probabilistic pointer analysis [ppa]
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Probabilistic Pointer Analysis [PPA]Probabilistic Pointer Analysis [PPA]Presented by: Jeff DaSilvaPresented by: Jeff DaSilva CARG
April 12, 2005
2
The Pointer Alias Analysis Problem
Statically decide for any pair of pointers, at any point in the program, whether two pointers point to the same memory location.
*A = ~ ~ = *B
*A = ~ ~ = *B
This problem is known to be undecidable. [Landi 1992]
Definitely Not
Definitely
Maybe
Typically, ‘Maybe’ is
treated like
‘Definitely’
3
The Compiler Writer vs. The Programming Language Designer
Pointers are needed by programmers to realize complex data structures
Pointers can make life difficult for optimizing compilers
4
Traditional pointer analysis techniques are either overly conservative or are so complex that they fail to scale with respect to code size Examples include: Address taken, Anderson’s
analysis, Steensgaard, Emami
Pointer analysis is a very difficult problem that may never be adequately solved.
Does hardware support for data speculation make the analysis easier for the compiler?
Concluding Remarks (from last time)
5
Support for Data Speculation Exists
EPIC instruction sets Uses explicit load/store speculation
Thread Level Speculation (TLS) Speculative Compiler Optimizations
Speculative PRE, register promotion and strength reduction [ ]
‘Speculative’ behavioral synthesis
Why are these techniques not more widely used?
One Reason:
Currently, they rely on data dependence profiling.
6
ExampleSpeculative Compiler Optimization
void foo(int *a, int *b){ for(i = 0; i < N; i++) { x = *a + 1; *b = x; }}
void foo_1(int *a, int *b){ x = *a + 1; *b = x; chk a != b}
Speculatea != b
void foo_2(int *a, int *b){ x = *a + N; *b = x; chk a == b}
Speculatea == b
7
ExampleThread Level Speculation (TLS)
To take advantage of data speculation support,
a probability metric and a cost function are required.
int *vector_add(int *a, int *b){ for(i = 0; i < N; i++) { a[i] = a[i] + b[i]; } return a;}
Can this loop be parallelized?
8
Probabilistic Pointer Analysis (PPA)
Statically decide for any pair of pointers, at any point in the program, whether two pointers point to the same memory location.
*A = ~ ~ = *B
*A = ~ ~ = *B
This problem is known to be undecidable. [Landi 1992]
Definitely Not
Definitely
Maybe
Typically, ‘Maybe’ is
treated like
‘Definitely’
Probability = 0.0
Probability = 1.0
0.0 < p < 1.0
Statically estimate for any pair of pointers, at any point in the program, the probability that two pointers point to the same memory location.
Isn’t this problem even worse?
9
Outline
PPA ObjectivesProbabilistic Pointer Analysis (PPA)
Theory The Probabilistic Points-To Graph The Points-To Matrix The Transformation Matrix
An ExampleSome Preliminary Results
10
How is Pointer Analysis used?
ExecutableSource Code
OptimizingCompiler
DependenceAnalysis
PointerAnalysis
11
Traditional Points-To Graph
int a, b, c;int *r, *s, *t;int **x, **y, **z;
r s t
x y z
a b c
12
Traditional Points-To Graph
int a, b, c;int *r, *s, *t;int **x, **y, **z;
x=&r; y=&s; z=&t; r=&a; s=&b; z=&c;
r s t
x y z
a b c
13
Traditional Points-To Graph
int a, b, c;int *r, *s, *t;int **x, **y, **z;
x=&r; y=&s; z=&t; r=&a; s=&b; z=&c;
if(…) { x=&s; s=&c;}
r s t
x y z
a b c
14
Traditional Points-To Graph
int a, b, c;int *r, *s, *t;int **x, **y, **z;
x=&r; y=&s; z=&t; r=&a; s=&b; z=&c;
if(…) { x=&s; s=&c;}r=&b; z=&r;
r s t
x y z
a b c
15
Traditional Points-To Graph
int a, b, c;int *r, *s, *t;int **x, **y, **z;
x=&r; y=&s; z=&t; r=&a; s=&b; z=&c;
if(…) { x=&s; s=&c;}r=&b; z=&r;
if(…) y = x;
r s t
x y z
a b c
16
Traditional Points-To Graph
int a, b, c;int *r, *s, *t;int **x, **y, **z;
x=&r; y=&s; z=&t; r=&a; s=&b; z=&c;
if(…) { x=&s; s=&c;}r=&b; z=&r;
if(…) y = x;
*x = &a;
r s t
x y z
a b c
17
How is PPA used?
