Electronic visualization laboratory, university of illinois at chicago Visualizing Very Large Scale Earthquake Simulations (SC 2003) K.L.Ma, UC-Davis.

Slides:

Advertisements

Similar presentations

Parallel Visualization Kenneth Moreland Sandia National Laboratories Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin.

Advertisements

Parallel Volume Rendering for Ocean Visualization in a Cluster of PCs Alexandre Coelho Marcio Nascimento Cristiana Bentes Maria Clicia S. de Castro Ricardo.

SC05 Time-Varying Visualization Workshop Toward Effective Visualization of Ultra-scale Time-Varying Data Han-Wei Shen Associate Professor The Ohio State.

Partitioning Screen Space for Parallel Rendering

A NOVEL APPROACH TO SOLVING LARGE-SCALE LINEAR SYSTEMS Ken Habgood, Itamar Arel Department of Electrical Engineering & Computer Science GABRIEL CRAMER.

Ray Tracing CMSC 635. Basic idea How many intersections?  Pixels  ~10 3 to ~10 7  Rays per Pixel  1 to ~10  Primitives  ~10 to ~10 7  Every ray.

A Coherent Grid Traversal Algorithm for Volume Rendering Ioannis Makris Supervisors: Philipp Slusallek*, Céline Loscos *Computer Graphics Lab, Universität.

Fast Volume Rendering Using a Shear-Warp Factorization of the Viewing Transformation Philippe Larcoute & Marc Levoy Stanford University Published in SIGGRAPH.

Distributed Breadth-First Search with 2-D Partitioning Edmond Chow, Keith Henderson, Andy Yoo Lawrence Livermore National Laboratory LLNL Technical report.

Hank Childs, University of Oregon November 15 th, 2013 Volume Rendering, Part 2.

 Understanding the Sources of Inefficiency in General-Purpose Chips.

Parallelizing stencil computations Based on slides from David Culler, Jim Demmel, Bob Lucas, Horst Simon, Kathy Yelick, et al., UCB CS267.

University of Illinois at Chicago Electronic Visualization Laboratory (EVL) Image Based Rendering with Stable Frame Rates by Qu, et al. IEEE 2000 presentation.

Efficient Parallelization for AMR MHD Multiphysics Calculations Implementation in AstroBEAR Collaborators: Adam Frank Brandon Shroyer Chen Ding Shule Li.

Rasterization and Ray Tracing in Real-Time Applications (Games) Andrew Graff.

Distributed Interactive Ray Tracing for Large Volume Visualization Dave DeMarle Steven Parker Mark Hartner Christiaan Gribble Charles Hansen.

Memory-Savvy Distributed Interactive Ray Tracing David E. DeMarle Christiaan Gribble Steven Parker.

Towards Data Partitioning for Parallel Computing on Three Interconnected Clusters Brett A. Becker and Alexey Lastovetsky Heterogeneous Computing Laboratory.

1 Lecture 26: Storage Systems Topics: Storage Systems (Chapter 6), other innovations Final exam stats:  Highest: 95  Mean: 70, Median: 73  Toughest.

1 Image-Based Visual Hulls Paper by Wojciech Matusik, Chris Buehler, Ramesh Raskar, Steven J. Gortler and Leonard McMillan [

Introduction to Volume Visualization Mengxia Zhu Fall 2007.

High Performance Computing 1 Parallelization Strategies and Load Balancing Some material borrowed from lectures of J. Demmel, UC Berkeley.

Efficient Parallelization for AMR MHD Multiphysics Calculations Implementation in AstroBEAR.

Introduction to Parallel Rendering: Sorting, Chromium, and MPI Mengxia Zhu Spring 2006.

Parallel Rendering Ed Angel

Sorting and cell compositing for irregular meshes Nelson Max, Peter Williams, Richard Cook, Claudio Silva.

Parallel Programming: Case Studies Todd C. Mowry CS 495 September 12, 2002.

A Multiresolution Volume Rendering Framework for Large-Scale Time- Varying Data Visualization Chaoli Wang 1, Jinzhu Gao 2, Liya Li 1, Han-Wei Shen 1 1.

Sort-Last Parallel Rendering for Viewing Extremely Large Data Sets on Tile Displays Paper by Kenneth Moreland, Brian Wylie, and Constantine Pavlakos Presented.

Computer Graphics 2 Lecture x: Acceleration Techniques for Ray-Tracing Benjamin Mora 1 University of Wales Swansea Dr. Benjamin Mora.

Load Balancing Dan Priece. What is Load Balancing? Distributed computing with multiple resources Need some way to distribute workload Discreet from the.

Electronic Visualization Laboratory University of Illinois at Chicago “Sort-First, Distributed Memory Parallel Visualization and Rendering” by E. Wes Bethel,

Parallel Volume Rendering Using Binary-Swap Image Composition Presented by Jin Ding Spring 2002 Visualization and Advanced Computer Graphics.

Technology and Historical Overview. Introduction to 3d Computer Graphics  3D computer graphics is the science, study, and method of projecting a mathematical.

Parallel Rendering 1. 2 Introduction In many situations, standard rendering pipeline not sufficient Need higher resolution display More primitives than.

Scalable Algorithms for Structured Adaptive Mesh Refinement Akhil Langer, Jonathan Lifflander, Phil Miller, Laxmikant Kale Parallel Programming Laboratory.

Efficient Volume Visualization of Large Medical Datasets Stefan Bruckner Institute of Computer Graphics and Algorithms Vienna University of Technology.

