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Visual Data Analytics in the Cloud for Exploratory Science

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Invited talk at Tableau, Inc. Part 1: Large-scale 3D Visualization in the cloud. Part 2: Semi-automatic mashups for eScience.

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Visual Data Analytics in the Cloud for Exploratory Science

  1. 1. Visual Data Analytics in the Cloud for Exploratory Science Bill Howe, UW QuickTime™ and a decompressor are needed to see this picture. Huy Vo, Utah Claudio Silva, Utah Juliana Freire, Utah YingYi Bu, UW
  2. 2. 3/12/09 Bill Howe, UW 2VisTrails + GridFields Data acquisition is no longer the bottleneck Old model: “Query the world” (Data acquisition coupled to a specific hypothesis) New model: “Download the world” (Data acquired en masse, in support of many hypotheses)  Astronomy: High-resolution, high-frequency sky surveys (SDSS, LSST, PanSTARRS)  Oceanography: high-resolution models, cheap sensors, satellites  Biology: lab automation, high-throughput sequencing,
  3. 3. 3/12/09 Bill Howe, UW 3VisTrails + GridFields Biology Oceanography Astronomy Two dimensions#ofbytes # of apps LSST SDSS Galaxy BioMart GEO IOOS OOI LANL HIVPathway Commons PanSTARRS
  4. 4. 3/12/09 Bill Howe, UW 4VisTrails + GridFields This Talk  # of Bytes: MapReduce for Scientific Viz  # of Apps: Other VDA Projects
  5. 5. 3/12/09 Bill Howe, UW 5VisTrails + GridFields Converging Requirements Vis DB
  6. 6. 3/12/09 Bill Howe, UW 6VisTrails + GridFields Why Vis Needs DB “Transferring the whole data generated … to a storage device or a visualization machine could become a serious bottleneck, because I/O would take most of the … time. A more feasible approach is to reduce and prepare the data in situ for subsequent visualization and data analysis tasks.” -- SciDAC Review Current Research Topics in Vis: • “Query-driven Visualization” • “In Situ Visualization” • “Remote Visualization”
  7. 7. 3/12/09 Bill Howe, UW 7VisTrails + GridFields Why DB Needs Vis
  8. 8. 3/12/09 Bill Howe, UW 8VisTrails + GridFields Why DB Needs Vis (2) “What does the salt wedge look like?”
  9. 9. 3/12/09 Bill Howe, UW 9VisTrails + GridFields Thesis  We can no longer afford to build separate visualization and data management systems  Data is increasingly destined for the cloud  First Attack: Implement Vis primitives in an existing “cloud” DM system
  10. 10. 3/12/09 Bill Howe, UW 10VisTrails + GridFields Core Vis Algorithms in MapReduce  Scalar/Volume Rendering  Isosurface Extraction  Mesh Simplification
  11. 11. 3/12/09 Bill Howe, UW 11VisTrails + GridFields Some distributed algorithm… Map (Shuffle) Reduce
  12. 12. 3/12/09 Bill Howe, UW 12VisTrails + GridFields CluE Cluster  410 nodes  Dual Intel Xeon 2.8GHz, hyperthreading  8GB main memory each  Hadoop, no access to OS  Google provided, IBM maintaine, NSF funded
  13. 13. 3/12/09 Bill Howe, UW 13VisTrails + GridFields CluE Cluster Scaling
  14. 14. 3/12/09 Bill Howe, UW 14VisTrails + GridFields Isosurface Example
  15. 15. 3/12/09 Bill Howe, UW 15VisTrails + GridFields Isosurface Example
  16. 16. 3/12/09 Bill Howe, UW 16VisTrails + GridFields Isosurface Example
  17. 17. 3/12/09 Bill Howe, UW 17VisTrails + GridFields Isosurface Example
  18. 18. 3/12/09 Bill Howe, UW 18VisTrails + GridFields Isosurface Extraction
  19. 19. 3/12/09 Bill Howe, UW 19VisTrails + GridFields Isosurface Extraction
  20. 20. 3/12/09 Bill Howe, UW 20VisTrails + GridFields Isosurface Results O(N2 )O(N)
  21. 21. 3/12/09 Bill Howe, UW 21VisTrails + GridFields Scalable Rendering
  22. 22. 3/12/09 Bill Howe, UW 22VisTrails + GridFields Scalable Rendering  Left: Atlas  18GB  500M triangles  Right: St. Matthew  13GB  372M triangles  Laser Scans, Digital Michelandgelo project srrc: Digital Michelangelo project