SpeculativeExecutable
Source Code
OptimizingCompiler
ProbabilisticDependence
Analysis
ProbabilisticPointer
Analysis
Control FlowEdge
Profiling(Optional)
with Data Speculation
Support
18
Definition: Probability Analysis Let <p,v> denote a points-to relationship from a pointer p to a
location v.
At every static program point s there exists a probability function P(s, <p,v>) that denotes the probability that p points to v during dynamic program execution.
P(s, <p,v>) = D(s, <p,v>) / D(s)
Where D(s) is the number of times s is (expected to be) dynamically visited and D(s, <p,v>) is the number of times that the points-to relation <p,v> dynamically holds.
19
Algorithm should output a Safe Conservative may alias Probability
A probability of 0.0 [P(s,<p,v>) = 0.0] indicates that a points-to relation <p,v> will never hold. The converse in not necessarily true
A probability of 1.0 [P(s,<p,v>) = 1.0] indicates that a points to relation <p,v> will always hold. The converse is also not necessarily true: a dynamic
points-to relationship <p,v> that always exists may not be reported with a probability of 1.0
20
Probabilistic Points-To Graph
int a, b, c;int *r, *s, *t;int **x, **y, **z;
x=&r; y=&s; z=&t; r=&a; s=&b; z=&c;
r s t
x y z
a b c
1.0 1.0
1.0 1.0 1.0
1.0
21
Probabilistic Points-To Graph
int a, b, c;int *r, *s, *t;int **x, **y, **z;
x=&r; y=&s; z=&t; r=&a; s=&b; z=&c;
if(…) { /*60% taken*/
x=&s; s=&c;}
r s t
x y z
a b c
0.60.4
0.60.4
22
Probabilistic Points-To Graph
int a, b, c;int *r, *s, *t;int **x, **y, **z;
x=&r; y=&s; z=&t; r=&a; s=&b; z=&c;
if(…) { /*60% taken*/
x=&s; s=&c;}r=&b; z=&r;
r s t
x y z
a b c
0.60.4
0.6
0.4
23
Probabilistic Points-To Graph
int a, b, c;int *r, *s, *t;int **x, **y, **z;
x=&r; y=&s; z=&t; r=&a; s=&b; z=&c;
if(…) { /*60% taken*/
x=&s; s=&c;}r=&b; z=&r;
if(…) /*10% taken*/
y = x;
r s t
x
y
z
a b c
0.6
0.4
0.6
0.4
0.040.96
24
Probabilistic Points-To Graph
int a, b, c;int *r, *s, *t;int **x, **y, **z;
x=&r; y=&s; z=&t; r=&a; s=&b; z=&c;
if(…) { /*60% taken*/
x=&s; s=&c;}r=&b; z=&r;
if(…) /*10% taken*/
y = x;
*x = &a;
r s t
xy
z
a b c
0.6
0.4
0.96
0.04
0.6
0.40.4
0.60.6
0.240.16
What is the probability that **y points to a?
25
Our PPA Algorithm Objectives
An Interprocedural, Flow Sensitive,
Context Sensitive approach that uses Linear Transfer Functions.
Must be scalable in time and space.
Provides an approximate probability for the ‘Maybe’ output.
