Intermediate representation

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "Intermediate representation"

Transcription

1 Intermediate representation Goals: encode knowledge about the program facilitate analysis facilitate retargeting facilitate optimization scanning parsing HIR semantic analysis HIR intermediate code gen. LIR optim LIR code gen. 1

2 Intermediate representation Components Issues code representation symbol table analysis information string table Use an existing IR or design a new one? How close should it be to the source/target? 2

3 IR selection Using an existing IR cost savings due to reuse it must be expressive and appropriate for the compiler operations Designing an IR decide how close to machine code it should be decide how expressive it should be decide its structure consider combining different kinds of IRs 3

4 IR classification: Level High-level closer to source language used early in the process usually converted to lower form later on Example: AST 4

5 IR classification: Level Medium-level try to reflect the range of features in the source language in a language-independent way most optimizations are performed at this level algebraic simplification copy propagation dead-code elimination common subexpression elimination loop-invariant code motion etc. 5

6 IR classification: Level Low-level very close to target-machine instructions architecture dependent useful for several optimizations loop unrolling branch scheduling instruction/data prefetching register allocation etc. 6

7 IR classification: Level for i := op1 to op2 step op3 instructions endfor High-level i := op1 if step < 0 goto L2 L1: if i > op2 goto L3 instructions i := i + step goto L1 L2: if i < op2 goto L3 instructions i := i + step goto L2 L3: Medium-level 7

8 IR classification: Structure Graphical Linear Hybrid Trees, graphs Not easy to rearrange Large structures Looks like pseudocode Easy to rearrange Combine graphical and linear IRs Example: low-level linear IR for basic blocks, and graph to represent flow of control 8

9 Graphical IRs Parse tree Abstract syntax tree High-level Useful for source-level information Retains syntactic structure Common uses source-to-source translation semantic analysis syntax-directed editors 9

10 Graphical IRs Tree, for basic block* Uses: root: operator up to two children: operands can be combined algebraic simplifications may generate locally optimal code. gt, t2 add, t1 0 L1: i := 2 assgn, i add, t1 gt, t2 t1:= i+1 t2 := t1>0 if t2 goto L1 2 i 1 t1 0 assgn, i 1 2 *straight-line code with no branches or branch targets. 10

11 Graphical IRs Directed acyclic graphs (DAGs) Like compressed trees leaves: variables, constants available on entry internal nodes: operators annotated with variable names? distinct left/right children Used for basic blocks (DAGs don't show control flow) Can generate efficient code. Note: DAGs encode common expressions But difficult to transform Good for analysis 11

12 Graphical IRs Generating DAGs Check whether an operand is already present if not, create a leaf for it Check whether there is a parent of the operand that represents the same operation if not create one, then label the node representing the result with the name of the destination variable, and remove that label from all other nodes in the DAG. 12

13 Graphical IRs Directed acyclic graphs (DAGs) Example m := 2 * y * z n := 3 * y * z p := 2 * y - z 13

14 Graphical IRs Control flow graphs (CFGs) Each node corresponds to a basic block, or part of a basic block, or may need to determine facts at specific points within BB a single statement more space and time Each edge represents flow of control 14

15 Graphical IRs Dependence graphs : they represents constraints on the sequencing of operations Dependence = a relation between two statements that puts a constraint on their execution order. Control dependence Based on the program's control flow. Data dependence Based on the flow of data between statements. Nodes represent statements Edges represent dependences Labels on the edges represent types of dependences Built for specific optimizations, then discarded 15

16 Graphical IRs Dependence graphs Example: s1 a := b + c s2 if a>10 goto L1 s3 d := b * e s4 e := d + 1 s5 L1: d := e / 2 s1 s2 s3 s4 s5 control dependence: s3 and s4 are executed only when a<=10 true or flow dependence: s2 uses a value defined in s1 This is read-after-write dependence antidependence: s4 defines a value used in s3 This is write-after-read dependence output dependence: s5 defines a value also defined in s3 This is write-after-write dependence input dependence: s5 uses a value also uses in s3 This is read-after-read situation. It places no constraints in the execution order, but is used in some optimizations. 16

17 Basic blocks Basic block = a sequence of consecutive statements in which flow of control enters at the beginning and leaves at the end without halt or possibility of branching except at the end. Partitioning a sequence of statements into BBs 1. Determine leaders (first statements of BBs) The first statement is a leader The target of a conditional is a leader A statement following a branch is a leader 2. For each leader, its basic block consists of the leader and all the statements up to but not including the next leader. 17

18 Basic blocks unsigned int fibonacci (unsigned int n) { unsigned int f0, f1, f2;; f0 = 0;; f1 = 1;; if (n <= 1) return n;; for (int i=2;; i<=n;; i++) { f2 = f0+f1;; f0 = f1;; f1 = f2;; } return f2;; } read(n) f0 := 0 f1 := 1 if n<=1 goto L0 i := 2 L2: if i<=n goto L1 return f2 L1: f2 := f0+f1 f0 := f1 f1 := f2 i := i+1 go to L2 L0: return n 18

