Optimizing Compilers CISC 673 Spring 2009 Control Flow

UUNIVERSITYNIVERSITY OFOF D DELAWARE ELAWARE • • C COMPUTER & OMPUTER & IINFORMATION NFORMATION SSCIENCES CIENCES DDEPARTMENTEPARTMENT

Optimizing CompilersCISC 673

Spring 2009Control Flow

John CavazosUniversity of Delaware

UUNIVERSITYNIVERSITY OFOF D DELAWARE ELAWARE • • C COMPUTER & OMPUTER & IINFORMATION NFORMATION SSCIENCES CIENCES DDEPARTMENTEPARTMENT 2

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… for i = 1 to 1000

for j = 1 to 1000 for k = 1 to 1000 do something

1 i = 1; j = 1; k = 1;2 A1: if i > 1000 goto L1;3 A2: if j > 1000 goto L2;4 A3: if k > 1000 goto L3;5 do something6 k = k + 1; goto A3;7 L3: j = j + 1; goto A2;8 L2: i = i + 1; goto A1;9 L1: halt

or harder(GOTOs)


Steps to Finding Loops

1. Identify basic blocks2. Build control-flow graph3. 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

leaders = instr(1) // first instruction

for i = 1 to |n| // iterate thru all instrsif instr(i) is a branch

leaders = leaders ∪ targets of instr(i)leaders = leaders ∪ instr(i+1) // instr after branch

worklist = leadersWhile worklist not empty

x = first instruction in worklistworklist = worklist – {x}block(x) = {x}for (i = x + 1; i <= |n| && i not in leaders; i++)

block(x) = block(x) ∪ {i}


Class Exampleleaders = instr(1)for i = 1 to |n|

if instr(i) is a branchleaders = leaders

∪ targets of instr(i)leaders = leaders ∪ instr(i+1)

worklist = leadersWhile worklist not empty

x = first instruction in worklistworklist = worklist – {x}block(x) = {x}for (i = x + 1; i <= |n| && i not in

leaders; i++)block(x) = block(x) ∪ {i}

1 A = 42 t1 = A * B3 L1: t2 = t1/C4 if t2 < W goto L25 M = t1 * k6 t3 = M + I7 L2: H = I8 M = t3 – H9 if t3 >= 0 goto L410 L3: goto L111 L4: goto L3


Basic Block Example

Leaders

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 blocksOutput: CFG where nodes are basic blocks

for i = 1 to the number of blocksx = 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

Leaders

Basic blocks



Steps to Finding Loops

1. Identify basic blocks2. Build control-flow graph3. 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

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 = falseInTree(root) = trueSpan(root)

A

B

C

D

E F


CFG Edge Classification

Tree edge:in CFG & ST

Advancing edge:(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

D

E F

loop


Depth-first spanning treeprocedure DFST (v) pre(v) = vnum++ InStack(v) = true for w in Succ(v) if not InTree(w) add v→w to TreeEdges InTree(w) = true DFST(w) else if pre(v) < pre(w) add v→w to AdvancingEdges else if InStack(w) add v→w to BackEdges else add v→w to CrossEdges InStack(v) = false

for v in V do inTree(v) = false vnum = 0InTree(root)DFST(root)

A

B

C

D

E F

1

2

3

4

5 6


Class Problem: Identify Edgesprocedure DFST (v) pre(v) = vnum++ InStack(v) = true for w in Succ(v) if not InTree(w) add v→w to TreeEdges InTree(w) = true DFST(w) else if pre(v) < pre(w) add v→w to AdvancingEdges else if InStack(w) add v→w to BackEdges else add v→w to CrossEdges InStack(v) = false

for v in V do inTree(v) = false vnum = 0InTree(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.

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