Interactive Visualization of Large Graphs and Networks
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Domain-Specific Programming Systems
Lecture 22: Domain-Specific Programming Systems Parallel Computer Architecture and Programming CMU 15-418/15-618, Spring 2020 Slide acknowledgments: Pat Hanrahan, Zach Devito (Stanford University) Jonathan Ragan-Kelley (MIT, Berkeley) Course themes: Designing computer systems that scale (running faster given more resources) Designing computer systems that are efficient (running faster under constraints on resources) Techniques discussed: Exploiting parallelism in applications Exploiting locality in applications Leveraging hardware specialization (earlier lecture) CMU 15-418/618, Spring 2020 Claim: most software uses modern hardware resources inefficiently ▪ Consider a piece of sequential C code - Call the performance of this code our “baseline performance” ▪ Well-written sequential C code: ~ 5-10x faster ▪ Assembly language program: maybe another small constant factor faster ▪ Java, Python, PHP, etc. ?? Credit: Pat Hanrahan CMU 15-418/618, Spring 2020 Code performance: relative to C (single core) GCC -O3 (no manual vector optimizations) 51 40/57/53 47 44/114x 40 = NBody 35 = Mandlebrot = Tree Alloc/Delloc 30 = Power method (compute eigenvalue) 25 20 15 10 5 Slowdown (Compared to C++) Slowdown (Compared no data no 0 data no Java Scala C# Haskell Go Javascript Lua PHP Python 3 Ruby (Mono) (V8) (JRuby) Data from: The Computer Language Benchmarks Game: CMU 15-418/618, http://shootout.alioth.debian.org Spring 2020 Even good C code is inefficient Recall Assignment 1’s Mandelbrot program Consider execution on a high-end laptop: quad-core, Intel Core i7, AVX instructions... Single core, with AVX vector instructions: 5.8x speedup over C implementation Multi-core + hyper-threading + AVX instructions: 21.7x speedup Conclusion: basic C implementation compiled with -O3 leaves a lot of performance on the table CMU 15-418/618, Spring 2020 Making efficient use of modern machines is challenging (proof by assignments 2, 3, and 4) In our assignments, you only programmed homogeneous parallel computers. -
The Intensity Powerwall: a SAN Performance Case Study
The InTENsity PowerWall: A SAN Performance Case Study Presented by Alex Elder Tricord Systems, Inc. [email protected] Joint NASA/IEEE Conference On Mass Storage Systems March 28, 2000 UofMN LCSE Talk Outline The LCSE Introduction InTENsity Applications Performance Testing Lessons Learned Future Work UofMN LCSE Laboratory for Computational Science and Engineering (LCSE) Part of University of Minnesota Institute of Technology Funded primarily by NSF/NCSA and DoE/ASCI Facility offers environment in which innovative hardware and software technologies can be tested and applied Broad mandate to develop innovative high performance computing technologies and capabilities History of Collaboration with Industrial Partners (in Alphabetical Order) ADIC/MountainGate, Ancor, Brocade, Ciprico, Qlogic, Seagate, Vixel Areas of focus include CFD, Shared File System Research, Distributed Shared Memory UofMN LCSE The InTENsity PowerWall What is the InTENsity PowerWall? Display Component Computing Environments Irix NT/Linux Cluster Storage Area Network UofMN LCSE What is the InTENsity PowerWall? Display system used for visualization of large volumetric data sets Very high resolution, for detailed display Very high performance–displays images at rates that allow for “movies” of data Driven by two computing environments with common shared storage UofMN LCSE InTENsity Design Requirements Very high resolution–beyond 10 million pixels Physically large, semi-immersive format Rear-projection display technology Smooth frame rate (over 15 frames per second) -
Preparing for an Installation of a 33 to 256 Processor System HR-04122-0B Origin ™ Systems Last Modified: August 1999
