Presentation Methodology Summary

1
Presentation Methodology Summary

• B. Golden

2
Introduction

• Why use visualizations?
• To facilitate user comprehension
• To convey complexity and intricacy of performance
  data
• Help bridge the gap between raw performance data
  and performance improvements
• When to use visualizations?
• On-line visualization while the application is
  running (can slow down execution significantly)
• Post mortem after execution (usually based on
  trace data gathered at runtime)
• What to visualize?
• Interactive displays to guide the user, not
  rationalize
• Default visualizations should provide high-level
  views
• Low-level information should be easily accessible

3
Visualization Concepts and Principles

• Context
• Perspective the point of view from which
  information is presented
• Semantic Context the relationship between
  performance information and user data constructs
• Sub-view Mapping a mapping between a subset of
  graphical views
• Scaling
• Multidimensional/Multivariate representation a
  representation of data with many attributes per
  data point
• Macroscopic/Microscopic views the level of
  detail represented by a given view
• Micro/Macro composition showing both local
  detail and global structure
• Adaptive display the adjustment of a displays
  characteristics in response to data size
• Display manipulation interactive modification of
  a display (i.e. zooming, scrolling)
• Composite view synthesis of two or more views
  into a single view
• Comparison
• Multiple views the presentation of data from
  multiple perspectives
• Small multiples a series of images indexed by
  changes in other performance data (e.g.
  animation)
• Cross-execution views comparison of performance
  information from various program executions
• Extraction of information
• Reduction and filtering representing raw data by
  statistical summaries
• Clustering multivariate statistical analysis and
  presentation techniques for grouping or
  categorizing related data points
• Encoding and abstracting using graphical
  attributes (color, shape, size, etc) to convey
  information

4
General Approaches to Performance Visualization

• General Categories
• Program specific application specific way to
  show how computation progresses by animating data
  structures
• System oriented focus on the impact the
  application has on the system
• System/Application independent depict
  performance data for a variety of systems and
  applications. See figure
• Meta-tools facilitate the development of custom
  visualization tools
• Other Categories
• On-line visualization during execution
• Can be intrusive
• Volume of information may be too large to
  interpret with out playback functionality
• Allows the user to observe only the interesting
  parts of execution
• Post mortem visualization after execution
• Large trace files
• Easier to implement
• Users are accustomed to this format

5
Specific Features of Existing Visualization
Strategies

• Animation
• Has been employed by various tools to assist in
  the program execution replay
• Communication operations are the most commonly
  animated events
• Viewing data dynamically may illuminate
  bottlenecks more efficiently
• Program graphs
• A generalized picture of the whole system
• Paradyn has this functionality
• Gantt charts
• De facto standard for displaying inter-process
  communication
• Data access displays
• Each cell of the 2D display is devoted to an
  element of the array
• Color distinguishes between local/remote and
  read/write
• Critical path analysis
• Concerned with identifying the program regions
  which most contribute to program execution time
• Construct a graph which depicts synchronization
  and communication dependencies among the
  processes in the program

6
Evaluation of User Interfaces

• General Guidelines
• Visualization should guide, not rationalize
• Scalability is crucial
• Color should inform, not entertain
• Visualization should be interactive
• Visualizations should provide meaningful labels
• Default visualization should provide useful
  information
• Avoid showing too much detail
• Visualization controls should be simple
• GOMS
• Goals, Operators, Methods, and Selection Rules
• Formal user interface evaluation technique
• A way to characterize a set of design decisions
  from the point of view of the user
• A description of what the user must learn may be
  the basis for reference documentation
• The knowledge is described in a form that can
  actually be executed (there have been several
  fairly successful attempts to implement GOMS
  analysis in software, ie GLEAN)
• There are various incarnations of GOMS with
  different assumptions useful for more specific
  analyses (KVL, CMN-GOMS, NGOMSL, CPM-GOMS, etc.)

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Simple GOMS Example

• GOMS model for OS X
• Method for goal delete a file.
• Step 1. Accomplish goal drag file to trash.
• Step 2. Return with goal accomplished.
• Method for goal move a file.
• Step 1. Accomplish goal drag file to
  destination.
• Step 2. Return with goal accomplished.
• GOMS model for UNIX
• Method for goal delete a file.
• Step 1. Recall that command verb is "rm -f".
• Step 2. Think of directory name and file name and
  retain as first filespec.
• Step 3. Accomplish goal enter and execute a
  command.
• Step 4. Return with goal accomplished.
• Method for goal copy a file.
• Step 1. Recall that command verb is "cp".
• Step 2. Think of source directory name and file
  name and retain as first filespec.
• Step 3. Think of destination directory name and
  retain as second filespec.
• Step 4. Accomplish goal enter and execute a
  command.
• Step 5. Return with goal accomplished.

8
Conclusion

• Plan for development
• Develop a preliminary interface that provides the
  functionality required by the user while
  conforming to visualization guidelines presented
  previously
• After the preliminary design is complete, elicit
  user feedback
• During periods where user contact is unavailable,
  we may be able to use GOMS analysis or another
  formal interface evaluation technique