19 Basic blocks Leaders: entry read(n) f0 := 0 f1 := 1 if n<=1 goto L0 i := 2 L2: if i<=n goto L1 return f2 L1: f2 := f0+f1 f0 := f1 f1 := f2 i := i+1 go to L2 L0: return n i := 2 i<=n f2 := f0+f1 f0 := f1 f1 := f2 i := i+1 read(n) f0 := 0 f1 := 1 n <= 1 return f2 return n exit 19

20 Linear IRs Sequence of instructions that execute in order of appearance Control flow is represented by conditional branches and jumps Common representations stack machine code three-address code 20

21 Linear IRs Stack machine code Assumes presence of operand stack Useful for stack architectures, JVM Operations typically pop operands and push results. Advantages Easy code generation Compact form Disadvantages Difficult to rearrange Difficult to reuse expressions 21

22 Linear IRs Three-address code Compact Generates temp variables Level of abstraction may vary Loses syntactic structure Quadruples operator up to two operands destination Triples similar to quadruples but the results are not named explicitly (index of operation is implicit name) Implement as table, array of pointers, or list 22

23 Linear IRs L1: i := 2 t1:= i+1 t2 := t1>0 if t2 goto L1 Quadruples (1) 2 (2) i st (1) (3) i + 1 (4) (3) > 0 (5) if (4), (1) Triples 23

24 SSA form Static Single Assignment Form Encodes information about data and control flow Two constraints: Each definition has a unique name Each use refers to a single definition all uses reached by a definition are renamed accordingly Advantages: Simplifies data flow analysis & several optimizations SSA size is linear to program size Eliminates certain dependences (write-after-read, writeafter-write) Example: x := 5 x := x +1 y := x * 2 x 0 := 5 x 1 := x 0 +1 y 0 := x 1 * 2 24

25 SSA form Consider a situation where two control-flow paths merge (e.g. due to a loop, or an if-statement) read(x) if ( x > 0) y:= 5 else y:=10 x := y read(x) x > 0 y := 5 y := 10 x := y read(x 0 ) x 0 > 0 y 0 := 5 y 1 := 10 x 1 := y should this be y 0 or y 1? 25

26 SSA form The compiler inserts special join functions (called ϕ- functions) at points where different control flow paths meet. read(x 0 ) x 0 > 0 y 0 := 5 y 1 := 10 y 2 = ϕ(y 0, y 1 ) x 1 := y 2 ϕ is not an executable function! If we do need to generate executable code from this form, we insert appropriate copy statements in the predecessors: y 0 := 5 y 1 := 10 y 2 := y 0 y 2 := y 1 x 1 := y 2 26

27 SSA form Example 2: x := 0 i := 1 while (i<10) x := x+i i := i+1 x := 0 i := 1 i < 10 x 0 := 0 i 0 := 1 x 1 := ϕ(x 0, x 2 ) i 1 := ϕ(i 0, i 2 ) i 1 < 10 x := x + 1 i := i + 1 exit x 2 := x i 2 := i exit 27

28 SSA form B2 B1 B3 x 0 := 0 i 0 := 1 x 1 := ϕ(x 0, x 2 ) i 1 := ϕ(i 0, i 2 ) i 1 < 10 x 2 := x i 2 := i exit When and where do we add ϕ functions? Intuition: There is a definition of x in B1 and another in B2 There is a path from B1 to B3 There is a path from B2 to B3 B3 is the first "common" node in these two paths. A ϕ function for x should be placed in B3. 28

29 SSA form A program is in SSA form if Each variable is assigned a value in exactly one statement Each use of a variable is dominated by the definition. Domination Node A dominates node B if every path from the flow graph entry to B goes through A Every node dominates itself Intuition: control will have to go through A in order to reach B Dominators are useful in identifying loops and in computing the SSA form. 29

30 Dominators A node N may have several dominators, but one of them will be closest to N and be dominated by all other dominators of N. That node is called the immediate dominator of N. The dominator tree is a data structure that shows the dominator relationships of a control flow graph. Each node in the tree is the immediate dominator of its children. 30

31 entry B2 B1 B3 B4 The dominator tree shows the dominator relation: each node in the tree is the immediate dominator of its children. Example: B7 is dominated by B1, B3, and B4, but its immediate (closest) dominator is B4 Note: B5 does not dominate B7 because we can go from the entry to B7 through the B6 path. B1 B5 B6 B2 B3 B7 B8 B4 B5 B6 B7 B9 exit B10 B9 B8 B10 31