26
BDD based
Accuracy/Efficiency Tradeoff
• Linear Time Complexity• Inaccurate - many false ‘maybe’ outputs• Memory Required: Negligible
• Doubly Exponential• Accurate – very few ‘maybe’ outputs•Does not scale
PPA
• Polynomial Time Complexity• Inaccurate – many false ‘maybe’ outputs, but provides probability metric
SPAN
Chen, et al: Only Other PPA
Steensgaard
Address-taken
Emami
Anderson
27
Encoding the Probabilistic Points-To Graph
The Points-To Matrix The Probabilistic Points-To graph is encoded
using a sparse Markov Matrix All elements are real numbers in the closed interval [0,1] All rows sum to 1.0
Each pointer set and location set is given a unique id representing it’s matrix row & column
Rules for linearity: Pointers can only point to Location sets Location sets always point to themselves with
probability 1.0
28
Points-To Matrix Structure
Location Sets
AreaOf
Interestø
ø I
Pointer Sets
1
2
…
N-1
N
…
N-1 N 1 2 3 … P
oin
ter
Se
ts
Loca
tion
Set
s
29
Points-To Matrix Example
p
a
q
b
und
p aq b und
0.0 0.0 0.0 0.0 1.0
0.0 0.0 0.0 0.0 1.0
0.0 0.0 1.0 0.0 0.0
0.0 0.0 0.0 1.0 0.0
0.0 0.0 0.0 0.0 1.0
p
a
q
b und
p
a
q
b
und
p aq b und
0.0 0.0 0.0 0.0 1.0
0.0 0.0 0.0 0.0 1.0
0.0 0.0 1.0 0.0 0.0
0.0 0.0 0.0 1.0 0.0
0.0 0.0 0.0 0.0 1.0
int a, b, *p, *q;void main(){ p = &a; q = &b; foo();}
void foo(){ for(i = 0; i < 10; i++) { if(…) /* 50% chance taken */
q = &a; else p = q; }}
int a, b, *p, *q;void main(){ p = &a; q = &b; foo();}
void foo(){ for(i = 0; i < 10; i++) { if(…) q = &a; else p = q; }}
30
Another Points-To Matrix Example
p
a
q
b
und
p aq b und
0.0 0.0 1.0 0.0 0.0
0.0 0.0 0.5 0.46 0.04
0.0 0.0 1.0 0.0 0.0
0.0 0.0 0.0 1.0 0.0
0.0 0.0 0.0 0.0 1.0
p
a
q
b und
0.040.460.5
31
What about double pointers?
p
a
q
b und
0.040.460.5
p
a
*p
b
und
p a*p b und
0.0 0.0 0.0 1.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.5 0.46 0.04
0.0 0.0 0.0 0.0 0.5 0.46 0.04
0.0 0.0 0.0 1.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 1.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 1.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 1.0
q q
q
q
32
Basic Pointer Assignments
x = &y Address-of Assignment
x = y Copy Assignment
x = *y Load Assignment
*x = y Store Assignment
33
Transforming the points-to matrix
Let Xs represent the probabilistic points-to matrix at a specific program point s.
Basic pointer assignment instruction
XIN
XOUT
Claim: There exists a transformation function T(X) for every instruction i, such that XOUT = Ti(XIN).
34
The fundamental PPA Equation
Points-ToMatrix Out
Points-ToMatrix In
Transformation
Matrix=
35
Transformation Matrix Structure
1
2
N-1 N
ø I
1 2 3 …
…
N-1
N
…
Area of Interest
Location Sets Pointer Sets P
oin
ter
Se
ts
Loca
tion
Set
s
36
Example
int a, b, *p, *q;void main(){ p = &a; q = &b; foo()}
void foo(){ for(i = 0; i < 10; i++) { if(…) q = &a; else p = q; }}
p
a
q
b
und
p aq b und
0.0 0.0 0.0 0.0 1.0
0.0 0.0 0.0 0.0 1.0
0.0 0.0 1.0 0.0 0.0
0.0 0.0 0.0 1.0 0.0
0.0 0.0 0.0 0.0 1.0