  23. 23. 3/12/09 Bill Howe, UW 23VisTrails + GridFields Rendering Results
  24. 24. 3/12/09 Bill Howe, UW 24VisTrails + GridFields Roadmap  # of Bytes: MapReduce for Scientific Viz  # of Apps: Other VDA projects  Azure Ocean  SQLShare  Automating Mashups
  25. 25. 3/12/09 Bill Howe, UW 25VisTrails + GridFields [John Delaney, University of Washington]
  26. 26. 3/12/09 Bill Howe, UW 26VisTrails + GridFields Azure OceanAzure Ocean COVE for Visualization Trident for Processing Azure for Data+ +
  27. 27. 3/12/09 Bill Howe, UW 27VisTrails + GridFields SQLShare: Query Services for Ad Hoc Research Data
  28. 28. 3/12/09 Bill Howe, UW 28VisTrails + GridFields Ad Hoc Research Data 5/18/10 Garret Cole, eScience Institute Fasta format Spread sheets Tabular data
  29. 29. 3/12/09 Bill Howe, UW 29VisTrails + GridFields5/18/10 Garret Cole, eScience Institute Problem “I spend 90% of my time handling data rather than doing science” -- Robin Kodner, Postdoc, Armbrust Lab
  30. 30. 3/12/09 Bill Howe, UW 30VisTrails + GridFields An observation about “handling data”  How often does each RNA hit appear inside my annotated surface group?  SELECT hit, COUNT(*) as cnt FROM tigrfamannotation_surface GROUP BY hit ORDER BY cnt DESC 5/18/10 Garret Cole, eScience Institute
  31. 31. 3/12/09 Bill Howe, UW 31VisTrails + GridFields 31 Discovery: SQL Does not Terrify Scientists 5/18/10 Garret Cole, eScience Institute
  32. 32. 3/12/09 Bill Howe, UW 32VisTrails + GridFields
  33. 33. 3/12/09 Bill Howe, UW 33VisTrails + GridFields5/18/10 Garret Cole, eScience Institute Technology used in 1st Gen Component Stack
  34. 34. 3/12/09 Bill Howe, UW 34VisTrails + GridFields SQLShare Redux  Conventional wisdom says “Scientists won’t write SQL”  We don’t believe it!  Instead, we implicate difficulty in  installation  configuration  schema design  performance tuning  data ingest  over-reliance on GUIs  Critical need for visualization  Clear role for Tableau! We are asking “What kind of platform will make SQL useful for scientific inquiry?”
  35. 35. 3/12/09 Bill Howe, UW 35VisTrails + GridFields Automating Mashups
  36. 36. 3/12/09 Bill Howe, UW 36VisTrails + GridFields Why Mashups?  Jim Gray: # of datasets scales as N2  Each pairwise comparison generates a new dataset  Corollary: # of apps scales as N2  Every pairwise comparison motivates a new mashup  To keep up, we need to  entrain new programmers,  make existing programmers more productive,  or both
  37. 37. 3/12/09 Bill Howe, UW 37VisTrails + GridFields Satellite Images + Crime Incidence Reports
  38. 38. 3/12/09 Bill Howe, UW 38VisTrails + GridFields Twitter Feed + Flickr Stream
  39. 39. 3/12/09 Bill Howe, UW 39VisTrails + GridFields Why Mashups?  The time of one’s data fitting into a 15 page research paper is past.  Datasets are too large and complex to be conveyed with a handful of static images  Prediction: succinct, targeted, interactive web apps will become the currency of scientific communication  with the public  with policy makers  with colleagues in other disciplines  with peers  with students (K12 - grad)
  40. 40. 3/12/09 Bill Howe, UW 40VisTrails + GridFields Tableau Mashups
  41. 41. 3/12/09 Bill Howe, UW 41VisTrails + GridFields Conclusions  Converging requirements for DB and Vis  At high scale:  A Vis library in MapReduce  At high complexity:  Azure Ocean  Data + Workflow + Vis  “Client + Cloud”,“Computational mobility”  SQLShare  Ad Hoc data -- “anything goes”  Visualization critical  (semi-)automated mashups  “Show me what’s interesting”
  42. 42. 3/12/09 Bill Howe, UW 42VisTrails + GridFields Acknowledgments http://escience.washington.edu
  43. 43. 3/12/09 Bill Howe, UW 43VisTrails + GridFields BACKUP SLIDES