32 Dominators and loops We can use dominators to identify the loops in a flow graph: Natural Loop = A set of basic blocks with a single entry point called the header, which dominates all other blocks in the set, and at least one way to iterate (i.e. go back to the header) Loop-finding algorithm: Find an edge B A where A dominates B. This is called a back-edge. Add A and B to the loop Find all nodes that can reach B without going through A. Add them to the loop. 32

33 entry Loops B2 B1 back edge: B9 B1 loop: {B9, B8, B7, B10, B6, B5, B4, B3, B2, B1} B3 back edge: B10 B7 loop: {B10, B8, B7} B4 B5 B6 back edge: B8 B3 loop: {B8, B7, B10, B6, B5, B4, B3 } B7 back edge: B7 B4 loop: {B7, B10, B6, B5, B8, B4} B9 B8 B10 back edge: B4 B3 loop: {B4, B7, B10, B8, B6, B5, B3} exit 33

34 Nested Loops Loop K is nested within loop L if L and K have different headers, λ and κ respectively, and λ is dominated by κ 34

35 Dominators and SSA form Dominators can be used in a more efficient computation of the SSA form. Node A strictly dominates node B if A dominates B and A B. The dominance frontier of a node B is the set of all nodes X such that B does not strictly dominate X B dominates a predecessor of X Intuition: The nodes in the dominance frontier of B are destinations of the edges that leave an area dominated by B. Since those destinations are not dominated by B, there must be some other path from the entry to them. Therefore, they must be convergence points, and we need to place ϕ functions in them for any variables defined in B. Y is a successor of X if there is an edge X Y in the flow graph. 35

36 Dominance frontier B0 B2 dominates B3, B4, and B5. B1 B2 does not dominate B6 or B8, but does dominate their predecessors. B11 B3 B2 B4 B7 B6 and B8 are the dominance frontier of B2. The paths that converge to B8 have been marked. B5 B8 B9 For each variable definition in B2, insert a ϕ function in B6 and in B8. B6 B10 36

37 Dominance frontier The dominance frontier of a node B can be computed as follows: DF(B) = DF local (B) U where: U C DF up (C) C is the set of children of B in the dominator tree DF up (C) is the set of nodes in the dominance frontier of C, that are not strictly dominated by C's immediate dominator DF local (B) are the successors of B that are not strictly dominated by B. 37

38 Dominance frontier algorithm compute the dominator tree for each X in a bottom-up traversal of the tree, do DF(X) := { } for each Y Succ (X) do if (idom (Y) X) DF local then DF(X) := DF(X) {Y} end for for each Z in children(x) do for each W DF(Z) do if (idom (W) X) then DF(X) := DF(X) {Y} DF up end for end for end for 38

39 entry 1 entry exit {ex} {ex} 6 8 {8} {8} {2, ex} {6} {6} {8} {2, ex} {11} 1 1 {9, 2, ex} 1 2 exit 1 2 {local, up} {2, ex} 39

40 entry B0 B1 entry B0 {} B2 B11 B1 B6 exit B11 B3 B4 B7 {B6} {B6, exit} {exit} B5 B8 B9 B8 B2 B7 {B8, B6} {B10} {B8, B10} B10 {exit} B6 B10 B3 B4 B5 B9 exit {B5} {B8} {B6} {B10} 40

6. Intermediate Representation

6. Intermediate Representation 6. Intermediate Representation Oscar Nierstrasz Thanks to Jens Palsberg and Tony Hosking for their kind permission to reuse and adapt the CS132 and CS502 lecture notes. http://www.cs.ucla.edu/~palsberg/

More information

Intermediate Representations. Reading & Topics. Intermediate Representations CS2210

Intermediate Representations. Reading & Topics. Intermediate Representations CS2210 Intermediate Representations CS2210 Lecture 11 Reading & Topics Muchnick: chapter 6 Topics today: Intermediate representations Automatic code generation with pattern matching Optimization Overview Control

More information

Control Flow Analysis & Def-Use. Hwansoo Han

Control Flow Analysis & Def-Use. Hwansoo Han Control Flow Analysis & Def-Use Hwansoo Han Control Flow Graph What is CFG? Represents program structure for internal use of compilers Used in various program analyses Generated from AST or a sequential

More information

Data Structures and Algorithms in Compiler Optimization. Comp314 Lecture Dave Peixotto

Data Structures and Algorithms in Compiler Optimization. Comp314 Lecture Dave Peixotto Data Structures and Algorithms in Compiler Optimization Comp314 Lecture Dave Peixotto 1 What is a compiler Compilers translate between program representations Interpreters evaluate their input to produce

More information

Principles of Compiler Design

Principles of Compiler Design Principles of Compiler Design Intermediate Representation Compiler Lexical Analysis Syntax Analysis Semantic Analysis Source Program Token stream Abstract Syntax tree Unambiguous Program representation

More information

Middle End. Code Improvement (or Optimization) Analyzes IR and rewrites (or transforms) IR Primary goal is to reduce running time of the compiled code

