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Semelhante a Big data visualization frameworks and applications at Kitware
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Big data visualization frameworks and applications at Kitware
- 1. Dr. Marcus D. Hanwell mhanwell@kitware.com @mhanwell www.kitware.com 27 March, 2014 South Bay Meetup Big Data Visualization Frameworks and Applications at Kitware !"
- 3. Kitware, Inc. • Founded in 1998 by five former GE Research employees • 118 current employees; 39 with PhDs • Privately held, profitable from creation, no debt • Rapidly Growing: >30% in 2011, 7M web-visitors/quarter • Offices – Clifton Park, NY – Carrboro, NC – Santa Fe, NM – Lyon, France • 2011 Small Business Administration’s Tibbetts Award • HPCWire Readers and Editor’s Choice • Inc’s 5000 List since 2008
- 4. Kitware’s customers & collaborators Over 75 academic institutions including! • Harvard • MIT • University of California, Berkeley • Stanford University • California Institute of Technology • Imperial College London • Johns Hopkins University • Cornell University • Columbia University • Robarts Research Institute • University of Pennsylvania • Rensselaer Polytechnic Institute • University of Utah • University of North Carolina Over 50 government agencies and labs including! • National Institutes of Health (NIH) • National Science Foundation (NSF) • National Library of Medicine (NLM) • Department of Defense (DOD) • Department of Energy (DOE) • Defense Advanced Research Projects Agency (DARPA) • Army Research Lab (ARL) • Air Force Research Lab (AFRL) • Sandia (SNL) • Los Alamos National Labs (LANL) • Argonne (ANL) • Oak Ridge (ORNL) • Lawrence Livermore (LLNL) Over 100 commercial companies in fields including! • Automotive • Aircraft • Defense • Energy technology • Environmental sciences • Finance • Industrial inspection • Oil & gas • Pharmaceuticals • Publishing • 3D Mapping • Medical devices • Security • Simulation
- 6. Business Model: Open Source • Open-source Software – Normally BSD-licensed – Collaboration platforms • Collaborative Research and Development • Technology Integration • Services and Support • Consulting • Training and webinars %"
- 8. What is “Big Data”? • We deal with two primary types – Small number of very large data elements • Computational fluid dynamics simulations • Cosmological simulations covering billions of years – Large number of (usually smaller) elements • Social media data, financial data, geospatial data • Over 3M compounds, 40M quantum calculations • Different types of data differ in structure • Very different strategies are needed! '"
- 9. Many Small Versus Few Big • Many small “records” – Major challenge lies in indexing, searching – Once found we can generally send to browser – Aggregation and/or summarization important • Few big “records” – Major challenge lies in data reduction – Must work hard to do all work near the data – Can still deliver reduced data to web clients ("
- 10. Considerations for Data at Scale • Key areas to be addressed: – Storage – Metadata extraction – Index – Search – Visualization – Interaction – Further calculations, simulations, etc. !)"
- 11. Data Storage at Scale • How much data do you have? • Must all data be stored in the same place? • Existing metadata extraction techniques? • Uniform data layout/schema? • Existing index/search techniques? – Algorithmic challenges – Open implementations that scale – Interaction with the database !!"
- 12. What Does a Result Look Like? • Once you are done searching: – What does a typical result look like? – How big is the resulting data? – How should the data be presented? – Is all data in the database referenced? • Is a simple ordered list useful? • What about multidimensional result sets? !#"
- 13. Challenges with Big Data • Storage for petabytes of data is tough – Moving it is even harder – Extracting metadata is a challenge – Backing up and restoring isn’t any easier – Even individual results can be very large • Mostly done in central facilities – Specialized file systems – Power, backup, redundancy, staff !*"