  44. 44. 3/12/09 Bill Howe, UW 44VisTrails + GridFields [John Delaney, University of Washington]
  45. 45. 3/12/09 Bill Howe, UW 45VisTrails + GridFields
  46. 46. 3/12/09 Bill Howe, UW 46VisTrails + GridFields John Delaney
  47. 47. 3/12/09 Bill Howe, UW 47VisTrails + GridFields Azure OceanAzure Ocean COVE for Visualization Trident for Processing Azure for Data+ +
  48. 48. COVECOVE  Research into new interfaces for cross-disciplinary ocean scienceResearch into new interfaces for cross-disciplinary ocean science  Extensive instrument and cable layout for creating experimentsExtensive instrument and cable layout for creating experiments  Flexible terrain and image engine for visualizing siteFlexible terrain and image engine for visualizing site  True 3D/4D science dataset visualizationTrue 3D/4D science dataset visualization  Field tested in RSN observatory layout and on ocean expeditionsField tested in RSN observatory layout and on ocean expeditions  Cross platform and extensible with python and workflow systemsCross platform and extensible with python and workflow systems
  49. 49. 3/12/09 Bill Howe, UW 49VisTrails + GridFields TridentTrident  Microsoft Research scientific workflow systemMicrosoft Research scientific workflow system  Visual programming environment for connecting tasksVisual programming environment for connecting tasks  Science-specific task libraries including one for ocean sciencesScience-specific task libraries including one for ocean sciences  Automated provenance capture, monitoring, and fault toleranceAutomated provenance capture, monitoring, and fault tolerance  Runs on local system, Windows server, or HPC ClusterRuns on local system, Windows server, or HPC Cluster  Cross platform with Silverlight and web service interfaceCross platform with Silverlight and web service interface
  50. 50. 3/12/09 Bill Howe, UW 50VisTrails + GridFields AzureAzure  Microsoft’s cloud computing platformMicrosoft’s cloud computing platform  Provides storage and computing as pay-as-you-go servicesProvides storage and computing as pay-as-you-go services  From development standpoint, system looks like provisioned VM’sFrom development standpoint, system looks like provisioned VM’s  SQL, table, and blob (file system) storage models are includedSQL, table, and blob (file system) storage models are included  Access to storage via RESTful HTTP interfaceAccess to storage via RESTful HTTP interface
  51. 51. 3/12/09 Bill Howe, UW 51VisTrails + GridFields Azure OceanAzure Ocean  COVE + Trident + Azure provides visual analytics to scientistsCOVE + Trident + Azure provides visual analytics to scientists  Any component –Any component – VisualizationVisualization,, ComputingComputing, or, or DataData –– can becan be provisioned locally, on a server, or in the cloudprovisioned locally, on a server, or in the cloud  When on same machine, system APIs are leveraged for speedWhen on same machine, system APIs are leveraged for speed  When distributed, communication is through HTTP and RESTful APIsWhen distributed, communication is through HTTP and RESTful APIs  Flexible platform for the diverse ocean science needsFlexible platform for the diverse ocean science needs
  52. 52. 3/12/09 Bill Howe, UW 52VisTrails + GridFields
  53. 53. 3/12/09 Bill Howe, UW 53VisTrails + GridFields MapReduce Programming Model  Input & Output: each a set of key/value pairs  Programmer specifies two functions:  Processes input key/value pair  Produces set of intermediate pairs  Combines all intermediate values for a particular key  Produces a set of merged output values (usually just one) map (in_key, in_value) -> list(out_key, intermediate_value) reduce (out_key, list(intermediate_value)) -> list(out_value) slide source: Google, Inc.
