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Cost Effective Dynamic Program Slicing. Xiangyu Zhang Rajiv Gupta The University of Arizona. Program Slicing. Definition Slice( v @ S ) Slice of v at S is the set of statements involved in computing v ’s value at S . [Mark Weiser, 1982] - PowerPoint PPT Presentation
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1
Cost Effective Dynamic Program Slicing
Xiangyu ZhangRajiv Gupta
The University of Arizona
2
Program Slicing
Definition
Slice(v@S)
• Slice of v at S is the set of statements involved in computing v ’s value at S. [Mark Weiser, 1982]
Static slice is the set of statements that COULD influence the value of a variable for ANY input.
• Construct static dependence graph Control dependences Data dependences
• Traverse dependence graph to compute slice Transitive closure over control and data dependences
3
Dynamic Slicing
Dynamic slice is the set of statements that DID affect the value of a variable at a program point for ONE specific execution. [Korel and Laski, 1988]
• Execution trace control flow trace -- dynamic control dependences memory reference trace -- dynamic data dependences
• Construct a dynamic dependence graph• Traverse dynamic dependence graph to compute slices• Smaller, more precise, slices are more helpful
4
Slice Sizes: Static vs. Dynamic
Program Statements
Avg. of 25 slices Static /
Dynamic Static Dynamic
126.gcc
099.go
134.perl
130.li
008.espresso
585,491
95,459
116,182
31,829
74,039
51,098
16,941
5,242
2,450
2,353
6,614
5,382
765
206
350
7.72
3.14
6.85
11.89
6.72
Static slice can be much larger than the dynamic slice
5
Applications of Dynamic Slicing
Debugging [Korel & Laski - 1988]
Detecting Spyware [Jha - 2003]
• Installed without users’ knowledge
Software Testing [Duesterwald, Gupta, & Soffa - 1992]
• Dependence based structural testing - output slices.
Module Cohesion [N.Gupta & Rao - 2001]
• Guide program structuring
Performance Enhancing Transformations• Instruction criticality [Ziles & Sohi - 2000]• Instruction isomorphism [Sazeides - 2003]
Others…
6
The Graph Size Problem
ProgramStatements Executed (Millions)
Dynamic Dependence
Graph Size(MB)
300.twolf
256.bzip2
255.vortex
197.parser
181.mcf
164.gzip
134.perl
130.li
126.gcc
099.go
140
67
108
123
118
71
220
124
131
138
1,568
1,296
1,442
1,816
1,535
835
1,954
1,745
1,534
1,707
Graphs of
realistic
program
runs do not
fit in
memory.
7
Space and Time Cost of LP [ICSE 2003]
ProgramSlicing Time
Average (Minutes)
Max. Dynamic Dependence
Graph Size(MB)
300.twolf
256.bzip2
255.vortex
197.parser
181.mcf
164.gzip
134.perl
130.li
126.gcc
099.go
13.9
9.2
10.2
9.9
12.3
4.69
25.2
11.3
12.1
10.7
296
81
34
40
114
35
54
105
58
162
Still not
fast
enough.
Need to
keep graph
in memory.
8
Input: N=2
Dependence Graph Representation
51: for I=1 to N do61: if (i%2==0) then
71: p=&a
81: a=a+191: z=2*(*p)
101: print(z)
11: z=021: a=031: b=2
41: p=&b
52: for I=1 to N do
62: if (i%2==0) then
82: a=a+192: z=2*(*p)
1: z=02: a=03: b=24: p=&b5: for i = 1 to N do6: if ( i %2 == 0) then7: p=&a endif8: a=a+19: z=2*(*p) endfor10: print(z)
9
5:for i=1 to N
6:if (i%2==0) then
7: p=&a
8: a=a+1
9: z=2*(*p)
10: print(z)
T F
1: z=0
2: a=0
3: b=2
4: p=&b
T
Input: N=2
11: z=0
21: a=0
31: b=2
41: p=&b
51: for i = 1 to N do
61: if ( i %2 == 0) then
81: a=a+1
91: z=2*(*p)
52: for i = 1 to N do
62: if ( i %2 == 0) then
71: p=&a
82: a=a+1
92: z=2*(*p)
101: print(z)
T1
2
3
4
5
6
7
8
9
10
11
12
13
14
Dependence Graph Representation
<3,8><2,7>
<7,12>
<11,13>
<13,14>
<4,8>
<12,13>
<5,6><9,10>
<10,11>
<5,7><9,12>
<5,8><9,13>
F
10
OPT: Compacted Graph Algorithm
Compaction• Elimination of timestamp labels.