Middle End. Code Improvement (or Optimization) Analyzes IR and rewrites (or transforms) IR Primary goal is to reduce running time of the compiled code Traditional Three-pass Compiler Source Code Front End IR Middle End IR Back End Machine code Errors Code Improvement (or Optimization) Analyzes IR and rewrites (or transforms) IR Primary goal is to reduce

More information

An Overview of GCC Architecture (source: wikipedia) Control-Flow Analysis and Loop Detection

An Overview of GCC Architecture (source: wikipedia) Control-Flow Analysis and Loop Detection An Overview of GCC Architecture (source: wikipedia) CS553 Lecture Control-Flow, Dominators, Loop Detection, and SSA Control-Flow Analysis and Loop Detection Last time Lattice-theoretic framework for data-flow

More information

Advanced Compilers CMPSCI 710 Spring 2003 Computing SSA

Advanced Compilers CMPSCI 710 Spring 2003 Computing SSA Advanced Compilers CMPSCI 70 Spring 00 Computing SSA Emery Berger University of Massachusetts, Amherst More SSA Last time dominance SSA form Today Computing SSA form Criteria for Inserting f Functions

More information

Topic I (d): Static Single Assignment Form (SSA)

Topic I (d): Static Single Assignment Form (SSA) Topic I (d): Static Single Assignment Form (SSA) 621-10F/Topic-1d-SSA 1 Reading List Slides: Topic Ix Other readings as assigned in class 621-10F/Topic-1d-SSA 2 ABET Outcome Ability to apply knowledge

More information

Control Flow Analysis

Control Flow Analysis COMP 6 Program Analysis and Transformations These slides have been adapted from http://cs.gmu.edu/~white/cs60/slides/cs60--0.ppt by Professor Liz White. How to represent the structure of the program? Based

More information

ECE 5775 (Fall 17) High-Level Digital Design Automation. Static Single Assignment

ECE 5775 (Fall 17) High-Level Digital Design Automation. Static Single Assignment ECE 5775 (Fall 17) High-Level Digital Design Automation Static Single Assignment Announcements HW 1 released (due Friday) Student-led discussions on Tuesday 9/26 Sign up on Piazza: 3 students / group Meet

More information

Control Flow Analysis

Control Flow Analysis Control Flow Analysis Last time Undergraduate compilers in a day Today Assignment 0 due Control-flow analysis Building basic blocks Building control-flow graphs Loops January 28, 2015 Control Flow Analysis

More information

Control flow graphs and loop optimizations. Thursday, October 24, 13

Control flow graphs and loop optimizations. Thursday, October 24, 13 Control flow graphs and loop optimizations Agenda Building control flow graphs Low level loop optimizations Code motion Strength reduction Unrolling High level loop optimizations Loop fusion Loop interchange

More information

Computer Science Department Carlos III University of Madrid Leganés (Spain) David Griol Barres

Computer Science Department Carlos III University of Madrid Leganés (Spain) David Griol Barres Computer Science Department Carlos III University of Madrid Leganés (Spain) David Griol Barres dgriol@inf.uc3m.es Compiler Architecture Source language Scanner (lexical analysis) tokens Parser (syntax

More information

Alternatives for semantic processing

Alternatives for semantic processing Semantic Processing Copyright c 2000 by Antony L. Hosking. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies

More information

Introduction to Machine-Independent Optimizations - 6

Introduction to Machine-Independent Optimizations - 6 Introduction to Machine-Independent Optimizations - 6 Machine-Independent Optimization Algorithms Department of Computer Science and Automation Indian Institute of Science Bangalore 560 012 NPTEL Course

More information

Principles of Compiler Design

Principles of Compiler Design Principles of Compiler Design Code Generation Compiler Lexical Analysis Syntax Analysis Semantic Analysis Source Program Token stream Abstract Syntax tree Intermediate Code Code Generation Target Program

More information

Challenges in the back end. CS Compiler Design. Basic blocks. Compiler analysis

Challenges in the back end. CS Compiler Design. Basic blocks. Compiler analysis Challenges in the back end CS3300 - Compiler Design Basic Blocks and CFG V. Krishna Nandivada IIT Madras The input to the backend (What?). The target program instruction set, constraints, relocatable or

More information

Compiler Theory. (Intermediate Code Generation Abstract S yntax + 3 Address Code)

Compiler Theory. (Intermediate Code Generation Abstract S yntax + 3 Address Code) Compiler Theory (Intermediate Code Generation Abstract S yntax + 3 Address Code) 006 Why intermediate code? Details of the source language are confined to the frontend (analysis phase) of a compiler, while

More information

Intermediate Representations

Intermediate Representations COMP 506 Rice University Spring 2018 Intermediate Representations source code IR Front End Optimizer Back End IR target code Copyright 2018, Keith D. Cooper & Linda Torczon, all rights reserved. Students

