Using Visualization to Understand

the 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 Visualization:Existing 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 Visualization:Existing 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

Procedure:PID:Page Faults:

Redraw()1717

129...

Tuple Tabl

e

10

.

.

.

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 datavisual mapping– Metaphors and primitives are data independent

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Display

The Encoding Process: Example

X

Y

Graph Metaphor:

Spatial EncodingsProcedure:

PID:Page Faults:

Redraw()1717

129

Tuple

C

F

S

Rectangle Primitive: Attribute Encodings

0.5

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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 Scriptset table [DataVector]set parser [CSVParser -args $table]rparse $parser poletops.csv Import data into

tableCreate point primitive

set primitive [GLPoint]

Create graph metaphor

rwindow .radiosrgeometry .radios W 500 H 675rglob Graph .radios.map.radios.map SetData $table

Encode Longitude as X

set gran [expr 1.0 / 3600.0]set lmin [$table GetMin Longitude]set lmax [$table GetMax Longitude]set long [QUniformRangeMap –args $lmin $lmax $gran].radios.map EncodeAsXPosition \ [QRangeEncoding -args $long Longitude]

Encode # Radios as Color

set hue [list 0.0 0.5]set sat [list 0.0 1.0]set val [list 0.6 1.0]set ramp [IsomorphicColorMap –args $hue $sat $val]set rmin [$table GetMin Radios]set rmax [$table GetMax Radios]set radios [QNumberMap -args $Log $rmin $rmax]$ramp SetDomainMap $radios$primitive EncodeAsColor \ [QColorEncoding -args $ramp Radios]

Encode Latitude as Y

set lmin [$table GetMin Latitude]set lmax [$table GetMax Latitude]set lat [QUniformRangeMap –args $lmin $lmax $gran].radios.map EncodeAsYPosition \ [QRangeEncoding -args $lat Latitude]

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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 Explorer:Interactive 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 Computer:System 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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Change HW model or SW

Simulation & Visualization Cycle

Configure simulated machine and

software

Perform simulation

Visualize results

Change annotations

Change visualization

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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

Large amounts of unexpected idle timeVery little memory stall time in process view

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Visualization of Process Data

Processes go idle in kernel pfault/vfault calls

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Process Scheduling & Kernel Locks

CPU schedulingkernel lock

If the lock is unavailable, the process is descheduledIf the lock is available, it is immediately granted

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Process Scheduling & Kernel Locks

kernel lock is held

process is descheduled

Kernel lock is heavily contended

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Results With Processes Pinned

Lots of idle time and kernel lock contention still remain

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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