37
Example
int a, b, *p, *q;void main(){ p = &a; q = &b; foo()}
0.0 0.0 0.0 0.0 1.0
0.0 0.0 0.0 0.0 1.0
0.0 0.0 1.0 0.0 0.0
0.0 0.0 0.0 1.0 0.0
0.0 0.0 0.0 0.0 1.0
0.0 0.0 1.0 0.0 0.0
0.0 1.0 0.0 0.0 0.0
0.0 0.0 1.0 0.0 0.0
0.0 0.0 0.0 1.0 0.0
0.0 0.0 0.0 0.0 1.0
38
Example
int a, b, *p, *q;void main(){ p = &a; q = &b; foo()}
0.0 0.0 0.0 0.0 1.0
0.0 0.0 0.0 0.0 1.0
0.0 0.0 1.0 0.0 0.0
0.0 0.0 0.0 1.0 0.0
0.0 0.0 0.0 0.0 1.0
0.0 0.0 1.0 0.0 0.0
0.0 1.0 0.0 0.0 0.0
0.0 0.0 1.0 0.0 0.0
0.0 0.0 0.0 1.0 0.0
0.0 0.0 0.0 0.0 1.0
p
a
q
b
und
p aq b und
0.0 0.0 1.0 0.0 1.0
0.0 0.0 0.0 0.0 1.0
0.0 0.0 1.0 0.0 0.0
0.0 0.0 0.0 1.0 0.0
0.0 0.0 0.0 0.0 1.0
=p
a
q
b und
39
Combining Transformation Matrices
XOUT = T2 T1 XIN
T2: Basic pointer assignment instruction
XIN
XOUT
T1: Basic pointer assignment instruction
40
Combining Transformation Matrices Exampleint a, b, *p, *q;void main(){ p = &a; q = &b; foo();}
0.0 0.0 1.0 0.0 0.0
0.0 1.0 0.0 0.0 0.0
0.0 0.0 1.0 0.0 0.0
0.0 0.0 0.0 1.0 0.0
0.0 0.0 0.0 0.0 1.0
41
Combining Transformation Matrices Exampleint a, b, *p, *q;void main(){ p = &a; q = &b; foo();}
0.0 0.0 1.0 0.0 0.0
0.0 1.0 0.0 0.0 0.0
0.0 0.0 1.0 0.0 0.0
0.0 0.0 0.0 1.0 0.0
0.0 0.0 0.0 0.0 1.0
1.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 1.0 0.0
0.0 0.0 1.0 0.0 0.0
0.0 0.0 0.0 1.0 0.0
0.0 0.0 0.0 0.0 1.0
42
Combining Transformation Matrices Exampleint a, b, *p, *q;void main(){ p = &a; q = &b; foo();}
0.0 0.0 1.0 0.0 0.0
0.0 1.0 0.0 0.0 0.0
0.0 0.0 1.0 0.0 0.0
0.0 0.0 0.0 1.0 0.0
0.0 0.0 0.0 0.0 1.0
1.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 1.0 0.0
0.0 0.0 1.0 0.0 0.0
0.0 0.0 0.0 1.0 0.0
0.0 0.0 0.0 0.0 1.0
0.001 0.01 0.989 0.0 0.0
0.0 0.001 0.999 0.0 0.0
0.0 0.0 1.0 0.0 0.0
0.0 0.0 0.0 1.0 0.0
0.0 0.0 0.0 0.0 1.0
43
Combining Transformation Matrices Example
0.0 0.0 1.0 0.0 0.0
0.0 1.0 0.0 0.0 0.0
0.0 0.0 1.0 0.0 0.0
0.0 0.0 0.0 1.0 0.0
0.0 0.0 0.0 0.0 1.0
1.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 1.0 0.0
0.0 0.0 1.0 0.0 0.0
0.0 0.0 0.0 1.0 0.0
0.0 0.0 0.0 0.0 1.0
=
0.001 0.01 0.989 0.0 0.0
0.0 0.001 0.999 0.0 0.0
0.0 0.0 1.0 0.0 0.0
0.0 0.0 0.0 1.0 0.0
0.0 0.0 0.0 0.0 1.0
0.0 0.0 0.99 0.01 0.0
0.0 0.0 0.999 0.001 0.0
0.0 0.0 1.0 0.0 0.0
0.0 0.0 0.0 1.0 0.0
0.0 0.0 0.0 0.0 1.0
44
Combining Transformation Matrices Exampleint a, b, *p, *q;void main(){ p = &a; q = &b; foo();}
0.0 0.0 0.99 0.01 0.0
0.0 0.0 0.999 0.001 0.0
0.0 0.0 1.0 0.0 0.0
0.0 0.0 0.0 1.0 0.0
0.0 0.0 0.0 0.0 1.0
45
Combining Transformation Matrices Exampleint a, b, *p, *q;void main(){ p = &a; q = &b; foo();}
0.0 0.0 0.0 0.0 1.0
0.0 0.0 0.0 0.0 1.0
0.0 0.0 1.0 0.0 0.0
0.0 0.0 0.0 1.0 0.0
0.0 0.0 0.0 0.0 1.0
p
a
q
b
und
p aq b und
0.0 0.0 .99 0.01 0.0
0.0 0.0 .999 0.001 0.0
0.0 0.0 1.0 0.0 0.0
0.0 0.0 0.0 1.0 0.0
0.0 0.0 0.0 0.0 1.0
=
0.0 0.0 0.99 0.01 0.0
0.0 0.0 0.999 0.001 0.0
0.0 0.0 1.0 0.0 0.0
0.0 0.0 0.0 1.0 0.0
0.0 0.0 0.0 0.0 1.0
p
a
q
b und
0.0010.99
0.9990.01
46
[ ]^10
How to handle Control Flow?