  54. 54. 3/12/09 Bill Howe, UW 54VisTrails + GridFields Isosurface Example
  55. 55. 3/12/09 Bill Howe, UW 55VisTrails + GridFields Isosurface Example <Vis movie>QuickTime™ and a decompressor are needed to see this picture. Key idea: Zooplankton correlated with temperature
  56. 56. 3/12/09 Bill Howe, UW 56VisTrails + GridFields Example Query Results
  57. 57. 3/12/09 Bill Howe, UW 57VisTrails + GridFields Example Query: Climatology Feb May Average Surface Salinity by Month Columbia River Plume 1999-2006 Columbia River psu Washington Oregon animation
  58. 58. 3/12/09 Bill Howe, UW 58VisTrails + GridFields UW + Utah CluE Program  Goals  10+-year “climatologies” at interactive speeds  …with provenance, reproducibility, collaboration …on a shared-nothing, commodity platform  In general: Explore the intersection of scientific databases and scientific visualization, at scale  Methods  “Cloud-Enable” two projects  GridFields: Query algebra for mesh data  VisTrails: Scientific workflow and provenance
  59. 59. 3/12/09 Bill Howe, UW 59VisTrails + GridFields
  60. 60. 3/12/09 Bill Howe, UW 60VisTrails + GridFields Converging Requirements Vis: “Query-driven Visualization” Vis: “In Situ Visualization” Vis: “Remote Visualization” DB: Millions of tuples per result Vis DB
  61. 61. 3/12/09 Bill Howe, UW 61VisTrails + GridFields Preliminary results  Managing Hadoop jobs with VisTrails  GridField queries in Hadoop  Core Visualization algorithms in Hadoop
  62. 62. 3/12/09 Bill Howe, UW 62VisTrails + GridFields Core Vis Algorithms in MapReduce  Scalar/Volume Rendering  Map: Rasterization  Reduce: Compositing, blending  Isosurface Extraction  Map: Isosurface Extraction  Reduce: Combine like isovalues  Mesh Simplification  Map: Bin vertices  Reduce: Collapse binned triangles
  63. 63. 3/12/09 Bill Howe, UW 63VisTrails + GridFields ATLAS dataset
  64. 64. 3/12/09 Bill Howe, UW 64VisTrails + GridFields Rendering (not CluE) # of mappers 57-node Nehalem
  65. 65. 3/12/09 Bill Howe, UW 65VisTrails + GridFields Isosurface Extraction (Preliminary) 32 48 64 96 128
  66. 66. 3/12/09 Bill Howe, UW 66VisTrails + GridFields “Query-Driven Visualization”  Vis perspective:  query = subsetting  DB perspective:  query = manipulation, preparation, restructuring, index-building, aggregation, regridding, downsampling, simplification, reformatting, etc. Database Maxims: 1. Push the computation to the data. 2. Declarative programming is a good thing.
  67. 67. 3/12/09 Bill Howe, UW 67VisTrails + GridFields Why Cloud?  “Cloud”?  Software as a Service (SaaS)  Infrastructure as a Service (IaaS)  Platform as a Service (PaaS)  Working definition: General, elastic, data-intensive, scalable computing This work: Vis techniques + DB techniques in the Cloud
  68. 68. 3/12/09 Bill Howe, UW 68VisTrails + GridFields Shared Nothing Parallel Databases  Teradata  Greenplum  Netezza  Aster Data Systems  Datallegro  Vertica  MonetDB Microsoft Recently commercialized as “Vectorwise”
  69. 69. 3/12/09 Bill Howe, UW 69VisTrails + GridFields Taxonomy of Parallel Architectures Easiest to program, but $$$$ Scales to 1000s of nodes
  70. 70. 3/12/09 Bill Howe, UW 70VisTrails + GridFieldsscreenshot: VisTrails, Claudio Silva, Juliana Freire, et al., University of Utah VisTrails
  71. 71. 3/12/09 Bill Howe, UW 71VisTrails + GridFieldsscreenshot: VisTrails, Claudio Silva, Juliana Freire, et al., University of Utah Version Tree
  72. 72. 3/12/09 Bill Howe, UW 72VisTrails + GridFields Collaboration Bill Howe @ UW computes salt flux using GridFields Erik Anderson @ Utah adds vector streamlines and adjusts opacity Bill Howe @ UW adds an isosurface of salinity Peter Lawson adds discussion of the scientific interpretation Howe et al., eScience 2008
  73. 73. 3/12/09 Bill Howe, UW 73VisTrails + GridFields Preliminary results  Managing Hadoop jobs with VisTrails  GridField queries in Hadoop  Core Visualization algorithms in Hadoop