Preparing for an Installation of a 33 to 256 Processor System HR-04122-0B Origin ™ Systems Last Modified: August 1999 Record of Revision . 4 Overview . 5 System Components and Configurations . 6 Equipment Separation Limits . 8 Site Requirements . 10 Planning Your Access Route . 10 Environmental Requirements . 14 Facility Power Requirements . 15 Remote Support . 20 Network Connections . 20 Raised-floor Installations . 21 Securing the Cabinets . 25 Physical Specifications . 28 Origin 2000 Systems . 34 Onyx2 InfiniteReality2 Systems . 38 Onyx2 InfiniteReality2 Rack System . 38 Onyx2 InfiniteReality Multirack Systems . 38 Onyx2 InfiniteReality2 Multirack System Layout Options . 41 SCSI RAID Rack . 42 Origin Fibre Channel Rack . 43 O2 Workstation . 44 Site Planning Checklist . 46 Summary . 48 HR-04122-0B SGI Proprietary 1 Preparing for an Installation Figures Figure 1. Origin 2000 128- and 256-Processor Multirack Systems: Standard and Optional Floor Layouts Placed on 24 in. x 24 in. Floor Panels . 7 Figure 2. Distance between Racks (Standard Layout) . 8 Figure 3. Separation Limits . 9 Figure 4. Origin 2000 Rack, Onyx2 InfiniteReality2 Rack, and MetaRouter Shipping Configuration . 11 Figure 5. SCSI RAID Rack Shipping Configuration . 12 Figure 6. Origin Fibre Channel Rack Shipping Configuration . 13 Figure 7. Origin 2000 Rack and Onyx2 InfiniteReality2 Rack Floor Cutout 22 Figure 8. MetaRouter Floor Cutout . 23 Figure 9. SCSI RAID Rack Floor Cutout . 24 Figure 10. Origin Fibre Channel Rack Floor Cutout . 24 Figure 11. Securing the Origin 2000 Rack and Onyx2 Rack . 25 Figure 12. Securing the MetaRouter . 26 Figure 13. Securing the Origin Fibre Channel Rack . 27 Figure 14. Origin 2000 Rack . 35 Figure 15. MetaRouter . 36 Figure 16. -
Jahresbericht 2007 Des Leibniz-Rechenzentrums I
Leibniz-Rechenzentrum der Bayerischen Akademie der Wissenschaften Jahresbericht 2007 März 2008 LRZ-Bericht 2008-01 Direktorium: Leibniz-Rechenzentrum Boltzmannstraße 1 Telefon: (089) 35831-8784 Öffentliche Verkehrsmittel: Prof. Dr. H.-G. Hegering (Vorsitzender) 85748 Garching Telefax: (089) 35831-9700 Prof. Dr. A. Bode E-Mail: [email protected] U6: Garching-Forschungszentrum Prof. Dr. Chr. Zenger UST-ID-Nr. DE811305931 Internet: http://www.lrz.de Jahresbericht 2007 des Leibniz-Rechenzentrums i Vorwort ........................................................................................................................................ 6 Teil I Das LRZ, Entwicklungsstand zum Jahresende 2007 .............................................. 11 1 Einordnung und Aufgaben des Leibniz-Rechenzentrums (LRZ) ............................................ 11 2 Das Dienstleistungsangebot des LRZ .......................................................................................... 15 2.1 Dokumentation, Beratung, Kurse ......................................................................................... 15 2.1.1 Dokumentation ........................................................................................................ 15 2.1.2 Beratung und Unterstützung .................................................................................... 15 2.1.3 Kurse, Veranstaltungen ........................................................................................... 17 2.2 Planung und Bereitstellung des Kommunikationsnetzes .................................................... -
41St CUG Conference Final Program Welcome
MAY 24-28, 1999 • MINNEAPOLIS, MN 41st CUG Conference Final Program Welcome Welcome to the 1999 CUG Conference! is a beautiful time of year here, and enthusiasm runs high as people begin to enjoy the outdoors after a long northern winter. From the bluffs of the mighty Mississippi River in We are excited to welcome everyone to Minneapolis for the the east, to the scenic shores of one of the largest fresh water 1999 CUG Conference. As the original home of Cray lakes in the world (Lake Superior), to the farms and prairies Research, it seems a fitting place to close out the millennium of the west, Minnesota provides a diverse offering for visi- and look ahead to the next. We are indeed at the end of an tors. Whether you like outdoor activities and sports, a quiet era, as the supercomputing paradigm has shifted from the respite in the northern woods, or just some time to see the "big iron" to an HPC world of high-performance commodity sights in the Twin Cities of Minneapolis and St. Paul, we processors. hope your stay will be memorable. The CUG Program Committee has planned an exciting week Minnesota is known for friendly but reserved people, who of technical sessions and discussions. Add to that good food, often are masters of understatement. To use the words of a grand night out in the elegant Minnesota History Center, Garrison Keillor, well-known Minnesota humorist and host an evening reception hosted by SGI, a special luncheon for of national public radio's "A Prairie Home Companion," we CUG newcomers, and a chance to meet with your colleagues think this conference will be "not too bad," which, translated in the comfortable setting of the downtown Minneapolis from understated Minnesota parlance, means it will be Marriott City Center Hotel. -