More information

Data Flow Analysis and Computation of SSA

Data Flow Analysis and Computation of SSA Compiler Design 1 Data Flow Analysis and Computation of SSA Compiler Design 2 Definitions A basic block is the longest sequence of three-address codes with the following properties. The control flows to

More information

Compiler Optimization Intermediate Representation

Compiler Optimization Intermediate Representation Compiler Optimization Intermediate Representation Virendra Singh Associate Professor Computer Architecture and Dependable Systems Lab Department of Electrical Engineering Indian Institute of Technology

More information

CS 406/534 Compiler Construction Putting It All Together

CS 406/534 Compiler Construction Putting It All Together CS 406/534 Compiler Construction Putting It All Together Prof. Li Xu Dept. of Computer Science UMass Lowell Fall 2004 Part of the course lecture notes are based on Prof. Keith Cooper, Prof. Ken Kennedy

More information

Compiler Construction 2016/2017 Loop Optimizations

Compiler Construction 2016/2017 Loop Optimizations Compiler Construction 2016/2017 Loop Optimizations Peter Thiemann January 16, 2017 Outline 1 Loops 2 Dominators 3 Loop-Invariant Computations 4 Induction Variables 5 Array-Bounds Checks 6 Loop Unrolling

More information

Concepts Introduced in Chapter 6

Concepts Introduced in Chapter 6 Concepts Introduced in Chapter 6 types of intermediate code representations translation of declarations arithmetic expressions boolean expressions flow-of-control statements backpatching EECS 665 Compiler

More information

CONTROL FLOW ANALYSIS. The slides adapted from Vikram Adve

CONTROL FLOW ANALYSIS. The slides adapted from Vikram Adve CONTROL FLOW ANALYSIS The slides adapted from Vikram Adve Flow Graphs Flow Graph: A triple G=(N,A,s), where (N,A) is a (finite) directed graph, s N is a designated initial node, and there is a path from

More information

Group B Assignment 9. Code generation using DAG. Title of Assignment: Problem Definition: Code generation using DAG / labeled tree.

Group B Assignment 9. Code generation using DAG. Title of Assignment: Problem Definition: Code generation using DAG / labeled tree. Group B Assignment 9 Att (2) Perm(3) Oral(5) Total(10) Sign Title of Assignment: Code generation using DAG. 9.1.1 Problem Definition: Code generation using DAG / labeled tree. 9.1.2 Perquisite: Lex, Yacc,

More information

Compiler Structure. Data Flow Analysis. Control-Flow Graph. Available Expressions. Data Flow Facts

Compiler Structure. Data Flow Analysis. Control-Flow Graph. Available Expressions. Data Flow Facts Compiler Structure Source Code Abstract Syntax Tree Control Flow Graph Object Code CMSC 631 Program Analysis and Understanding Fall 2003 Data Flow Analysis Source code parsed to produce AST AST transformed

More information

JOHANNES KEPLER UNIVERSITÄT LINZ

JOHANNES KEPLER UNIVERSITÄT LINZ JOHANNES KEPLER UNIVERSITÄT LINZ Institut für Praktische Informatik (Systemsoftware) Adding Static Single Assignment Form and a Graph Coloring Register Allocator to the Java Hotspot Client Compiler Hanspeter

More information

Loops. Lather, Rinse, Repeat. CS4410: Spring 2013

Loops. Lather, Rinse, Repeat. CS4410: Spring 2013 Loops or Lather, Rinse, Repeat CS4410: Spring 2013 Program Loops Reading: Appel Ch. 18 Loop = a computation repeatedly executed until a terminating condition is reached High-level loop constructs: While

More information

Concepts Introduced in Chapter 6

Concepts Introduced in Chapter 6 Concepts Introduced in Chapter 6 types of intermediate code representations translation of declarations arithmetic expressions boolean expressions flow-of-control statements backpatching EECS 665 Compiler

More information

About the Authors... iii Introduction... xvii. Chapter 1: System Software... 1

About the Authors... iii Introduction... xvii. Chapter 1: System Software... 1 Table of Contents About the Authors... iii Introduction... xvii Chapter 1: System Software... 1 1.1 Concept of System Software... 2 Types of Software Programs... 2 Software Programs and the Computing Machine...

