Scale Simulation of Reactive Systems _______________________________ Metin Aktulga, Sagar Pandit, Alejandro Strachan,
Outline Numerical implementation Diagnosis of difficulties
Parallel and Distributed Computing Research at the Computing Research Institute Ananth Grama Computing Research Institute and Department of Computer Sciences.
Molecular Modelling - Lecture 2 Techniques for Conformational Sampling Uses CHARMM force field Written in C++
October 2008 Integrated Predictive Simulation System for Earthquake and Tsunami Disaster CREST/Japan Science and Technology Agency (JST)
Javier Junquera Introduction to atomistic simulation methods in condensed matter Alberto García Pablo Ordejón.
Progress on Component-Based Subsurface Simulation I: Smooth Particle Hydrodynamics Bruce Palmer Pacific Northwest National Laboratory Richland, WA.
Linear Algebra Libraries: BLAS, LAPACK, ScaLAPACK, PLASMA, MAGMA
Partial Derivatives Example: Find If solution: Partial Derivatives Example: Find If solution: gradient grad(u) = gradient.
Algebraic Solvers in FASTMath Argonne Training Program on Extreme-Scale Computing August 2015.
Consider Preconditioning – Basic Principles Basic Idea: is to use Krylov subspace method (CG, GMRES, MINRES …) on a modified system such as The matrix.
Monte Carlo Linear Algebra Techniques and Their Parallelization Ashok Srinivasan Computer Science Florida State University
C OMPUTATIONAL R ESEARCH D IVISION 1 Defining Software Requirements for Scientific Computing Phillip Colella Applied Numerical Algorithms Group Lawrence.
F. Fairag, H Tawfiq and M. Al-Shahrani Department of Math & Stat Department of Mathematics and Statistics, KFUPM. Nov 6, 2013 Preconditioning Technique.
Anders Nielsen Technical University of Denmark, DTU-Aqua Mark Maunder Inter-American Tropical Tuna Commission An Introduction.
A Parallel Hierarchical Solver for the Poisson Equation Seung Lee Deparment of Mechanical Engineering
Adaptive grid refinement. Adaptivity in Diffpack Error estimatorError estimator Adaptive refinementAdaptive refinement A hierarchy of unstructured gridsA.
Multipole-Based Preconditioners for Sparse Linear Systems. Ananth Grama Purdue University. Supported by the National Science Foundation.
An Introduction to Computational Fluids Dynamics Prapared by: Chudasama Gulambhai H ( ) Azhar Damani ( ) Dave Aman ( )
Computational Physics (Lecture 11) PHY4061. Variation quantum Monte Carlo the approximate solution of the Hamiltonian Time Independent many-body Schrodinger’s.
Computational Fluid Dynamics Lecture II Numerical Methods and Criteria for CFD Dr. Ugur GUVEN Professor of Aerospace Engineering.
Monte Carlo Linear Algebra Techniques and Their Parallelization Ashok Srinivasan Computer Science Florida State University
Computational Techniques for Efficient Carbon Nanotube Simulation
Xing Cai University of Oslo
Introduction to the Finite Element Method
Data Structures for Efficient and Integrated Simulation of Multi-Physics Processes in Complex Geometries A.Smirnov MulPhys LLC github/mulphys
Fei Cai Shaogang Wu Longbing Zhang and Zhimin Tang
Algorithms and Software for Large-Scale Simulation of Reactive Systems
GENERAL VIEW OF KRATOS MULTIPHYSICS
Supported by the National Science Foundation.
Numerical Linear Algebra
Computational Techniques for Efficient Carbon Nanotube Simulation
Algorithms and Software for Large-Scale Simulation of Reactive Systems
Ph.D. Thesis Numerical Solution of PDEs and Their Object-oriented Parallel Implementations Xing Cai October 26, 1998.
Parallel Implementation of Adaptive Spacetime Simulations A
Computational issues Issues Solutions Large time scale
Continuum Simulation Monday, 9/30/2002.
Presentation transcript:

Computational Aspects of Multi-scale Modeling Ahmed Sameh, Ananth Grama Computing Research Institute Purdue University.

Technical Objectives Efficient Numerical Algorithms Parallel and Distributed Computing Software and Libraries Interfaces to Experimental Data Acquisition and Design Components Interfaces to Application Servers The overall goal is to develop a comprehensive simulation environment built upon novel algorithms and parallelism for multi- scale modeling of NEMS.

Technical Objectives

Technical Challenges Diversity of phenomena - multiphysics Variance in spatial scales - nm to cm Variance in temporal scales - fs to s Variety of modeling phenomena Self consistency between scales and phenomena

Technical Challenges

Making software tools available to applications researchers - next generation PUNCH Portable, efficient parallel software development using in-house tools such as the Polaris compiler and Ursa Minor performance profiler

Computational and Mathematical Challenges Novel problems in linear algebra Special functions and approximations Self consistency between scales and phenomena Highly dynamic geometries and interfaces Extremely large number of degrees of freedom Need for scalable parallelism

Quantum Simulations Five to six orders of magnitude more expensive than classical MD Typical simulations limited to few hundred atoms Ab-initio methods solve complex many- body Schrodinger equation A number of packages such as VASP and ABINIT have been developed

Quantum Simulations Iterative matrix diagonalization schemes based on conjugate gradients, block Davidson methods (and our own Trace Minimization Scmeme), and residual minimization are at the core Preconditioning is critical for accelerating this procedure We have extensive experience in iterative schemes, eigenvalue problems, and developing parallel preconditioners.

Molecular Dynamics Describe atomic-scale phenomena Particles move as rigid bodies in many-body potential A variety of potentials describe pair-wise interactions, local potential (using e.d.f.), and long-range atomic interactions Short-range potentials are typically truncated and long-range potentials are evaluated using particle-particle-particle-mesh methods.

Molecular Dynamics - Prior Results Error control in hierarchical approximation techniques Parallel formulations - high efficiency and scalability to hundreds of processors Use in iterative boundary element solvers with multipole-based matrix vector products Preconditioners for boundary element solvers Use of multipole operators as preconditioners for sparse matrices

Outstanding Challenges in Molecular Dynamics Approximations for long range potential functions Efficient algorithms for integrating long- range and short-range potentials in a parallel framework Self-consistent handshake with quantum simulations Software development

Meso-scale Models micron scales Defect-interface interactions and defect dynamics become significant Models for dynamics of defect ensembles (interacting cracks, dislocations) Statistical approaches (Kinetic Monte Carlo Methods)

Continuum Models Space and time scales in excess of defects (100 microns or more) Finite difference and finite element approaches are most commonly used In addition to classical problems in FE/FD modeling of highly unstructured domains, NEMS pose novel problems for continuum models.