Using Visualization to Understand the Behavior of Computer Systems

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Using Visualization to Understandthe Behavior of
Computer Systems

• Robert P. Bosch Jr.
• Stanford University
• May 3, 2001

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Motivation

• Explosion in complexity of computer systems
• Development of rich data collection tools
• Complete Machine Simulation SimOS
• Software Monitoring Performance Co-Pilot (PCP)
• Firmware Instrumentation FlashPoint
• Hardware Monitoring DCPI
• Challenge how do we fully exploit the large,
  detailed data sets these tools can generate?

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Data Analysis Challenge

• How do we typically handle this data?
• Visual inspection of huge log files
• Summarize through statistics and aggregation
• Focus on restricted data subsets
• Alternative approach data visualization
• Display large amounts of data at once
• Enable interactive exploration of entire data set
• Overview, zoom and filter, details-on-demand
• Use human perception to discover patterns,
  trends, and interesting information

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Computer Systems VisualizationExisting Work

• Pedagogical examples
• Processors DLXView, Pentium Pro Tutorial
• Memory Cache Visualization Tool (CVT)
• Parallel systems performance
• AIMS, Pablo, Paradyn, ParaGraph, PARvis,
  StormWatch, VAMPIR
• Other examples
• Network performance, file systems, etc.

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Computer Systems VisualizationExisting Work

• Demonstrate potential of visualization
• Limitations
• Focused on particular system components
• Integrated with specific data collection tools
• Limited to a fixed set of visual representations
• Conclusion rich, flexible data sources require
  an equally powerful and flexible visualization
  system

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Outline

• Motivation
• The Rivet visualization environment
• Architecture
• Implementation
• Focused visualization systems
• SUIF Explorer
• Thor
• PipeCleaner
• Visible Computer
• Ad hoc analysis and visualization
• Contributions

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Rivet Architecture Goals

• Learn once, apply to wide range of problems
• Decouple visualization and data collection
• Interactive exploration of large data sets
• Couple visualization and analysis
• Rapid prototyping of visualizations
• Extensibility
• Allow users to add new components

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Rivet Architecture Approach

• Identify visualization building blocks
• Mix and match to create visualizations
• Users can add new components as needed
• Three basic object types
• Data management tuples, tables, and transforms
• Visual representation primitives and metaphors
• Mapping from data to visual encodings

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Data Model

• Simplified relational model
• Tuple collection of data fields
• Table set of tuples with common data format
• Familiar, homogeneous model
• Load data by parsing text files
• Directly save/load tables in binary format

Tuple
Table
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Data Transforms

• Transforms enable users to operate on data
• Can be composed to form data networks
• Active data changes are propagated
• Rivet includes a set of standard transforms
• Filter, sort, group, merge, join, aggregate, etc.
• Users may design and incorporate their own
• Example clustering algorithms

GroupBy
Transform
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Visual Objects and Data Mapping

• Primitives draw individual tuples
• Metaphors draw entire tables
• Draws table attributes axes, labels, etc.
• For each tuple compute bounding box, select
  primitive
• Encodings map tuple contents to visual properties
• Metaphors use spatial encodings
• Map tuple fields to bounding box parameters
• Primitives use attribute encodings
• Map tuple fields to retinal properties color,
  fill pattern, size
• Encodings encapsulate data?visual mapping
• Metaphors and primitives are data independent

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The Encoding Process Example
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Coordination and Interaction

• Implicit coordination sharing of objects
• Examples
• Primitives share attribute encodings brushing
• Metaphors share primitives common appearance
• Metaphors share spatial encodings common axes
• Listener mechanism
• Explicit coordination events and bindings
• Rivet objects can raise events
• User can bind actions to these events
• Example details-on-demand

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Rivet Implementation

• Goal balance performance and flexibility
• Interactive visualizations of large data sets
• Rapid prototyping of visualizations
• C and OpenGL for performance
• Also provides platform independence
• Scripting language interfaces for flexibility
• Simplified Wrapper and Interface Generator (SWIG)
• Create visualizations by writing scripts
• Interpreter is not in the main loop
• Listener mechanism for high-frequency events
• Event bindings for low-frequency user interaction

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Sample Rivet Script
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Outline

• Motivation
• The Rivet visualization environment
• Architecture
• Implementation
• Focused visualization systems
• SUIF Explorer Interactive user-directed
  parallelization
• Thor Detailed memory profiling on FLASH
• PipeCleaner Superscalar processor pipelines
• Visible Computer System and cluster monitoring
• Ad hoc analysis and visualization
• Contributions

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SUIF ExplorerInteractive Parallelization
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SUIF Explorer Background

• Goal enable sequential codes to run fast on MPs
• Compiler parallelizes loops when possible
• Otherwise
• Compiler presents loop analysis results to user
• User applies application knowledge to assist
  compiler
• Data source SUIF dynamic analyzers
• Performance metrics for all loops in program
• Coverage, granularity, loop level
• Visualization
• Displays data in context of program source code
• Focuses on loops most deserving of user attention

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SUIF Explorer Discussion

• Benefits
• Combines data with source, instead of loop IDs
• Allows user to filter uninteresting lines of code
• Facilitates comparisons between runs
• Limitations
• Loosely coupled with rest of SUIF Explorer
• Short loops less prominent than long loops
• Add sortable table of results as a linked view

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Thor Detailed Memory Profiling
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Thor Background