More information

Context-sensitive analysis. Semantic Processing. Alternatives for semantic processing. Context-sensitive analysis

Context-sensitive analysis. Semantic Processing. Alternatives for semantic processing. Context-sensitive analysis Semantic Processing The compilation process is driven by the syntactic structure of the program as discovered by the parser Semantic routines: interpret meaning of the program based on its syntactic structure

More information

Control Flow Analysis. Reading & Topics. Optimization Overview CS2210. Muchnick: chapter 7

Control Flow Analysis. Reading & Topics. Optimization Overview CS2210. Muchnick: chapter 7 Control Flow Analysis CS2210 Lecture 11 Reading & Topics Muchnick: chapter 7 Optimization Overview Control Flow Analysis Maybe start data flow analysis Optimization Overview Two step process Analyze program

More information

THEORY OF COMPILATION

THEORY OF COMPILATION Lecture 10 Code Generation THEORY OF COMPILATION EranYahav Reference: Dragon 8. MCD 4.2.4 1 You are here Compiler txt Source Lexical Analysis Syntax Analysis Parsing Semantic Analysis Inter. Rep. (IR)

More information

Lecture 25 CIS 341: COMPILERS

Lecture 25 CIS 341: COMPILERS Lecture 25 CIS 341: COMPILERS Announcements HW6: Dataflow Analysis Due: Weds. April 26 th NOTE: See Piazza for an update TLDR: "simple" regalloc should not suffice. Change gradedtests.ml >= to > FINAL

More information

Lecture Compiler Middle-End

Lecture Compiler Middle-End Lecture 16-18 18 Compiler Middle-End Jianwen Zhu Electrical and Computer Engineering University of Toronto Jianwen Zhu 2009 - P. 1 What We Have Done A lot! Compiler Frontend Defining language Generating

More information

IR Optimization. May 15th, Tuesday, May 14, 13

IR Optimization. May 15th, Tuesday, May 14, 13 IR Optimization May 15th, 2013 Tuesday, May 14, 13 But before we talk about IR optimization... Wrapping up IR generation Tuesday, May 14, 13 Three-Address Code Or TAC The IR that you will be using for

More information

CSc 453 Interpreters & Interpretation

CSc 453 Interpreters & Interpretation CSc 453 Interpreters & Interpretation Saumya Debray The University of Arizona Tucson Interpreters An interpreter is a program that executes another program. An interpreter implements a virtual machine,

More information

Plan for Today. Concepts. Next Time. Some slides are from Calvin Lin s grad compiler slides. CS553 Lecture 2 Optimizations and LLVM 1

Plan for Today. Concepts. Next Time. Some slides are from Calvin Lin s grad compiler slides. CS553 Lecture 2 Optimizations and LLVM 1 Plan for Today Quiz 2 How to automate the process of performance optimization LLVM: Intro to Intermediate Representation Loops as iteration spaces Data-flow Analysis Intro Control-flow graph terminology

More information

CS 432 Fall Mike Lam, Professor. Code Generation

CS 432 Fall Mike Lam, Professor. Code Generation CS 432 Fall 2015 Mike Lam, Professor Code Generation Compilers "Back end" Source code Tokens Syntax tree Machine code char data[20]; int main() { float x = 42.0; return 7; } 7f 45 4c 46 01 01 01 00 00

More information

Context-Free Grammar. Concepts Introduced in Chapter 2. Parse Trees. Example Grammar and Derivation

Context-Free Grammar. Concepts Introduced in Chapter 2. Parse Trees. Example Grammar and Derivation Concepts Introduced in Chapter 2 A more detailed overview of the compilation process. Parsing Scanning Semantic Analysis Syntax-Directed Translation Intermediate Code Generation Context-Free Grammar A

More information

Local Optimization: Value Numbering The Desert Island Optimization. Comp 412 COMP 412 FALL Chapter 8 in EaC2e. target code

Local Optimization: Value Numbering The Desert Island Optimization. Comp 412 COMP 412 FALL Chapter 8 in EaC2e. target code COMP 412 FALL 2017 Local Optimization: Value Numbering The Desert Island Optimization Comp 412 source code IR Front End Optimizer Back End IR target code Copyright 2017, Keith D. Cooper & Linda Torczon,

More information

Compiler Code Generation COMP360

Compiler Code Generation COMP360 Compiler Code Generation COMP360 Students who acquire large debts putting themselves through school are unlikely to think about changing society. When you trap people in a system of debt, they can t afford

More information

MIT Introduction to Program Analysis and Optimization. Martin Rinard Laboratory for Computer Science Massachusetts Institute of Technology

MIT Introduction to Program Analysis and Optimization. Martin Rinard Laboratory for Computer Science Massachusetts Institute of Technology MIT 6.035 Introduction to Program Analysis and Optimization Martin Rinard Laboratory for Computer Science Massachusetts Institute of Technology Program Analysis Compile-time reasoning about run-time behavior

More information

Intermediate Code Generation

Intermediate Code Generation Intermediate Code Generation Rupesh Nasre. CS3300 Compiler Design IIT Madras Aug 2015 Character stream Lexical Analyzer Machine-Independent Code Optimizer F r o n t e n d Token stream Syntax Analyzer Syntax

More information

Calvin Lin The University of Texas at Austin

Calvin Lin The University of Texas at Austin Loop Invariant Code Motion Last Time SSA Today Loop invariant code motion Reuse optimization Next Time More reuse optimization Common subexpression elimination Partial redundancy elimination February 23,