Computational issues in Carbon nanotube simulation Ashok Srinivasan Department of Computer Science Florida State University.

So far we have covered … Basic visualization algorithms Parallel polygon rendering Occlusion culling They all indirectly or directly help understanding.

Parallel Rendering. 2 Introduction In many situations, a standard rendering pipeline might not be sufficient Need higher resolution display More primitives.

Balajee Vamanan and T. N. Vijaykumar School of Electrical & Computer Engineering CoNEXT 2011.

Copyright © 2009 Pearson Education, Inc. Publishing as Pearson Addison-Wesley 5-1 Two allocations of a 16X16 array to 16 processes: (a) 2-dimensional blocks;

CS 584. 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.

Author: B. C. Bromley Presented by: Shuaiyuan Zhou Quasi-random Number Generators for Parallel Monte Carlo Algorithms.

More on Adaptivity in Grids Sathish S. Vadhiyar Source/Credits: Figures from the referenced papers.

Rassul Ayani 1 Performance of parallel and distributed systems  What is the purpose of measurement?  To evaluate a system (or an architecture)  To compare.

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.

Hank Childs, University of Oregon Large Data Visualization.

An Evaluation of Partitioners for Parallel SAMR Applications Sumir Chandra & Manish Parashar ECE Dept., Rutgers University Submitted to: Euro-Par 2001.

Greg Humphreys CS445: Intro Graphics University of Virginia, Fall D Object Representations Greg Humphreys University of Virginia CS 445, Fall 2003.

3/16/04James R. McGirr1 Interactive Rendering of Large Volume Data Sets Written By : Stefan Guthe Michael Wand Julius Gonser Wolfgang Straβer University.

Electronic Visualization Laboratory University of Illinois at Chicago “Time-Critical Multiresolution Volume Rendering using 3D Texture Mapping Hardware”

Unstructured Volume Rendering. Grid Types uniformrectilinearregularcurvilinear Structured Grids: regularirregularhybridcurved Unstructured Grids:

An interleaved Parallel Volume Renderer with PC-clusters Antonio Garcia and Han-Wei Shen Dept of Computer Science Ohio State University Eurographics Workshop.

COMP7330/7336 Advanced Parallel and Distributed Computing Task Partitioning Dynamic Mapping Dr. Xiao Qin Auburn University

Kriging for Estimation of Mineral Resources GISELA/EPIKH School Exequiel Sepúlveda Department of Mining Engineering, University of Chile, Chile ALGES Laboratory,

Electronic visualization laboratory, university of illinois at chicago Sort Last Parallel Rendering for Viewing Extremely Large Data Sets on Tile Displays.

Motion tracking TEAM D, Project 11: Laura Gui - Timisoara Calin Garboni - Timisoara Peter Horvath - Szeged Peter Kovacs - Debrecen.

Veysi ISLER, Department of Computer Engineering, Middle East Technical University, Ankara, TURKEY Spring

Massively Parallel Cosmological Simulations with ChaNGa Pritish Jetley, Filippo Gioachin, Celso Mendes, Laxmikant V. Kale and Thomas Quinn.

Electronic Visualization Laboratory University of Illinois at Chicago “Fast And Reliable Space Leaping For Interactive Volume Rendering” by Ming Wan, Aamir.

National Center for Supercomputing Applications University of Illinois at Urbana-Champaign Recent TeraGrid Visualization Support Projects at NCSA Dave.

GPU Architecture and Its Application

Visualization of Scanned Cave Data with Global Illumination

So far we have covered … Basic visualization algorithms

3D Object Representations

Dongkeun Oh Sanghamitra Roy

Physics-based simulation for visual computing applications

Michael Cox, David Ellsworth, NASA Ames Research Center

Survey of Parallel Volume Rendering Algorithms

Presentation transcript:

electronic visualization laboratory, university of illinois at chicago Visualizing Very Large Scale Earthquake Simulations (SC 2003) K.L.Ma, UC-Davis

electronic visualization laboratory, university of illinois at chicago Summary A parallel rendering algorithm is presented to visualize a 3D seismic propagation (time varying data) of Northridge earthquake (highest resolution vol viz of an earthquake simulation to date) Issues dealt with: –Large data –Time varying data –Unstructured grid

electronic visualization laboratory, university of illinois at chicago Main strategies used Interleaving load distribution Overlapping communication and computation Avoiding per time step processing Buffering intermediate results to amortize communication overheads Compression A parallel cell projection algorithm is used which requires no connectivity between adjacent cells unlike ray tracing (for visualizing unstructured data)

electronic visualization laboratory, university of illinois at chicago Parallel Rendering An octree based representation of the volume is used at multiple resolutions The appropriate data resolution is used to match image resolution Projection data is scattered to the nodes to eliminate nodes that are not viewed The data block size is made coarse enough for faster traversal The loading of blocks from disk and rendering are overlapped.

electronic visualization laboratory, university of illinois at chicago Parallel Image Composition SLIC (Scheduled Linear Image Compositing) Pixels are classified as background (ignored), non- overlapping (sent directly to final processor) and 1, 2, 3 overlapping etc (figure 4) Compositing is scheduled according to overlap The overlapping calculation has to be redone when view point changes (scheduling for composition is also recalculated)

electronic visualization laboratory, university of illinois at chicago Results, References Test results should low compositing cost in most cases but after n=32, the parallel algorithm is inefficient due to load imbalance Papers to look at: –survey of visualization of time varying data –binary swap –communication costs of parallel volume rendering algorithms –SLIC