The Mission of IUPUI Is to Provide for Its Constituents Excellence in Teaching and Learning; Research, Scholarship, and Creative Activity; and Civic Engagement
INFO H517 Visualization Design, Analysis, and Evaluation Department of Human-Centered Computing Indiana University School of Informatics and Computing, Indianapolis Fall 2016 Section No.: 35557 Credit Hours: 3 Time: Wednesdays 12:00 – 2:40 PM Location: IT 257, Informatics & Communications Technology Complex 535 West Michigan Street, Indianapolis, IN 46202 [map] First Class: August 24, 2016 Website: http://vis.ninja/teaching/h590/ Instructor: Khairi Reda, Ph.D. in Computer Science (University of Illinois, Chicago) Assistant Professor, Human–Centered Computing Office Hours: Mondays, 1:00-2:30PM, or by Appointment Office: IT 581, Informatics & Communications Technology Complex 535 West Michigan Street, Indianapolis, IN 46202 [map] Phone: (317) 274-5788 (Office) Email: [email protected] Website: http://vis.ninja/ Prerequisites: Prior programming experience in a high-level language (e.g., Java, JavaScript, Python, C/C++, C#). COURSE DESCRIPTION This is an introductory course in design and evaluation of interactive visualizations for data analysis. Topics include human visual perception, visualization design, interaction techniques, and evaluation methods. Students develop projects to create their own web- based visualizations and develop competence to undertake independent research in visualization and visual analytics. EXTENDED COURSE DESCRIPTION This course introduces students to interactive data visualization from a human-centered perspective. Students learn how to apply principles from perceptual psychology, cognitive science, and graphics design to create effective visualizations for a variety of data types and analytical tasks. Topics include fundamentals of human visual perception and cognition, 1 2 graphical data encoding, visual representations (including statistical plots, maps, graphs, small-multiples), task abstraction, interaction techniques, data analysis methods (e.g., clustering and dimensionality reduction), and evaluation methods. -
Computational Photography
Computational Photography George Legrady © 2020 Experimental Visualization Lab Media Arts & Technology University of California, Santa Barbara Definition of Computational Photography • An R & D development in the mid 2000s • Computational photography refers to digital image-capture cameras where computers are integrated into the image-capture process within the camera • Examples of computational photography results include in-camera computation of digital panoramas,[6] high-dynamic-range images, light- field cameras, and other unconventional optics • Correlating one’s image with others through the internet (Photosynth) • Sensing in other electromagnetic spectrum • 3 Leading labs: Marc Levoy, Stanford (LightField, FrankenCamera, etc.) Ramesh Raskar, Camera Culture, MIT Media Lab Shree Nayar, CV Lab, Columbia University https://en.wikipedia.org/wiki/Computational_photography Definition of Computational Photography Sensor System Opticcs Software Processing Image From Shree K. Nayar, Columbia University From Shree K. Nayar, Columbia University Shree K. Nayar, Director (lab description video) • Computer Vision Laboratory • Lab focuses on vision sensors; physics based models for vision; algorithms for scene interpretation • Digital imaging, machine vision, robotics, human- machine interfaces, computer graphics and displays [URL] From Shree K. Nayar, Columbia University • Refraction (lens / dioptrics) and reflection (mirror / catoptrics) are combined in an optical system, (Used in search lights, headlamps, optical telescopes, microscopes, and telephoto lenses.) • Catadioptric Camera for 360 degree Imaging • Reflector | Recorded Image | Unwrapped [dance video] • 360 degree cameras [Various projects] Marc Levoy, Computer Science Lab, Stanford George Legrady Director, Experimental Visualization Lab Media Arts & Technology University of California, Santa Barbara http://vislab.ucsb.edu http://georgelegrady.com Marc Levoy, Computer Science Lab, Stanford • Light fields were introduced into computer graphics in 1996 by Marc Levoy and Pat Hanrahan. -
Surfacing Visualization Mirages