int a, b, *p, *q;void main(){ p = &a; q = &b; foo();}
void foo(){ for(i = 0; i < 10; i++) { if(…) q = &a; else p = q; }}
q = &a; < 9 >p = q;
0.50.5
4
32
1
TF_foo = 4 3 2 ] 1[0.5 +0.5 0.001 0.01 0.989 0.0 0.0
0.0 0.001 0.999 0.0 0.0
0.0 0.0 1.0 0.0 0.0
0.0 0.0 0.0 1.0 0.0
0.0 0.0 0.0 0.0 1.0
47
PPA Infrastructure
.spd files .spx files
EdgeProfile
PPA Results
MATLABDebugging
Script
Suif Infrastructure
ICFGAbstract Memory
Model (AMM)
Transfer FunctionBuilder
(Suif2TF)
MATLABC Library
TransformationMatrix
Collector [BU]PPA Matrix Builder
Points-To MatrixPropagator [TD]
48
Current Results
Spec2000 Total CPU Time (sec) (m:s) Mem Required (mB)
gzip 0.6 0:1 8.72
vpr 3 0:3 14.51
gcc 1380.59 23:1 376.69
mcf 0.14 0:0 5.84
crafty 4.05 0:4 34.01
parser 5.44 0:5 15.73
perlbmk 252.56 4:13 172.62
gap 142.12 2:22 76.29
vortex 98.55 1:39 61.83
bzip2 0.28 0:0 7.2
twolf 4.21 0:4 29.96
49
Current Results
Spec95 Total CPU Time (sec)
(min:sec) Mem Required (mB)
go 3.2 0:3 28.31
m88ksim 3.61 0:4 17.88
gcc 905.59 15:6 346.08
compress 0.05 0:0 5.32
li 10.61 0:11 14.21
ijpeg 5.26 0:5 18.88
perl 26.02 0:26 94.18
vortex 99.79 1:40 61.83
50
The End… Any Comments or Questions?
51
References Peng-Sheng Chen, Ming-Yu Hung, Yuan-Shin Hwang, Roy Dz-Ching Ju, Jenq Kuen Lee.
Compiler support for speculative multithreading architecture with probabilistic points-to analysis PPOPP 2003: 25-36
Only other PPA research Group
Jin Lin, Tong Chen, Wei-Chung Hsu, Peng-Chung Yew, Roy Dz-Ching Ju, Tin-Fook Ngai and Sun Chan, “A Compiler Framework for Speculative Analysis and Optimizations,” in Proceedings of the ACM SIGPLAN 2003 Conference on Programming Language Design and Implementation, San Diego, California, June 9-11, 2003, pp. 289-299
Roy Dz-Ching Ju. Probabilistic Memory Disambiguation and its Application to Data Speculation (1996)
Probabilistic array subscript analysis
Manel Fernandez and Roger Espasa. Speculative Alias Analysis for Executable Code (2002)
52
BACKUPSLIDES
53
Does Probabilistic Aliasing Exist?
Spec95 Normalized Dependence Frequency Analysis
0
20
40
60
80
100
< 0.1 < 0.2 < 0.3 < 0.4 < 0.5 < 0.6 < 0.7 < 0.8 < 0.9 < 1.0 ==1
Probabity Range
% o
f Dep
ende
ncie
s
cc1*
compress95
go
ijpeg
m88ksim
vortex*
54
Does Probabilistic Aliasing Exist?
Spec2000 Normalized Dependence Frequency Analysis
0
20
40
60
80
100
< 0.1 < 0.2 < 0.3 < 0.4 < 0.5 < 0.6 < 0.7 < 0.8 < 0.9 < 1.0 ==1
Probabity Range
% o
f Dep
ende
ncie
s
bzip2
crafty
gzip
mcf
vpr
55
Does Probabilistic Aliasing Exist?
AVG Normalized Dependence Frequency Analysis
0
20
40
60
80
100
< 0.1 < 0.2 < 0.3 < 0.4 < 0.5 < 0.6 < 0.7 < 0.8 < 0.9 < 1.0 ==1
Probabity Range
% o
f Dep
ende
ncie
s
AVG
56
Does Probabilistic Aliasing Exist?