  74. 74. 3/12/09 Bill Howe, UW 74VisTrails + GridFields Preliminary results  Managing Hadoop jobs with VisTrails  GridField queries in Hadoop  Core Visualization algorithms in Hadoop
  75. 75. 3/12/09 Bill Howe, UW 75VisTrails + GridFields Hadoop in VisTrails  Wrap Hadoop Streaming/HDFS Operations  Plug “PreProcess” to actual Vis Pipeline 3/12/09 75
  76. 76. 3/12/09 Bill Howe, UW 76VisTrails + GridFields Hadoop in VisTrails  Provenance and Monitoring 3/12/09 76
  77. 77. 3/12/09 Bill Howe, UW 77VisTrails + GridFields Preliminary results  Managing Hadoop jobs with VisTrails  GridField queries in Hadoop  Core Visualization algorithms in Hadoop
  78. 78. 3/12/09 Bill Howe, UW 78VisTrails + GridFields All Science is reducing to a database problem Old model: “Query the world” (Data acquisition coupled to a specific hypothesis) New model: “Download the world” (Data acquired en masse, independent of hypotheses)  Astronomy: High-resolution, high-frequency sky surveys (SDSS, LSST, PanSTARRS)  Medicine: ubiquitous digital records, MRI, ultrasound  Oceanography: high-resolution models, cheap sensors, satellites  Biology: lab automation, high-throughput sequencing “Increase Data Collection Exponentially in Less Time, with FlowCAM” Empirical X  Analytical X  Computational X  X-informatics
  79. 79. 3/12/09 Bill Howe, UW 79VisTrails + GridFields Key Idea: Declarative Languages SELECT * FROM Order o, Item i WHERE o.item = i.item AND o.date = today() join select scan scan date = today() o.item = i.item Order oItem i Find all orders from today, along with the items ordered
  80. 80. 3/12/09 Bill Howe, UW 80VisTrails + GridFields Example System: Teradata AMP = unit of parallelism
  81. 81. 3/12/09 Bill Howe, UW 81VisTrails + GridFields Example System: Teradata AMP 1 AMP 2 AMP 3 select date=today() select date=today() select date=today() scan Order o scan Order o scan Order o hash h(item) hash h(item) hash h(item) AMP 4 AMP 5 AMP 6
  82. 82. 3/12/09 Bill Howe, UW 82VisTrails + GridFields Example System: Teradata AMP 1 AMP 2 AMP 3 scan Item i AMP 4 AMP 5 AMP 6 hash h(item) scan Item i hash h(item) scan Item i hash h(item)
  83. 83. 3/12/09 Bill Howe, UW 83VisTrails + GridFields Example System: Teradata AMP 4 AMP 5 AMP 6 join join join o.item = i.item o.item = i.item o.item = i.item contains all orders and all lines where hash(item) = 1 contains all orders and all lines where hash(item) = 2 contains all orders and all lines where hash(item) = 3
  84. 84. 3/12/09 Bill Howe, UW 84VisTrails + GridFields Workflow Execution Plans Need execution plans spanning client/server/cloud
  85. 85. 3/12/09 Bill Howe, UW 85VisTrails + GridFields Example: Isosurface Browsing QuickTime™ and a decompressor are needed to see this picture.
  86. 86. 3/12/09 Bill Howe, UW 86VisTrails + GridFields Example: Isosurface Browsing  Plan A Subset Subset Subset Subset tstep 0 tstep 1 tstep 2 tstep 3
  87. 87. 3/12/09 Bill Howe, UW 87VisTrails + GridFields Example: Isosurface Browsing  Plan B: Build an index Build Index, e.g., an Interval Tree (Cignoni 97) Subset Subset Subset tstep 0 tstep 1 tstep 2 tstep 3 Subset Render Isosurface Isosurface Isosurface Isosurface Render Render Render
  88. 88. 3/12/09 Bill Howe, UW 88VisTrails + GridFields Example: Isosurface Browsing  Plan C: Build a spatial index to support panning  Plan D: Build a multi-resolution index to support zoom  …and so on  Why not precompute all appropriate indexes?  Some will (partially) reside on client  Storage is not as cheap as we pretend  Need a flexible system where  a “query result” can be explored interactively, and  we prepare for similar queries  similarity defined by natural “browsing patterns” in visualization systems
  89. 89. 3/12/09 Bill Howe, UW 89VisTrails + GridFields