- 14. !+" Frameworks
- 15. The Visualization Toolkit (VTK) • Collection of C++ libraries – Leveraged by many applications – Divided into logical areas, e.g. • Filtering – data processing in visualization pipeline • InfoVis – informatics visualization • Widgets – 3D interaction widgets • VolumeRendering – 3D volume rendering • Cross platform, using OpenGL • Wrapped in Python, Tcl and Java http://www.vtk.org/
- 16. Visualization
- 17. VTK Architecture • Hybrid approach – Compiled C++ core (faster algorithms) – Interpreted applications (rapid development) – Interpreted layer generated automatically C++ core Interpreter
- 18. The Visualization Pipeline • A sequence of algorithms that operate on data objects to generate geometry Source Data Data Filter Filter Data Data Mapper Mapper Actor Actor Render on screen
- 19. ParaView • Parallel visualization application • Open source, BSD licensed • Turn-key application wrapper around VTK • Parallel data processing and rendering http://www.paraview.org/
- 20. ParaView is for Extremely Large Data 1 billion cell asteroid detonation simulation ! billion cell weather simulation source: Sandia National Lab
- 21. ,-./-0" 1234250" 6784-"92:" ,-./-0" 1234250" 6784-"92:" ;<=" >?@""A9" >?@"A9" @"B2CD23-34"E.4." <.0.FF-F8GC"H20">"A9I4-" J" ,-3/-0"K-0L-0" ,-3/-0"K-0L-0" ,-3/-0"K-0L-0" ,-3/-0"K-0L-0" 1F8-34"E.4."K-0L-0" E.4."K-0L-0" E.4."K-0L-0" E.4."K-0L-0" E.4."K-0L-0" E.4."K-0L-0" Depth Composite Tile Display Control, Display and Rendering of Small Data
- 22. • Python web framework built on CherryPy • Flexible HTML5 web server architecture • Developed with a clean separation – Application in HTML, JavaScript, CSS – Service in pure Python (+ wrapped C/C++) • Packages several other frameworks too – Bootstrap, D3, Vega, MongoDB • Making web apps easier to develop/deploy ##"http://tangelo.kitware.com/
- 23. • Python for server side, native web clients • Easily add new services (single .py file) – Use RESTful API – JSON delivery of data – Full power of Python • Rapid prototyping #*" Browser Tangelo web service “foo” index.html index.js styles.css foo.py
- 24. ParaViewWeb – Web Enabled • Bring 3D visualization to a web page – Targeting HPC web portal – Simple usage with basic/rigid workflow – Framework to develop 3D web applications – Must work now (no WebGL) – Support collaboration with multiple clients sharing the same visualization • The goal was NOT to – Redo another generic ParaView client #+
- 25. Tangelo Powering ParaViewWeb • We need a web front end to – Start processes – Forward communications #$
- 27. Visualizing Flickr Metadata • Uses Google maps • Flickr data in MongoDB • Python service retrieves data using PyMongo • D3 layer over maps – Geolocation – Day of the week – Photo (mouse hover) #&"
- 28. Enron Email Network Visualization • enron.py retrieves emails – Computes graph structure • D3 force layout for viz • Controls to: – Slice email by time – Change email originator – Set number of hops • Tool targeted at investigating social network behavior #'"
- 29. Bitcoin Analysis • Uses bitcoin blockchain – Individual transactions • Intensity histogram with transaction volume in date/amount ranges • Detail plot with individual transactions • Anomaly search – Theft detection • Study large scale behavior over time #("
- 31. Informatics Software Stack *!" MNO" PD-3M8-Q" <.0.M8-Q6-R" M8G2C8BG" SN;T?U.L.GB08D4" E*?M-V." 6-R"WDDG" E-GX42D"WDDG" S2GD84.F" 12G4G" YF8BX0" 17.084I@-4" <I4723" N.3V-F2" ," ;.4F.R" @TNO" S./22D" ;23V2" =CD.F." KZT" 1KM? UKP@" W3.FIG8G"W/.D4-0G" E.4."W/.D4-0G" J" J"
- 32. Digital Pathology • MongoDB used for image tiles – Store once, using multiple times – Metadata, processing status, results – Browser-based application/interaction *#" https://slide-atlas.org/
- 33. Arbor is an NSF-funded project to enable evolutionary biological research by making it easy for biologists to • create, • test, • and visualize algorithms on the Tree of Life. Below is the evolutionary tree for Heliconia (Lobster Claw) plants coupled to a character matrix of observational data such as color, feature measurements, and range.