Surfacing Visualization Mirages Andrew McNutt Gordon Kindlmann Michael Correll University of Chicago University of Chicago Tableau Research Chicago, IL Chicago, IL Seattle, WA [email protected] [email protected] [email protected] ABSTRACT In this paper, we present a conceptual model of these visual- Dirty data and deceptive design practices can undermine, in- ization mirages and show how users’ choices can cause errors vert, or invalidate the purported messages of charts and graphs. in all stages of the visual analytics (VA) process that can lead These failures can arise silently: a conclusion derived from to untrue or unwarranted conclusions from data. Using our a particular visualization may look plausible unless the an- model we observe a gap in automatic techniques for validating alyst looks closer and discovers an issue with the backing visualizations, specifically in the relationship between data data, visual specification, or their own assumptions. We term and chart specification. We address this gap by developing a such silent but significant failures visualization mirages. We theory of metamorphic testing for visualization which synthe- describe a conceptual model of mirages and show how they sizes prior work on metamorphic testing [92] and algebraic can be generated at every stage of the visual analytics process. visualization errors [54]. Through this combination we seek to We adapt a methodology from software testing, metamorphic alert viewers to situations where minor changes to the visual- testing, as a way of automatically surfacing potential mirages ization design or backing data have large (but illusory) effects at the visual encoding stage of analysis through modifications on the resulting visualization, or where potentially important to the underlying data and chart specification. -
Sgiconsole™ Hardware Connectivity Guide
SGIconsole™ Hardware Connectivity Guide Document Number 007-4340-001 Contributors Written by Francisco Razo Illustrated by Dan Young Production by Karen Jacobson Contributions by Jagdish Bhavsar, Michael T. Brown, Dick Brownell, Jason Chang, Steven Dean, Steve Ewing, Jim Friedl, Jim Grisham, Karen Johnson, Tony Kavadias, Paul Kinyon, Jenny Leung, Laraine MacKenzie, Philip Montalban, Rod Negus, Sonny Oh, Keith Rich, Laura Shepard, Paddy Sreenivasan, Rebecca Underwood, and Eric Zamost. COPYRIGHT © 2001 Silicon Graphics, Inc. All rights reserved; provided portions may be copyright in third parties, as indicated elsewhere herein. No permission is granted to copy, distribute, or create derivative works from the contents of this electronic documentation in any manner, in whole or in part, without the prior written permission of Silicon Graphics, Inc. LIMITED RIGHTS LEGEND The electronic (software) version of this document was developed at private expense; if acquired under an agreement with the USA government or any contractor thereto, it is acquired as "commercial computer software" subject to the provisions of its applicable license agreement, as specified in (a) 48 CFR 12.212 of the FAR; or, if acquired for Department of Defense units, (b) 48 CFR 227-7202 of the DoD FAR Supplement; or sections succeeding thereto. Contractor/manufacturer is Silicon Graphics, Inc., 1600 Amphitheatre Pkwy 2E, Mountain View, CA 94043-1351. TRADEMARKS AND ATTRIBUTIONS Indy, IRIS, IRIX, Onyx2, and Silicon Graphics are registered trademarks of Silicon Graphics, Inc. SGI, the SGI logo, IRISconsole, IRIS InSight, and SGIconsole are trademarks of Silicon Graphics, Inc. PostScript is a registered trademark of Adobe Systems, Inc. Linux is a registered trademark of Linus Torvalds. -
To Draw a Tree
To Draw a Tree Pat Hanrahan Computer Science Department Stanford University Motivation Hierarchies File systems and web sites Organization charts Categorical classifications Similiarity and clustering Branching processes Genealogy and lineages Phylogenetic trees Decision processes Indices or search trees Decision trees Tournaments Page 1 Tree Drawing Simple Tree Drawing Preorder or inorder traversal Page 2 Rheingold-Tilford Algorithm Information Visualization Page 3 Tree Representations Most Common … Page 4 Tournaments! Page 5 Second Most Common … Lineages Page 6 http://www.royal.gov.uk/history/trees.htm Page 7 Demonstration Saito-Sederberg Genealogy Viewer C. Elegans Cell Lineage [Sulston] Page 8 Page 9 Page 10 Page 11 Evolutionary Trees [Haeckel] Page 12 Page 13 [Agassiz, 1883] 1989 Page 14 Chapple and Garofolo, In Tufte [Furbringer] Page 15 [Simpson]] [Gould] Page 16 Tree of Life [Haeckel] [Tufte] Page 17 Janvier, 1812 “Graphical Excellence is nearly always