More information

Problem Score Max Score 1 Syntax directed translation & type

Problem Score Max Score 1 Syntax directed translation & type CMSC430 Spring 2014 Midterm 2 Name Instructions You have 75 minutes for to take this exam. This exam has a total of 100 points. An average of 45 seconds per point. This is a closed book exam. No notes

More information

CS 406/534 Compiler Construction Intermediate Representation and Procedure Abstraction

CS 406/534 Compiler Construction Intermediate Representation and Procedure Abstraction CS 406/534 Compiler Construction Intermediate Representation and Procedure Abstraction Prof. Li Xu Dept. of Computer Science UMass Lowell Fall 2004 Part of the course lecture notes are based on Prof. Keith

More information

Generating Control Flow Graphs for Ada Programs

Generating Control Flow Graphs for Ada Programs Institut für Automation Abt. für Automatisierungssysteme Technische Universität Wien Projektbericht Nr. 183/1-139 September 2007 Generating Control Flow Graphs for Ada Programs Raul Fechete Georg Kienesberger

More information

GCC Internals Control and data flow support

GCC Internals Control and data flow support GCC Internals Control and data flow support Diego Novillo dnovillo@google.com November 2007 Control/Data Flow support Call Graph (cgraph) Control Flow Graph (CFG) Static Single Assignment in GIMPLE (SSA)

More information

Semantic Analysis computes additional information related to the meaning of the program once the syntactic structure is known.

Semantic Analysis computes additional information related to the meaning of the program once the syntactic structure is known. SEMANTIC ANALYSIS: Semantic Analysis computes additional information related to the meaning of the program once the syntactic structure is known. Parsing only verifies that the program consists of tokens

More information

MODULE 32 DAG based Code Generation and Dynamic Programming

MODULE 32 DAG based Code Generation and Dynamic Programming MODULE 32 DAG based Code Generation and Dynamic Programming In this module, we would try to understand the code generation algorithm from DAG after the DAG has been reordered and labeled. As another approach

More information

Fall Compiler Principles Lecture 6: Intermediate Representation. Roman Manevich Ben-Gurion University of the Negev

Fall Compiler Principles Lecture 6: Intermediate Representation. Roman Manevich Ben-Gurion University of the Negev Fall 2015-2016 Compiler Principles Lecture 6: Intermediate Representation Roman Manevich Ben-Gurion University of the Negev Tentative syllabus Front End Intermediate Representation Optimizations Code Generation

More information

Static Single Assignment Form in the COINS Compiler Infrastructure

Static Single Assignment Form in the COINS Compiler Infrastructure Static Single Assignment Form in the COINS Compiler Infrastructure Masataka Sassa (Tokyo Institute of Technology) Background Static single assignment (SSA) form facilitates compiler optimizations. Compiler

More information

CS606- compiler instruction Solved MCQS From Midterm Papers

CS606- compiler instruction Solved MCQS From Midterm Papers CS606- compiler instruction Solved MCQS From Midterm Papers March 06,2014 MC100401285 Moaaz.pk@gmail.com Mc100401285@gmail.com PSMD01 Final Term MCQ s and Quizzes CS606- compiler instruction If X is a

More information

Code Generation. The Main Idea of Today s Lecture. We can emit stack-machine-style code for expressions via recursion. Lecture Outline.

Code Generation. The Main Idea of Today s Lecture. We can emit stack-machine-style code for expressions via recursion. Lecture Outline. The Main Idea of Today s Lecture Code Generation We can emit stack-machine-style code for expressions via recursion (We will use MIPS assembly as our target language) 2 Lecture Outline What are stack machines?

More information

Compiler Optimization and Code Generation

Compiler Optimization and Code Generation Compiler Optimization and Code Generation Professor: Sc.D., Professor Vazgen Melikyan 1 Course Overview Introduction: Overview of Optimizations 1 lecture Intermediate-Code Generation 2 lectures Machine-Independent

More information

DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING Subject Name: CS2352 Principles of Compiler Design Year/Sem : III/VI

DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING Subject Name: CS2352 Principles of Compiler Design Year/Sem : III/VI DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING Subject Name: CS2352 Principles of Compiler Design Year/Sem : III/VI UNIT I - LEXICAL ANALYSIS 1. What is the role of Lexical Analyzer? [NOV 2014] 2. Write

More information

Where We Are. Lexical Analysis. Syntax Analysis. IR Generation. IR Optimization. Code Generation. Machine Code. Optimization.

