1 1  Capabilities: Dynamic load balancing and static data partitioning -Geometric, graph-based, hypergraph-based -Interfaces to ParMETIS, PT-Scotch, PaToH.

Slides:



Advertisements
Similar presentations
PERFORMANCE EVALUATION OF USER-REAXC PACKAGE Hasan Metin Aktulga Postdoctoral Researcher Scientific Computing Group Lawrence Berkeley National Laboratory.
Advertisements

Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energy under contract.
Computational Science R&D for Electromagnetic Modeling: Recent Advances and Perspective to Extreme-Scale Lie-Quan Lee For SLAC Computational Team ComPASS.
Parallelizing stencil computations Based on slides from David Culler, Jim Demmel, Bob Lucas, Horst Simon, Kathy Yelick, et al., UCB CS267.
Software Version Control SubVersion software version control system WebSVN graphical interface o View version history logs o Browse directory structure.
Some Experiences on Parallel Finite Element Computations Using IBM/SP2 Yuan-Sen Yang and Shang-Hsien Hsieh National Taiwan University Taipei, Taiwan, R.O.C.
CS267 L12 Sources of Parallelism(3).1 Demmel Sp 1999 CS 267 Applications of Parallel Computers Lecture 12: Sources of Parallelism and Locality (Part 3)
Parallel Mesh Refinement with Optimal Load Balancing Jean-Francois Remacle, Joseph E. Flaherty and Mark. S. Shephard Scientific Computation Research Center.
High Performance Computing 1 Parallelization Strategies and Load Balancing Some material borrowed from lectures of J. Demmel, UC Berkeley.
Scientific Computing on Heterogeneous Clusters using DRUM (Dynamic Resource Utilization Model) Jamal Faik 1, J. D. Teresco 2, J. E. Flaherty 1, K. Devine.
Y. S. Yang and S. H. Hsieh National Taiwan University, Taipei, Taiwan December 8, 2000 FE2000: An Object-Oriented Framework For Parallel Nonlinear Dynamic.
SLAC is focusing on the modeling and simulation of DOE accelerators using high- performance computing The performance of high-brightness RF guns operating.
Wakefield Damping Effects in the CLIC Power Extraction and Transfer Structure (PETS) Wakefield Simulation of CLIC PETS Structure Using Parallel 3D Finite.
Department of Biomedical Informatics Dynamic Load Balancing (Repartitioning) & Matrix Partitioning Ümit V. Çatalyürek Associate Professor Department of.
Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energy’s National Nuclear.
Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energy’s National Nuclear.
Infrastructure for Parallel Adaptive Unstructured Mesh Simulations
Combinatorial Scientific Computing is concerned with the development, analysis and utilization of discrete algorithms in scientific and engineering applications.
Sandia National Laboratories Graph Partitioning Workshop Oct. 15, Load Balancing Myths, Fictions & Legends Bruce Hendrickson Sandia National Laboratories.
Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energy’s National Nuclear.
Trilinos: From a User’s Perspective Russell Hooper Nov. 7, 2007 SAND P Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed.
1 Presenters: Cameron W. Smith and Glen Hansen Workflow demonstration using Simmetrix/PUMI/PAALS for parallel adaptive simulations FASTMath SciDAC Institute.
Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energy’s National Nuclear.
High Performance Computing 1 Load-Balancing. High Performance Computing 1 Load-Balancing What is load-balancing? –Dividing up the total work between processes.
Strategic Goals: To align the many efforts at Sandia involved in developing software for the modeling and simulation of physical systems (mostly PDEs):
Collaborative Research into Exascale Systemware, Tools and Applications Gregor Matura, German Aerospace Center (DLR) Achim Basermann, Fang Chen, Markus.
After step 2, processors know who owns the data in their assumed partitions— now the assumed partition defines the rendezvous points Scalable Conceptual.
Automatic Differentiation: Introduction Automatic differentiation (AD) is a technology for transforming a subprogram that computes some function into a.
Positioning in Ad-Hoc Networks - Directions and Results Jan Beutel Computer Engineering and Networks Lab Swiss Federal Institute of Technology Zurich August.
Laser Energy Transport and Deposition Package for CRASH Fall 2011 Review Ben Torralva.
Supercomputing ‘99 Parallelization of a Dynamic Unstructured Application using Three Leading Paradigms Leonid Oliker NERSC Lawrence Berkeley National Laboratory.
Combinatorial Scientific Computing and Petascale Simulation (CSCAPES) A SciDAC Institute Funded by DOE’s Office of Science Investigators Alex Pothen, Florin.
Math /4.2/4.3 – Solving Systems of Linear Equations 1.
1 1 What does Performance Across the Software Stack mean?  High level view: Providing performance for physics simulations meaningful to applications 
Interoperable Mesh Tools for Petascale Applications Lori Diachin, LLNL Representing the ITAPS Team.
Adaptive Meshing Control to Improve Petascale Compass Simulations Xiao-Juan Luo and Mark S Shephard Scientific Computation Research Center (SCOREC) Interoperable.
Enabling Self-management of Component-based High-performance Scientific Applications Hua (Maria) Liu and Manish Parashar The Applied Software Systems Laboratory.
Domain Decomposition in High-Level Parallelizaton of PDE codes Xing Cai University of Oslo.
CS 484 Load Balancing. Goal: All processors working all the time Efficiency of 1 Distribute the load (work) to meet the goal Two types of load balancing.
1 1  Capabilities: Serial (C), shared-memory (OpenMP or Pthreads), distributed-memory (hybrid MPI+ OpenM + CUDA). All have Fortran interface. Sparse LU.
Generic GUI – Thoughts to Share Jinping Gwo EMSGi.org.
Data Structures and Algorithms in Parallel Computing Lecture 7.
COMPASS All-Hands Meeting, FNAL, Sept , 2007 Accelerator Prototyping Through Multi-physics Analysis Volkan Akcelik, Lie-Quan Lee, Ernesto Prudencio,
1 1  Capabilities: PCU: Communication, threading, and File IO built on MPI APF: Abstract definition of meshes, fields, and their algorithms GMI: Interface.
C OMPUTATIONAL R ESEARCH D IVISION 1 Defining Software Requirements for Scientific Computing Phillip Colella Applied Numerical Algorithms Group Lawrence.
Predictive Load Balancing Using Mesh Adjacencies for Mesh Adaptation  Cameron Smith, Onkar Sahni, Mark S. Shephard  Scientific Computation Research Center.
1 1 Zoltan: Toolkit of parallel combinatorial algorithms for unstructured, dynamic and/or adaptive computations Unstructured Communication Tools -Communication.
Dynamic Load Balancing in Scientific Simulation
Hybrid Parallel Implementation of The DG Method Advanced Computing Department/ CAAM 03/03/2016 N. Chaabane, B. Riviere, H. Calandra, M. Sekachev, S. Hamlaoui.
CSCAPES Mission Research and development Provide load balancing and parallelization toolkits for petascale computation Develop advanced automatic differentiation.
High Performance Computing Seminar II Parallel mesh partitioning with ParMETIS Parallel iterative solvers with Hypre M.Sc. Caroline Mendonça Costa.
Unstructured Meshing Tools for Fusion Plasma Simulations
Parallel Hypergraph Partitioning for Scientific Computing
Computational Techniques for Efficient Carbon Nanotube Simulation
Xing Cai University of Oslo
Challenges in Electromagnetic Modeling Scalable Solvers
3.3 – Solving Systems of Inequalities by Graphing
Parallel Unstructured Mesh Infrastructure
Linear Systems November 28, 2016.
Construction of Parallel Adaptive Simulation Loops
Parallel 3D Finite Element Particle-In-Cell Simulations with Pic3P*
L Ge, L Lee, A. Candel, C Ng, K Ko, SLAC
Solve a system of linear equation in two variables
Wakefield Simulation of CLIC PETS Structure Using Parallel 3D Finite Element Time-Domain Solver T3P* Arno Candel, Andreas Kabel, Zenghai Li, Cho Ng, Liequan.
UEDGE Points of Investigation: Needs of a scalable preconditioner
Final Review.
5.1 Solving Systems of Equations by Graphing
Computational Techniques for Efficient Carbon Nanotube Simulation
Systems of Equations Solve by Graphing.
Ph.D. Thesis Numerical Solution of PDEs and Their Object-oriented Parallel Implementations Xing Cai October 26, 1998.
Presentation transcript:

1 1  Capabilities: Dynamic load balancing and static data partitioning -Geometric, graph-based, hypergraph-based -Interfaces to ParMETIS, PT-Scotch, PaToH Graph coloring Graph/matrix fill-reducing or locality-preserving ordering iZoltan interface supports ITAPS mesh interfaces  Download via the Trilinos toolkit:  Further information: Karen Devine, Erik Boman, Siva Rajamanickam, Zoltan Combinatorial Scientific Computing Toolkit

2 2 Example: LCLS RF gun, colors indicate distribution to different CPUs. Fields are computed only in causal region, using p-refinement. (Courtesy SLAC National Accelerator Laboratory.) Particle Partitioning Field Partitioning  SLAC’s Pic3P accelerator simulation solves Maxwell’s equations Field computation on fixed mesh Particles moving through domain  Load balance using two different data decompositions Fields partitioned with graph-based methods (ParMETIS) Particles partitioned geometrically (Zoltan RCB 3D)  Enables scalable solution of larger problems: 24k CPUs, 750M DOFs, 5B particles Zoltan example: Partitioning for Particle-in-Cell methods

3 3 Zoltan’s Data-Structure Neutral Design Supports a Wide Range of Applications and Data Structures. Multiphysics simulations Adaptive mesh refinement Crash simulations Particle methods Parallel electronics networks 1 2 Vs SOURCE_VOLTAGE 1 2 Rs R 1 2 Cm012 C 1 2 Rg02 R 1 2 Rg01 R 1 2 C01 C 1 2 C02 C 12 L2 INDUCTOR 12 L1 INDUCTOR 12 R1 R 12 R2 R 1 2 Rl R 1 2 Rg1 R 1 2 Rg2 R 1 2 C2 C 1 2 C1 C 1 2 Cm12 C Linear solvers & preconditioners