  90. 90. 3/12/09 Bill Howe, UW 90VisTrails + GridFields Why MapReduce/Hadoop?  Popular  AWS Elastic MapReduce  100s of startups  # of downloads  # of blog posts  Free as in Speech  Free as in Beer  Flexible, Lightweight  Scalable  Fault-tolerant
  91. 91. 3/12/09 Bill Howe, UW 91VisTrails + GridFields Reducing Latency  Online processing/progressive refinement  Deliver approximate/partial results  Standing Queries/Prepared plans  Exploit indexes Changes to Hadoop and/or other tools required (e.g., Hbase)
  92. 92. 3/12/09 Bill Howe, UW 92VisTrails + GridFields Masking Latency  Caching/materialized views  Reuse old results  Pre-fetching  Stage and prepare new results  Speculative processing  Anticipate future results No change to Hadoop required
  93. 93. 3/12/09 Bill Howe, UW 93VisTrails + GridFields source: Antonio Baptista, NSF CMOP STC
  94. 94. 3/12/09 Bill Howe, UW 94VisTrails + GridFields Why Visualization? (2) north channel south channel
  95. 95. 3/12/09 Bill Howe, UW 95VisTrails + GridFields MapReduce?  Hadoop simplifies parallel data processing  ++ scalability  ++ fault tolerance  ++ less programming  -- latency is an issue
  96. 96. 3/12/09 Bill Howe, UW 96VisTrails + GridFields 1 2 3 4 5 6 7 31 23 psu 8 9 10 11 12 13 14 15 16 17 18 (b) 19 20 21 22 24 25 26 27 28 29 30 Climatology Queries
  97. 97. 3/12/09 Bill Howe, UW 97VisTrails + GridFields
  98. 98. 3/12/09 Bill Howe, UW 98VisTrails + GridFields As a GridField Expression ⊗ H0 : (x,y,b) V0 : (σ ) apply(0, z=(surf − b) * σ ) bind(0, surf) C H = Scan(contxt, "H") rH = Restrict("(326<x) & (x<345) & (287<y) & (y<302)", 0, H) T = Scan(contxt, “T”) V = Scan(contxt, “V”) HxV = Cross(H, V) HxVxT = Cross(HxV, T) salt = Bind(contxt, HxVxT, “salt”) onemonth = Regrid(salt, HxV, equijoin(“hpos,vpos”), avg())
  99. 99. 3/12/09 Bill Howe, UW 99VisTrails + GridFields As a SQL Query Select hpos, vpos, avg(salt) from ocean group by hpos, vpos
  100. 100. 3/12/09 Bill Howe, UW 100VisTrails + GridFields Scientific Workflow Systems  Value proposition: More time on science, less time on code  How: By providing language features emphasizing sharing, reuse, reproducibility, rapid prototyping, efficiency  Provenance  Visual programming  Caching  Integration with domain-specific tools  Scheduling
  101. 101. 3/12/09 Bill Howe, UW 101VisTrails + GridFields Related Vis Work  Parallel visualization systems  ParaView, VisIt  Query-Driven Visualization  [Bethel et al 2006,2008,2009]  FastBit Index  [Shoshani et al 2007]  DB Vis systems  Tableau
  102. 102. 3/12/09 Bill Howe, UW 102VisTrails + GridFields Feeding the Pipeline source: Ken Moreland missing step?
  103. 103. 3/12/09 Bill Howe, UW 103VisTrails + GridFields Cannot Ignore “Preprocessing” Hadoop
  104. 104. 3/12/09 Bill Howe, UW 104VisTrails + GridFields Role 2: Move Computation to the Data “Transferring the whole data generated … to a storage device or a visualization machine could become a serious bottleneck, because I/O would take most of the … time. A more feasible approach is to reduce and prepare the data in situ for subsequent visualization and data analysis tasks.” -- SciDAC Review
  105. 105. 3/12/09 Bill Howe, UW 105VisTrails + GridFields Remote Visualization  Reduce and render remotely, transfer images  ++ transfers less data  -- specialized hardware, high load  Reduce remotely, transfer data/geometry, render locally  ++ uses local graphics pipeline  -- transfers more data
  106. 106. 3/12/09 Bill Howe, UW 106VisTrails + GridFields
  107. 107. 3/12/09 Bill Howe, UW 107VisTrails + GridFields Scientific Vis System Roundup  General  ParaView [KitWare, Los Alamos, Sandia]  VisIt [LLNL]  Specialized  SALSA, particles, Quinn, UW  VISUS, streaming/progressive, Jones, LLNL  SAGE,  Hyperwall, tiled display, NASA

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