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Optimizing Compilers CISC 673 Spring 2009 Control Flow. John Cavazos University of Delaware. Control-Flow Analysis. Motivating example: identifying loops majority of runtime focus optimization on loop bodies! remove redundant code, replace expensive operations ) speed up program

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Optimizing Compilers CISC 673 Spring 2009 Control Flow

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Optimizing CompilersCISC 673Spring 2009Control Flow

John Cavazos

University of Delaware

Control-Flow Analysis

• Motivating example: identifying loops

• majority of runtime

• focus optimization on loop bodies!

• remove redundant code, replace expensive operations ) speed up program

• Finding loops:

• easy…

i = 1; j = 1; k = 1;

A1: if i > 1000 goto L1;

A2: if j > 1000 goto L2;

A3: if k > 1000 goto L3;

do something

k = k + 1; goto A3;

L3: j = j + 1; goto A2;

L2: i = i + 1; goto A1;

L1: halt

for i = 1 to 1000

for j = 1 to 1000

for k = 1 to 1000

do something

• or harder(GOTOs)

Steps to Finding Loops

• Identify basic blocks

• Build control-flow graph

• Analyze CFG to find loops

Control-Flow Graphs

• Control-flow graph:

• Node: an instruction or sequence of instructions (a basic block)

• Two instructions i, j in same basic blockiff execution of i guarantees execution of j

• Directed edge: potential flow of control

• Distinguished start node Entry

• First instruction in program

Identifying Basic Blocks

• Input: sequence of instructions instr(i)

• Identify leaders:first instruction of basic block

• Iterate: add subsequent instructions to basic block until we reach another leader

Basic Block Partition Algorithm

for i = 1 to |n|// iterate thru all instrs

if instr(i) is a branch

While worklist notempty

x = first instruction in worklist

worklist = worklist – {x}

block(x) = {x}

for (i = x + 1; i <= |n| && i notin leaders; i++)

block(x) = block(x) ∪ {i}

Class Example

for i = 1 to |n|

if instr(i) is a branch

∪ targets of instr(i)

While worklist notempty

x = first instruction in worklist

worklist = worklist – {x}

block(x) = {x}

for (i = x + 1; i <= |n| && i notin

block(x) = block(x) ∪ {i}

A = 4

t1 = A * B

L1: t2 = t1/C

if t2 < W goto L2

M = t1 * k

t3 = M + I

L2: H = I

M = t3 – H

if t3 >= 0 goto L4

L3: goto L1

L4: goto L3

Basic Block Example

• A = 4

• t1 = A * B

• L1: t2 = t1/C

• if t2 < W goto L2

• M = t1 * k

• t3 = M + I

• L2: H = I

• M = t3 – H

• if t3 >= 0 goto L4

• L3: goto L1

• L4: goto L3

Basic blocks

Control-Flow Edges

• Basic blocks = nodes

• Edges:

• Add directed edge between B1 and B2 if:

• BRANCH from last statement of B1 to first statement of B2 (B2 is a leader), or

• B2 immediately follows B1 in program order and B1 does NOT end with unconditional branch (goto)

Control-Flow Edge Algorithm

Input: block(i), sequence of basic blocks

Output: CFG where nodes are basic blocks

for i = 1 to the number of blocks

x = last instruction of block(i)

if instr(x) is a branch

for each target y of instr(x),

create edge block i to block y

if instr(x) is not unconditional branch,

create edge block i to block i+1

CFG Edge Example

A

• A = 4

• t1 = A * B

• L1: t2 = t1/C

• if t2 < W goto L2

• M = t1 * k

• t3 = M + I

• L2: H = I

• M = t3 – H

• if t3 >= 0 goto L4

• L3: goto L1

• L4: goto L3

B

Basic blocks

C

D

E

F

Steps to Finding Loops

• Identify basic blocks

• Build control-flow graph

• Analyze CFG to find loops

• Spanning trees, depth-first spanning trees

• Reducibility

• Dominators

• Dominator tree

• Strongly-connected components

Spanning Tree

• Build a tree containing every node and some edges from CFG

A

procedure Span (v)

for w in Succ(v)

if not InTree(w)

add w, v→ w to ST

InTree(w) = true

Span(w)

for v in V do inTree = false

InTree(root) = true

Span(root)

B

C

D

E

F

CFG Edge Classification

Tree edge:

in CFG & ST

(v,w) not tree edge but w is descendant of v in ST

Back edge:

(v,w): v=w or w is proper ancestor of v in ST

Cross edge:

(v,w): w neither ancestor nor descendant of v in ST

A

B

C

loop

D

E

F

Depth-first spanning tree

procedure DFST (v)

pre(v) = vnum++

InStack(v) = true

for w in Succ(v)

if not InTree(w)

InTree(w) = true

DFST(w)

else if pre(v) < pre(w)

else if InStack(w)

else

InStack(v) = false

for v in V do inTree(v) = false

vnum = 0

InTree(root)

DFST(root)

A

1

B

2

C

3

D

4

E

F

5

6

Class Problem: Identify Edges

procedure DFST (v)

pre(v) = vnum++

InStack(v) = true

for w in Succ(v)

if not InTree(w)

InTree(w) = true

DFST(w)

else if pre(v) < pre(w)

else if InStack(w)

else

InStack(v) = false

for v in V do inTree(v) = false

vnum = 0

InTree(root)

DFST(root)

Compiler Optimizations I

• Unrolling and other loop optimizations for improving instruction level parallelism (ILP)

IDEA 1: Predict Best Unrolling Factor

IDEA 2: Implement Different Loop Opt

Reversal, Interchange, Fusion, Fission

Compiler Optimizations II

• Policies for method inlining

Question 1: What Method Characteristics are most important for Inlining?

Question 2: What is the impact of Inlining to Register Allocation?

Compiler Optimizations III

• Escape analysis

IDEA 1 : Move object access that do not “escape” method into registers.

IDEA 2: Allocate objects that do not “escape” on the stack.

Compiler Optimizations IV

• Improving virtual memory behavior

IDEA 1: Performance study of optimizations on virtual memory and/or parts of memory hierarchy (e.g., L3 cache). Use tool to analyze memory behavior.

Compiler Optimizations V

• Class analysis for safe object inlining

• Policies for object inlining

IDEA 1: Implement object inlining optimization.

Compiler Optimizations VI

• Field Reordering

• Graph Coloring RA

• Porting of old code

• Instruction Scheduling for X86

• Porting PowerPC version to X86

• Autotuning Java applications

• Interesting!

Next Time

• Reducibility

• Dominance

• Wikipedia: Dominator (graph_theory)

• Some Dataflow

• T.J. Marlowe and B.G. Ryder Properties of Data Flow Frameworks, pp. 121-163, ACTA Informatica, 28, 1990.