Download - Department of Computer Sciences Dynamic Shape Analysis via Degree Metrics Maria Jump & Kathryn S. McKinley Department of Computer Sciences The University.

Department of Computer Sciences

Dynamic Shape Analysisvia Degree Metrics

Maria Jump & Kathryn S. McKinleyDepartment of Computer SciencesThe University of Texas at Austin{mjump,mckinley}@cs.utexas.edu


20-Jun-2009 Jump & McKinley

Software is Dynamic Software is always changing

New expectations New algorithms New applications New users New notions of what is cool

Limited only by human ambition

First Law of Software: Software is a gas!It expands to fit the container it is in!

[Nathan Myhrvold, Former CTO, Microsoft]



Software Complexity

Developed by large teams Few, if any, understand all parts Need tools to help

Property of a system that is directly proportional to thedifficulty one has in comprehending the system

at a level and detail necessary to make changeswithout introducing instability or functional regressions

[Peter Rosser, Microsoft Software Engineer]



Code + Data

Program =

Program Analysis

Software complexity leads to heap complexity



Heap Complexity Exacerbated by modern languages

Objects are smaller and more numerous Most objects allocated on the heap

Objects encode program state Heap contains semantic, memory,

and concurrency bugs

Program analysis is incompletewithout heap analysis



An Opportunity Automatic memory management

examines live data on a regular basis Understand heap usage Detect heap-based bugs Optimize program based on heap usage

Opportunity to performheap analysis dynamically



Shape AnalysisCharacterize the “shape” of heap-allocated

pointer-based data structures andverify shape-preserving properties



Shape Analysis

Code + Data

Program =

Static Conservatively characterizes all “possible” shapes at every program point Only works on small programs

Dynamic Discover shape of current

data structure Generate assertions

for verification Monitor shape during

entire execution



Shape Analysis

0

20

40

60

80

100

compress

jess

raytrace

javac

mpegaudio

jackantlrbloatcharteclipse

fop

jythonluindexlusearch

pmdxalan

average

HomeBrewed Library%

of

Hea

p

LinkedHashMap: 99.3%

OctTree: 98%

BinaryTree: 35.8%HashMap: 59.5%

Custom Library

Custom: 33%Library: 58%



ShapeUp Desired goals

Discover shape of current data structure Discover dynamic invariants Monitor expected shape of current data

structure during execution

Heap summarization graph [POPL ‘07]

Class field-wise graph (CFWG) Identifies recursive data structures

(RDS) Summarize dynamic degree invariants



Class Field-Wise Graph (CFWG)

Heap

Home-brewed data structure from SPECjbb2000 (simplified)

CFWG



Class Field-Wise Graph (CFWG)

Heap

Home-brewed data structure from SPECjbb2000 (simplified)

CFWG

Degree Metrics



Degree Metrics Degree metrics per instance:

Out-degree: # of outgoing references In-degree: # of incoming references Only count edges between objects

of same class Use word in header to track in-

degree of backbone objects Summarize by class per data

structure instance in a degree profile



Calculating Degree Profile

CFWG




CFWG

n=1




CFWG

n=2




CFWG

n=3




CFWG

n=4




CFWG

n=5




CFWG

n=6




CFWG

n=7




CFWG

n=8




CFWG

n=9




CFWG

n=10




CFWG

n=11




CFWG

n=12




CFWG

n=13




CFWG

n=14




CFWG

n=15



Calculating Degree Profileconstant

range

n

Complete Binary Tree

CFWG




CFWG

left right

constant

range




Interpreting Degree Profile Monitoring degree profile shows

errors: Error: constant violation Warning: range violation [min-,max+]

Indicate problem with RDS during development and testing

Monitor shape after deployment for dynamically introduced errors

What kind of errors can be detected?



