Visual Analytics: An opportunity for the HPC community Shawn J. Bohn September 8-10, 2008 HPC User Forum Meeting

Visual Analytics Definitions, What and Why Visual Analytics History of Science Leading into the Future New Requirements within Digital Universe Examples and Observations Conclusion 2

3 Visual Analytics Definition Congress: Visual analytics provides the last 12 inches between the masses of information and the human mind to make decisions Science: Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces

4 History of Graphics and Visualization 70s to 80s CAD/CAM Manufacturing, cars, planes, and chips 3D, education, animation, medicine, etc. 80s to 90s –Scientific visualization –Realism, entertainment 90s to 2000s –Information visualization –Web and Virtual environments 2000s to 2010s –Visual Analytics –Visual/audio appliances

5 Selected Societal Drivers and Observations Scale of Things to Come: Information: In 2002, recorded media and electronic information flows generated about 22 exabytes (10 18 ) of information In 2006, the amount of digital information created, captured, and replicated was 161 EB In 2010, the amount of information added annually to the digital universe will be about 988 EB (almost 1 ZB) REFERENCES: A Forecast of Worldwide Information Growth Through 2010: IDC National Open Source Enterprise - Intelligence Community Directive No. 301, July 11, 2006 UC Berkeley School of Information Management and Systems: Now much Information

6 Why Must We Adapt Scale of Things to Come: Information: Drivers of Digital Universe: 70% of the Universe is being produced by individuals Organizations (businesses, agencies, governments, universities) produce 30% Wal-Mart has a database of 0.5 PB; it captures 30,000,000 transactions/day The growth is uneven Today the United States accounts for 41% of the Universe; by 2010, the Asia Pacific region will be growing 40% faster than any of the other regions

7 Why Must We Adapt Scale of Things to Come: Information Drivers of Digital Universe Kinds of Data: About 2 GB of digital information is being produced per person per year 95% of the Digital Universe’s information is unstructured 25% of the digital information produced by 2010 will be images By 2010, the number of boxes will reach 2 billion The users will send 28 trillion s/year, totaling about 6 EB of data

8 Why Must We Adapt Scale of Things to Come: Information: Drivers of Digital Universe: Kinds of Data Interaction: Today's interaction designed for point and click on individual items, directories, folders, and lists Today's interaction assumes user knows subject, concepts within information spaces, and can articulate what they want Today's interaction assumes data and interconnecting relationships are static in meaning over time Today's interaction is one way initiated Today’s interaction (WIMP) designed over 30 years ago

Visual Analytic Engages Multiple Specialties

10 Examples Demonstrating Need Changing Nature of Information Structure: Temporal, dynamically changing relationships, determination of intent (DC Sniper & ThemeRiver)

11 Examples Demonstrating Need Information synthesis while preserving security and privacy Data signatures that are semantic and scale Country A Firm 1 Firm 2 Firm 3 Firm 4 Firm 5Firm 6 Firm 7 Firm 8 Firm 9 Firm 10 A Bank Financial Images Audio Video Discover what is there AND discover what isn’t there

HPC and Visual Analytics – Example Krishnan M, SJ Bohn, WE Cowley, VL Crow, and J Nieplocha "Scalable Visual Analytics of Massive Textual Datasets." In IPDPS IEEE International Parallel and Distributed Processing Symposium, March 2007, Long Beach, CA, USA. State-of-the-art 1 Distributed in both processing and data Scalable (for a single data type) Limitations Inability to reconfigure on the fly Data model is based on subsetting Batch vs. interactive 12

Some Observations End Users want to: Steer their data (INTERACTION) Include and discard data on-the-fly Via Scenarios/Hypothesis generation Surround the user (i.e., data access) Be data scale and modalities agnostic Work from their desktops/mobile device Web/thin client/server type applications No specialized graphics hardware (simple visuals) Customize visualization based on who they are. Developers of Visual Analytics want: Better toolkits and APIs (e.g., Global Arrays) (who should do this?)

14 Conclusions Visual Analytics is one of the fastest growing fields of study and practice. Practice of interdisciplinary science is required Broadly applies to many aspects of society Visual Analytics is an HPC opportunity Thank you