Spec95 Normalized & Weighted Dependence Frequency Analysis
0
20
40
60
80
100
< 0.1 < 0.2 < 0.3 < 0.4 < 0.5 < 0.6 < 0.7 < 0.8 < 0.9 < 1.0 ==1
Probabity Range
% o
f Dep
ende
ncie
s cc1*
compress95
go
ijpeg
m88ksim
vortex*
57
Does Probabilistic Aliasing Exist?
Spec2000 Normalized & Weighted Dependence Frequency Analysis
0
20
40
60
80
100
< 0.1 < 0.2 < 0.3 < 0.4 < 0.5 < 0.6 < 0.7 < 0.8 < 0.9 < 1.0 ==1
Probabity Range
% o
f Dep
ende
ncie
s
bzip2
crafty
gzip
mcf
vpr
58
Does Probabilistic Aliasing Exist?
AVG Weighted vs Not Weighted Dependence Frequency Analysis
0
20
40
60
80
100
< 0.1 < 0.2 < 0.3 < 0.4 < 0.5 < 0.6 < 0.7 < 0.8 < 0.9 < 1.0 ==1
Probabity Range
% o
f Dep
ende
ncie
s
Avg Weighted
Avg Not Weighted
59
Probabilistic Dependence Matrix
Compress95Ref input setFlow Sensitive AnalysisLocation Oriented DDA85x85 static lod/str pairs
srcsnk
60
Pointer Analysis Issues
Scalability vs. Accuracy Generally, a ‘difficult’ tradeoff exists between:
the amount of computation and memory required vs. the accuracy of the analysis.
Precision/Efficiency tradeoff, where is the sweet spot?
Which metric should be used? Direct metric Report performance applied to an optimization Dynamically measure false positives
Which benchmark suite? Are the results reproducible?
61
Pointer Analysis Issues
Complications associated with pointer arithmetic, casting, function pointers, long jumps, and multithreaded applications. Can these be ignored?
Different pointer analysis uses have different needs. A universal pointer analysis probably doesn’t exist.
62
Optimizing Compilers 101 Optimizing compilers must preserve program
correctness All compiler algorithms (code transformations) are
developed within this rule
What if this rule was relaxed? If not bound by this rule of program correctness, the
opportunity constitutes a reevaluation of all optimizations that were originally created within this rule.
If given a framework for speculation recovery (hardware or software), this rule becomes flexible.
63
Compiler Support for Speculation
Control Speculation Executing instructions before determining that they
will execute in the normal flow of execution. Existing compiler frameworks can adequately
incorporate and exploit control speculation using control flow profiling or simple heuristic rules.
Data speculation Executing loads before potentially dependant stores Very little has been done to effectively exploit data
speculation. Data dependence profiling is expensive No effective heuristics exist
64
Pointer Analysis Pointer analysis is a critical compiler component
used to analyze programs written in C-like programming languages, which utilize pointers and pointer-based data structures
It attempts to disambiguate indirect memory references, so that subsequent compiler passes have a more accurate view of program behaviour.
65
How is Pointer Analysis used?
void foo(int *a, int *b) { for(i=1; i<N; i++) { a[i] = b[i] / 13; }
for(i=1; i<N; i++) { *a = *b + 1; }}
Parallelizing Compiler Can the loop be parallelized? (TLP)
Guide Compiler Optimizations load/store redundancy elimination, register allocation, CSE, dead code elimination, live variable, instruction scheduling [eg. VLIW] (ILP), loop invariant code motion, etc.
Behavioral Synthesis &Data Flow Processors Necessary for partitioning and instruction scheduling.
Error Detection & Program UnderstandingProgrammer can detect errors or discover poorly written code.
66
Pointer Analysis Design Choices
Flow/Path sensitivityContext sensitivityHeap modelingAggregate modelingAlias representationWhole ProgramIncremental Compilation
67
How is Pointer Analysis used?
void foo(int *a, int *b) {
for(i=1; i<N; i++) a[i] = b[i-1];
for(i=1; i<N; i++) { *a = *b + 1; }
}
Parallelizing Compiler Can the loop be parallelized? (TLP)
Guide Compiler Optimizations load/store redundancy elimination, register allocation, CSE, dead code elimination, live variable, instruction scheduling [eg. VLIW] (ILP), loop invariant code motion, etc.
Behavioral Synthesis &Data Flow Processors Necessary for partitioning and instruction scheduling.
Error Detection & Program UnderstandingProgrammer can detect errors or discover poorly written code.
Any more?
68
Pointer Analysis Published by Year
Haven’t we Solved this Problem Yet?
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