• Memory getting slower relative to CPU
• High remote access latencies on NUMA systems
• Memory system is often performance bottleneck
• Data source FlashPoint protocol on FLASH
• Collects all cache and TLB misses in firmware
• Classified as local/remote, read/write
• Attributed to CPU, procedure, data structure
• Visualization
• Stacked bar charts showing miss counts
• Collection of UI controls for configuring the
  display

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Thor Discussion

• Both post-mortem and real-time analysis
• Live connection to FLASH via socket
• Useful for multi-phase applications
• Simple, familiar visualization
• Interactive filtering, sorting, aggregation
• Drill down from overview to data of interest

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PipeCleaner Superscalar Processors
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PipeCleaner Background

• High peak performance
• Complex implementation techniques
• Speculation, multiple functional units,
  out-of-order execution
• Intended to be transparent to the programmer
• True for correctness, not necessarily for
  performance
• Data sources MXS and MMIX simulators
• Detailed superscalar processor pipeline models
• Visualization three linked views
• Overview occupancy strip charts
• Detail animated pipeline display
• Context program source code

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PipeCleaner Discussion

• Applications of PipeCleaner
• Program development
• Compiler optimizations
• Hardware design
• Education
• Simulator development
• Possible extensions
• Other computer pipelines graphics hardware
• Physical pipelines assembly lines, etc.

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Visible ComputerSystem and Cluster Monitoring
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Visible Computer Background

• Real-time analysis of system behavior
• Observe behavior of system in its entirety
• Explore interesting phenomena in detail
• Data sources PCP, SimOS
• Comprehensive data sources
• Collect low-level hardware events
• Cache misses, disk requests, CPU utilization,
  etc.
• Classify using high-level structures
• Process name, user/group, CPU mode, etc.

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Visible Computer Visualization

• Organizes data using nested physical hierarchy
• Provides overviews at each level of detail
• Active icons representing system components
• User defines data ranges of interest for each
  component
• Icons activate when components are in range
• Allows users to remove the cover, show next
  level
• Displays detailed charts on demand
• Data filtered/colored using high-level classifiers

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Visible Computer Discussion

• Unified interface for computer systems data
• Focus-plus-context view of system
• Physical layout, virtual classification
• Provides an overview of system behavior
• Draws attention to potential problem areas
• Suggests targets for further exploration

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Outline

• Motivation
• The Rivet visualization environment
• Architecture
• Implementation
• Focused visualization systems
• SUIF Explorer
• Thor
• PipeCleaner
• Visible Computer
• Ad hoc analysis and visualization
• Contributions

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Ad hoc Analysis and Visualization

• SimOS Complete machine simulator
• Full non-intrusive access to HW, OS, SW state
• Flexible data collection mechanism
• Deterministic execution
• Combine with Rivet
• Flexible data import mechanism
• Rapid prototyping of visualizations
• Result powerful analysis framework

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Simulation Visualization Cycle
Change HW model or SW
Configure simulated machine and software
Change annotations
Perform simulation
Change visualization
Visualize results
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Case Study Argus

• Parallel, multithreaded graphics library
• NURBS rendering application
• Implemented using fork shared memory region
• Performance limitations on SGI Origin
• Linear speedup up to 26 processors
• Rapid performance falloff beyond 26 CPUs
• Imported into SimOS environment
• Same performance characteristics observed
• Expectation memory system is the problem

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Memory Visualization
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Memory Visualization
1. Histograms of memory stall time vs.
physical/virtual address
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Memory Visualization
2. Memory stall time incurred by each line of
source code
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Memory Visualization
3. Local/remote stall time and idle time for each
process
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Memory Visualization
Very little memory stall time in process view
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Visualization of Process Data
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Process Scheduling Kernel Locks
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Process Scheduling Kernel Locks
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Results With Processes Pinned
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Results Using sproc

• Minimal kernel time
• No idle time at all
• Completes nearly twice as fast as original
  version
• 95 parallel efficiency

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Argus Conclusion

• Five simulation visualization iterations
• Initial suspicions were totally incorrect
• Flexibility of Rivet SimOS enabled us to follow
  leads
• Visualizations led us to the bottleneck
• Single scheduling event out of over 50,000 events

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Contributions

• Rivet computer systems visualization environment
• Rapid prototyping support
• Quick rough cut visualization of data
• Incremental development of sophisticated data
  displays
• Modular architecture
• Mix and match basic building blocks to create
  visualizations
• Enables coordination through object sharing
• Data transforms as first-class citizens in viz
  environment
• Unifies the analysis and visualization process
• Collection of computer systems visualizations
• Emphasize interactive data exploration
• Use relatively conventional visual
  representations
• Provide extensive support for filtering, sorting,
  brushing

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Acknowledgments

• Orals committee Joel Ferziger, Mendel Rosenblum,
  Pat Hanrahan, Mark Horowitz, Monica Lam
• Mendel
• Visualization collaborators
• PipeCleaner Donald Knuth
• Visible Computer John Gerth
• Argus Gordon Stoll
• Thor Jeff Gibson
• SUIF Explorer Shih-Wei Liao

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Acknowledgments

• Groupmates Rivet, SimOS, FLASH, Graphics
• Studio 354
• Steve Herrod and John Heinlein
• The foosball table
• Chris Stolte and Diane Tang
• Staff
• John, Charlie, Thoi, Kevin
• Ada, Heather, Chris,
• Funding agencies ONR, D?ARPA, ASCI
• Robert Bosch Corporation

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Acknowledgments

• Rains 7A Steve, Hoa, Marco
• Friends
• Family
• Ming