- 34. Cosmology Data Management *+" Supercomputer DISC LS ST K8C5F.[23" 12GC2N22FG" Y0.C-Q20X" !"#"$%&#' K8C5F.[23" =3D54"/-BX" 12GC2N22FG" 123V50.[23" (")"*+,-' .,)/,)' (")"*+,-' !$+,0#' <.0.M8-Q6-R" 1,2'3)4-&,)' K50L-IG" Advanced User/Developer/ Scientist E.4."=34-3G8L-" KB.F.RF-"12CD]" Database Scientist Experimentalist Database
- 35. *$" $+2!4&54644$&7"' Voronoi Tesselation FOF HaloFinder Stream Counter CosmoTools ParaView Plugins Caustics • ANL: Salman Habib, Katrin Heitmann, Tom Peterka, Adrian Pope, Hal Finkel • LANL: Jim Ahrens, Jon Woodring, Pat Fasel • Kitware: George Zagaris, Berk Geveci, Casey Goodlett, Zach Mullen
- 36. UV-CDAT for Climate Visualization • Ultrascale Visualization and Climate Data Analysis Toolkit – Collaborative effort led by LLNL – Integrate DOE’s climate modeling/measures • Integrates a large number of tools/libs – CDAT, VTK, R, ParaView, DV3D • Current data sets at about 3.5 petabytes – Growing to 350 petabytes to ~3 exabytes *%"
- 39. Applications Being Developed • Three independent applications • Communication handled with local sockets • Avogadro 2: Structure editing, input generation, output viewing, and analysis • MoleQueue: Running local and remote jobs in standalone programs, and management • MongoChem: Storage of data, searching, entry, and annotation • Supporting frameworks (AvogadroLibs & VTK) *("http://www.openchemistry.org/
- 40. Use Cases for Open Chemistry • Researchers interested in molecules – Various sources of starting structure • Perform studies using various codes – Some performed locally – Others using high-performance computing – Different calculations produce different data • How do these results get stored, analyzed? – How can previous work be indexed, reused? +)"
- 41. MongoChem Overview • A desktop cheminformatics tool – Chemical data exploration and analysis – Interactive, editable, and searchable database • Leverages several open-source projects – Qt, VTK, MongoDB, Avogadro 2, Open Babel • Designed to look at many molecules • Spots patterns, outliers; runs many jobs • Scales to studies with ~3 million structures
- 42. Architecture Overview • Native, cross-platform C++ application built with Qt and Avogadro 2 • Stores chemical data in a NoSQL MongoDB database • Uses VTK for 2D and 3D dataset visualization +#"
- 43. Moving MongoChem to the Web • Increasingly important to share data • MongoDB not suitable for web directly – Developing RESTful APIs – Building on VTKWeb and Tangelo – Can do more processing close to the data • Can we develop a platform for chemists? – Could this address materials and other areas? – Deposition of data, curation, client-server processing, web interface and APIs +*"
- 44. VTKWeb, Tangelo and MongoChem • Uses VTK’s web architecture • Performs interactive 3D rendering • Runs in any modern web browser • Same MongoDB server as MongoChem • Moves more to the client JavaScript code • Using a simple, Python-based server – Easy to add new APIs – Easy to deploy/integrate into other solutions ++"
- 46. Why MongoDB? • SQL vs NoSQL approaches • MongoDB is implemented in C++ – Scales well by adding extra shards (nodes) – Core constructs written in C++ – Access to JavaScript in map-reduce – Memory-mapped database files – GridFS for storing large files – Clients in many languages – C, C++, Python – Large, established open-source project +%"
- 47. JSON, BSON and NoSQL • JSON: JavaScript Object Notation • BSON: Binary JSON – Binary-encoded serialization of JSON-like documents • MongoDB stores BSON documents – Collections are memory-mapped BSON – Clients work directly with BSON on-the-wire • BSON written by client can be used by server • Very little overhead reading/writing documents +&"
- 48. Nature of Data • Many documents for molecules – Individual results are usually MBs – Small molecules, electronic structure, MD, etc. • Materials tend to be different – Less documents, larger results – Less existing identifiers/search techniques • Institutions maintain big disks – Move to referencing data, client-server, etc. +'"
- 50. Clean Energy Project: Introduction • Searching for organic photovoltaics – IBM World Community Grid – High-throughput, in-silico study – Partnered with experimental groups • Synthesize most promising candidates • Many views of the data – Simple numbers for many properties – 2D graphs and 3D chemical structures – 3D structures with quantum calculation output $)" http://cleanenergy.molecularspace.org/
- 51. Clean Energy Project: Big Data • Overall size and scope of the data: – 2.3 million unique molecules • 22 million conformers • 150 million DFT calculations • 400TB+ of raw output data • 80GB of metadata – Growing at just under 1TB a day – ~2.8 million unique molecules • ~27M conformers and 185M DFT calculations • 0.5PB of raw data in the latest result set $!"
- 52. Clean Energy Project: Open Data • Part of the Materials Genome Initiative • Data released under CC-BY-SA license • Amazing opportunity for Open Chemistry – Very large dataset pushing current limits – Openly-licensed, allowing us to experiment – Opportunity to improve the state-of-the-art – Molecules fit our model • Less than 1024 atoms • DFT calculations with metadata extraction $#"
- 53. Building Community • Community around projects • Using Kitware software process – Ensuring quality with continuous testing – Code contributions on the web – Public mailing lists, bug trackers, and code review • Promoting projects and participation – Publication – Conferences – Workshops – Social media $*" Software Repository Build, Test & Package Community Review Developers & Users
- 54. Conclusions • Shared frameworks needed to work with data • Domain specific approaches are essential – One size fits all rarely works well – The right frameworks can be extended/customized • Storing, sharing, publishing, and analyzing data • Data scales increasing, client-server can help • Semantic data is an important aspect too • Questions? $+"