multivariate” Edward Tufte Page 18 Phenograms to Cladograms GeneBase Page 19 http://www.gwu.edu/~clade/spiders/peet.htm Page 20 Page 21 The Shape of Trees Page 22 Patterns of Evolution Page 23 Hierachical Databases Stolte and Hanrahan, Polaris, InfoVis 2000 Page 24 Generalization • Aggregation • Simplification • Filtering Abstraction Hierarchies Datacubes Star and Snowflake Schemes Page 25 Memory & Code Cache misses for a procedure for 10 million cycles White = not run Grey = no misses Red = # misses y-dimension is source code x-dimension is cycles (time) Memory & Code zooming on y zooms from fileprocedurelineassembly code zooming on x increases time resolution down to one cycle per bar Page 26 Themes Cognitive Principles for Design Congruence Principle: The structure and content of the external representation should correspond to the desired structure and content of the internal representation. -
Using Visualization to Understand the Behavior of Computer Systems
USING VISUALIZATION TO UNDERSTAND THE BEHAVIOR OF COMPUTER SYSTEMS A DISSERTATION SUBMITTED TO THE DEPARTMENT OF COMPUTER SCIENCE AND THE COMMITTEE ON GRADUATE STUDIES OF STANFORD UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY Robert P. Bosch Jr. August 2001 c Copyright by Robert P. Bosch Jr. 2001 All Rights Reserved ii I certify that I have read this dissertation and that in my opinion it is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy. Dr. Mendel Rosenblum (Principal Advisor) I certify that I have read this dissertation and that in my opinion it is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy. Dr. Pat Hanrahan I certify that I have read this dissertation and that in my opinion it is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy. Dr. Mark Horowitz Approved for the University Committee on Graduate Studies: iii Abstract As computer systems continue to grow rapidly in both complexity and scale, developers need tools to help them understand the behavior and performance of these systems. While information visu- alization is a promising technique, most existing computer systems visualizations have focused on very specific problems and data sources, limiting their applicability. This dissertation introduces Rivet, a general-purpose environment for the development of com- puter systems visualizations. Rivet can be used for both real-time and post-mortem analyses of data from a wide variety of sources. The modular architecture of Rivet enables sophisticated visualiza- tions to be assembled using simple building blocks representing the data, the visual representations, and the mappings between them. -
Visual Analytics Application
Selecting a Visual Analytics Application AUTHORS: Professor Pat Hanrahan Stanford University CTO, Tableau Software Dr. Chris Stolte VP, Engineering Tableau Software Dr. Jock Mackinlay Director, Visual Analytics Tableau Software Name Inflation: Visual Analytics Visual analytics is becoming the fastest way for people to explore and understand data of any size. Many companies took notice when Gartner cited interactive data visualization as one of the top five trends transforming business intelligence. New conferences have emerged to promote research and best practices in the area, including VAST (Visual Analytics Science & Technology), organized by the 100,000 member IEEE. Technologies based on visual analytics have moved from research into widespread use in the last five years, driven by the increased power of analytical databases and computer hardware. The IT departments of leading companies are increasingly recognizing the need for a visual analytics standard. Not surprisingly, everywhere you look, software companies are adopting the terms “visual analytics” and “interactive data visualization.” Tools that do little more than produce charts and dashboards are now laying claim to the label. How can you tell the cleverly named from the genuine? What should you look for? It’s important to know the defining characteristics of visual analytics before you shop. This paper introduces you to the seven essential elements of true visual analytics applications. Figure 1: There are seven essential elements of a visual analytics application. Does a true visual analytics application also include standard analytical features like pivot-tables, dashboards and statistics? Of course—all good analytics applications do. But none of those features captures the essence of what visual analytics is bringing to the world’s leading companies.