Remove labels that can be inferred Transform dependence graph to enable elimination Remove labels that are redundant
Fast Traversal• Long search for relevant dependence is often replaced
by quick computation of dependence Consequence of compaction
11
OPT-1a. Infer Local Def-Use Labels: Full Elimination
X =
= X
X =
= X
0
X =
= X
(10,10)
(20,20)
(30,30)
Assign timestamps on node level
12
OPT-1b. Infer Local Def-Use Labels: Partial Elimination In Presence of Aliasing
X =
*P =
= X
X =
*P =
= X
(10,10)
(20,20) X =
*P =
= X
(10,10)0
*P is a may alias of X
13
OPT-2a. Transform Local Def-Use Labels: Full Elimination In Presence of Aliasing
Z =
Y =
(10,11)
(20,21)
(10,11)
(20,21)
X = f(Y)
= X
*P = g(Z)(11,11)
(21,21)
Z =
Y =(10,11)
(20,21)(10,11)
(20,21)
X = f(Y)
= X
*P = g(Z)
X = f(Y)
= X
*P = g(Z)0
0X = f(Y)
= X
*P = g(Z)
Z =
Y =
14
OPT-2b. Transform Non-local Def-Use to Local Use-Use Edges
= X
= X
X =
(10,11)
(20,21)
(10,11)
(20,21)
= X
= X
X =
(10,11)
(20,21) = X
= X
X =
0
use-use
15
OPT-2c. Transform Non-Local Def-Use to Local Def-Use Edges
X =
= Y
= X
Y =1
Y =2
X =
Y =1
Y =2
= Y
= X
(1,3)
(2,3)
(10,12)
(11,12)
X =
Y =1
Y =2
= Y
= X
(1,3)
(2,3)
= Y
= X
Y =2
X = 0
0
Node for path
16
OPT-3. Redundant Labels Across Non-Local Def-Use Edges
X =
Y =
= Y
= X
X =
Y =
X =
Y =
= Y
= X
X =
Y =
(1,2)
(1,2)
(10,11)
(10,11)
X =
Y =
= Y
= X
X =
Y =
(10,11)
(1,2)
17
OPT-4.(Control Dep.) Infer Fixed Distance Unique Control Ancestor
1
2
3
4
5
1.2.3.51.2.4.51.2.3.4.5
10.11.12.1320.21.22.2330.31.32.33.34
Path Timestamps
(32,33)
1
2
3
4
5
(10,13)
(20,23)
(30,34)
(21,22)
(11,12)
(31,32)
(10,11)
(20,21)
(30,31)
1
1
18
OPT-5a. Transform Multiple Control Ancestors
1
2
3
4
5
(32,33)
(10,13)
(20,23)
(30,34)
(21,22)
1
1
1
2
3
4
5
1
1
1
(10,13)
(30,34)
1
2
3
4
5
0
1
2
4
5
0
0
19
OPT-5b. Transform Varying Distance to Unique Control Ancestors
1
2
3
4
5
1
1
1
3
1
2
3
4
5
0
0 0
1
2
5
3
4 0
20
OPT-6. Redundant Across Non-Local Def- Use and Control Dependence Edges
X =
If P
= X
X =
If P
= X
(1,2)(1,2)
X =
If P
= X
(1,2)
21
Completeness of Label Elimination Optimizations
Data Dependence Labels• Local to a basic block
Infer (OPT-1a, OPT-1b) Transform (OPT-2a)
• Non-Local across basic blocks Transform (OPT-2b, OPT-2c) Redundant (OPT-3)
Control Dependence Labels Infer (OPT-4) Transform (OPT-5a, OPT-5b) Redundant (OPT-6)
22
Slicing algorithm (1)
{s2} U Slice(x,s2) @ t
0
Slice(v,s1) @ t =
…
s2: x=
…
s1:v=f(x,…)
0
23
Slicing algorithm (2)
Slice(x,s2) @ t
0
Slice(v,s1) @ t =
…
s2: …=x
…
s1:v=f(x,…)
0Use-use edge
24
Slicing algorithm (3)
{s3} U Slice(x,s3) @ t’Slice(v,s1) @ t =
…
…
s1:v=f(x,…)
…
s3: x=…
…
s4: x=…
…<t’,t>… …
25
Shortcuts to Speed Up Traversal
0: X =
1: Y = f(X)
2: Z = g(Y)
3: … = Z
(10,11)
(20,21)
0
0
0: X =
1: Y = f(X)
2: Z = g(Y)
3: … = Z
(10,11)
(20,21)
0
{2}
26
Experimental Setup
Implementation• Trimaran: C programs, IR (intermediate representation) • An instrumented interpreter executes IR, collects compact
control flow trace and memory trace.• CFG and PDG are constructed on IR level so that the
slicing is also on IR level.