Implementation and Methodology Jikes RVM SPECjvm98, DaCapo, SPECjbb2000

Heap composition Showed degree metric are stable by

class Overheads

<8% total time <1% space overhead [used bits in header]

Microbenchmarks Single RDS in isolation Random error injection

Singly-linked listDoubly-linked list

Binary TreeBinary Tree w/ PP



Looking for errors

TRAINING Ran microbenchmark 100 times Merged together dynamic invariants

ERROR DETECTION Created RDS with 100,000 nodes Errors: 1, 2, 3, 4, 5, 10, 50, and

100 Detected dynamic invariant

violations



Doubly-linked List

=0 =1 =2

in 0 2 n-2

out

0 2 n-2

circle cyclic

disconnect skip



0

10

20

30

40

50

60

70

80

90

100

Circle

Disconnect

CyclicSkip

Random

% Detection

1 2 3 4 5 10 50 100

Doubly-linked List

=0 =1 =2

in 0 2 n-2

out

0 2 n-2



Binary Tree =0 =1 =2


in 1 n-1 0

out

[50.0,50.2]

0 [49.8,50.0]

Full Binary Tree

in 1 n-1 0

out

[50.0,50.1]

0 [49.9,50.0]

Random Binary Tree

in 1 n-1 0

out

[33.6,35.7]

[28.7,35.1]

[32.3,35.6]link



0

10

20

30

40

50

60

70

80

90

100

BTComplete

BTFull

BTRandom

% Detection

1 2 3 4 5 10 50 100

Binary Tree =0 =1 =2


in 1 n-1 0

out

[50.0,50.2]

0 [49.8,50.0]

Full Binary Tree

in 1 n-1 0

out

[50.0,50.1]

0 [49.9,50.0]

Random Binary Tree

in 1 n-1 0

out

[33.6,35.7]

[28.7,35.1]

[32.3,35.6]



Binary Tree w/ PP =1 =2 =3

Complete Binary Tree w/ PP

in [50.0,50.0]

2 [49.9,50.0]

out

[50.0,50.0]

2 [49.9,50.0]

Full Binary Tree w/ PP

in [50.0,50.1]

1 [49.9,50.0]

out

[50.0,50.1]

1 [49.9,50.0]

Random Binary Tree w/ PP

in [33.8,35.2]

[30.0,32.6]

[33.6,34.8]

out

[33.8,35.2]

[30.0,32.6]

[33.6,34.8]

link

disconnect



Microbenchmark: Binary Tree w/ PP

0102030405060708090

100

BTPComplete

Link

BTPFull LinkBTPRandom

Link

BTPCompleteDisconnect

BTPFull

DisconnectBTPRandom

DisconnectBTPComplete

RandomBTPFullRandom

BTPRandom

Random

% Detection

1 2 3 4 5 10 50 100



ShapeUp’s Contributions Performs dynamic heap analysis to determine

shapeof current data structure

Summarizes degree metrics using a class field-wise graph with low overheads

Shows whole-heap degree metrics are not stable, but class degree metrics are

Introduces degree profiles for RDS Regular RDS have more invariants than random RDS Degree profiles can detect some shape errors

Thank You!



Thank You!



Space Overhead

jess Eclipse Geomean

# of types

bm+VM 1744 3365 1747

avg 318 667 334

max 319 775 346

# of edges

avg 844 4090 904

max 861 7585 1142

Increased Alloc %

0.094% 0.167% 0.233%

19%

2.7X

0.233%



Time Overhead

No

rmal

ized

To

tal

Tim

e

Heap Size Relative to Minimum



Singly-linked List

circle := attaches head to tail

cyclic := creates a cycle from tail to random node

0

10

20

30

40

50

60

70

80

90

100

SLL Circle SLL Cyclic

1 Error

=0 =1

in 1 n-1

out

1 n-1

De

tec

tio

n %



Microbenchmark: HashMap=0 =1 =2

in [31.9,51.6]

[48.4,68.1]

{0}

out

[31.9,51.6]

[48.4,68.1]

{0}

link := creates a connection between buckets from a null ptr to a random entry

0

10

20

30

40

50

60

70

80

90

100

HM Link

1 2 3 4 5 10 50 100



Data Structures in Benchmarks

Benchmark RDS Node L or H? %

Raytrace OctNode H 78.0

Jack LinkedHashMap$LinkedHashEntry L 44.2

RuntimeNfaState H 9.4

Antlr Object[] H 76.2

Bloat HashMap$HashEntry L 56.6

CallMethodExpr H 3.7

Eclipse LinkedHashmap$LinkedHashEntry L 59.0

AND_AND_Expression H 0.4

Fop HashMap$HashEntry L 51.4

PropertyList H 4.4

Jython Pyframe H 94.6

Luindex LinkedHashMap$LinkedHashEntry L 99.3

Lusearch WeakHashMap$WeakBucket L 47.5

HitDoc H 2.0

Xalan ChildIterator H 34.6