Experiment• In order to get fair comparisons among algorithms,
we shared as much code as possible in different implementations.
• 2.2 GHz Pentium, 2 G RAM, 1 G swap space.• For each benchmark, we collected 3 different traces,
for each trace, we randomly computed 25 slices.
27
OPT: Compacted Graph Sizes
Program
Graph Size (MB) Before /
After
Explicit Dependences (%)Before After
300.twolf
256.bzip2
255.vortex
197.parser
181.mcf
164.gzip
134.perl
130.li
126.gcc
099.go
1,568
1,296
1,442
1,816
1,535
835
1,954
1,745
1,534
1,707
210
51
65
70
170
52
21
97
75
131
7.72
25.68
22.26
26.03
9.02
16.19
93.40
18.09
20.54
13.01
13.40
3.89
4.49
3.84
11.09
6.18
1.07
5.53
4.87
7.69
28
OPT: Effects
29
OPT: Slicing Times at Different Execution Points
30
OPT: Benefit of Shortcuts
ProgramOPT Slicing Times (Avg. of 25 slices)
W/O Shortcuts (Seconds)
With Shortcuts (Seconds)
300.twolf
256.bzip2
255.vortex
197.parser
181.mcf
164.gzip
134.perl
130.li
126.gcc
099.go
68.0
6.1
5.6
4.9
22.0
4.5
12.6
15.7
9.8
26.9
36.3
2.1
1.9
2.2
17.1
1.7
4.1
6.1
3.8
11.4
31
OPT vs. LP: Graph Sizes
ProgramGraph Size (MB)
OPT LP (Max. of 25)
300.twolf
256.bzip2
255.vortex
197.parser
181.mcf
164.gzip
134.perl
130.li
126.gcc
099.go
210
51
65
70
170
52
21
97
75
131
296
81
35
40
113
35
54
105
57
162
32
OPT vs. LP: Slicing Times
ProgramSlicing Times (Avg. of 25 slices)
OPT (Seconds)
LP (Minutes)
300.twolf
256.bzip2
255.vortex
197.parser
181.mcf
164.gzip
134.perl
130.li
126.gcc
099.go
36.3
2.1
1.9
2.2
17.1
1.7
4.1
6.1
3.8
11.4
13.9
9.2
10.2
9.9
12.3
4.7
25.2
11.3
12.1
10.7
33
Traditional vs. OPT: Short Program Runs
ProgramSlicing Times (Avg. of 25 slices)
OPT (Seconds)
Traditional (Seconds)
300.twolf
256.bzip2
255.vortex
197.parser
181.mcf
164.gzip
134.perl
130.li
126.gcc
099.go
36.3 : 68.0
2.1 : 6.1
1.9 : 5.6
2.2 : 4.86
17.1 : 22.0
4.5 : 1.7
4.1 : 12.6
6.1 : 15.7
3.8 : 9.8
11.4 : 26.9
66.0
5.9
6.2
5.3
21.7
4.8
-
17.9
11.0
29.8
34
Graph Construction Cost
• Trace Generation - Instrumented program takes twice as long to run as the uninstrumented program.
• Trace Preprocessing for Graph Construction Time(LP) < Time(OPT) < Time(Traditional)
Program LP (min) OPT (min) Trad. (min)
300.twolf
256.bzip2
255.vortex
197.parser
181.mcf
164.gzip
134.perl
130.li
126.gcc
099.go
14.54
9.38
16.35
16.23
16.64
14.56
17.18
19.23
26.65
17.06
65.29
38.36
44.46
44.06
53.64
23.52
51.12
49.88
48.83
35.24
99.62
80.78
55.47
67.57
71.17
31.66
-
74.86
52.70
42.17
35
Conclusion
A straightforward implementation of precise algorithm is not practical.
Carefully designed precise dynamic slicing algorithms provide precise dynamic slices at reasonable space and time costs.
Our work is one step toward making dynamic slicing practical.
• On going work: Efficient online compression another 5-10 times reduction; 15MB for 150Mills(over 100 times reduction in total); 4-10 times slowdown.