Where We Are. Lexical Analysis. Syntax Analysis. IR Generation. IR Optimization. Code Generation. Machine Code. Optimization. Where We Are Source Code Lexical Analysis Syntax Analysis Semantic Analysis IR Generation IR Optimization Code Generation Optimization Machine Code Where We Are Source Code Lexical Analysis Syntax Analysis

More information

CS DATA STRUCTURES AND ALGORITHMS

CS DATA STRUCTURES AND ALGORITHMS Computer Science and Engineering Third Semester CS1211 - DATA STRUCTURES AND ALGORITHMS UNIT-I - INTRODUCTION TO DATASTRUCTURES 1.Write down the definition of data structures? PART -A A data structure

More information

SPARK: A Parallelizing High-Level Synthesis Framework

SPARK: A Parallelizing High-Level Synthesis Framework SPARK: A Parallelizing High-Level Synthesis Framework Sumit Gupta Rajesh Gupta, Nikil Dutt, Alex Nicolau Center for Embedded Computer Systems University of California, Irvine and San Diego http://www.cecs.uci.edu/~spark

More information

Compiler Optimizations. Chapter 8, Section 8.5 Chapter 9, Section 9.1.7

Compiler Optimizations. Chapter 8, Section 8.5 Chapter 9, Section 9.1.7 Compiler Optimizations Chapter 8, Section 8.5 Chapter 9, Section 9.1.7 2 Local vs. Global Optimizations Local: inside a single basic block Simple forms of common subexpression elimination, dead code elimination,

More information

CS553 Lecture Dynamic Optimizations 2

CS553 Lecture Dynamic Optimizations 2 Dynamic Optimizations Last time Predication and speculation Today Dynamic compilation CS553 Lecture Dynamic Optimizations 2 Motivation Limitations of static analysis Programs can have values and invariants

More information

An Introduction to Trees

An Introduction to Trees An Introduction to Trees Alice E. Fischer Spring 2017 Alice E. Fischer An Introduction to Trees... 1/34 Spring 2017 1 / 34 Outline 1 Trees the Abstraction Definitions 2 Expression Trees 3 Binary Search

More information

The GNU Compiler Collection

The GNU Compiler Collection The GNU Compiler Collection Diego Novillo dnovillo@redhat.com Gelato Federation Meeting Porto Alegre, Rio Grande do Sul, Brazil October 3, 2005 Introduction GCC is a popular compiler, freely available

More information

Lecture Notes on Static Single Assignment Form

Lecture Notes on Static Single Assignment Form Lecture Notes on Static Single Assignment Form 15-411: Compiler Design Frank Pfenning, Rob Simmons Lecture 10 October 1, 2015 1 Introduction In abstract machine code of the kind we have discussed so far,

More information

Reuse Optimization. LLVM Compiler Infrastructure. Local Value Numbering. Local Value Numbering (cont)

Reuse Optimization. LLVM Compiler Infrastructure. Local Value Numbering. Local Value Numbering (cont) LLVM Compiler Infrastructure Source: LLVM: A Compilation Framework for Lifelong Program Analysis & Transformation by Lattner and Adve Reuse Optimization Eliminate redundant operations in the dynamic execution

More information

Lecture Notes on Loop Optimizations

Lecture Notes on Loop Optimizations Lecture Notes on Loop Optimizations 15-411: Compiler Design Frank Pfenning Lecture 17 October 22, 2013 1 Introduction Optimizing loops is particularly important in compilation, since loops (and in particular

More information

Compiler Optimizations. Chapter 8, Section 8.5 Chapter 9, Section 9.1.7

Compiler Optimizations. Chapter 8, Section 8.5 Chapter 9, Section 9.1.7 Compiler Optimizations Chapter 8, Section 8.5 Chapter 9, Section 9.1.7 2 Local vs. Global Optimizations Local: inside a single basic block Simple forms of common subexpression elimination, dead code elimination,

More information

KINGS COLLEGE OF ENGINEERING DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING ACADEMIC YEAR / EVEN SEMESTER

KINGS COLLEGE OF ENGINEERING DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING ACADEMIC YEAR / EVEN SEMESTER KINGS COLLEGE OF ENGINEERING PUNALKULAM DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING ACADEMIC YEAR 2010-2011 / EVEN SEMESTER SUBJECT CODE\SUBJECT NAME: CS1352 \ PRINCIPLES OF COMPILER DESIGN QUESTION BANK

More information

Lecture Compiler Backend

Lecture Compiler Backend Lecture 19-23 Compiler Backend Jianwen Zhu Electrical and Computer Engineering University of Toronto Jianwen Zhu 2009 - P. 1 Backend Tasks Instruction selection Map virtual instructions To machine instructions

More information

CSE 501: Compiler Construction. Course outline. Goals for language implementation. Why study compilers? Models of compilation

CSE 501: Compiler Construction. Course outline. Goals for language implementation. Why study compilers? Models of compilation CSE 501: Compiler Construction Course outline Main focus: program analysis and transformation how to represent programs? how to analyze programs? what to analyze? how to transform programs? what transformations

More information

Lecture 15-16: Intermediate Code-Generation

Lecture 15-16: Intermediate Code-Generation Lecture 15-16: Intermediate Code-Generation Dr Kieran T. Herley Department of Computer Science University College Cork 2017-2018 KH (16/11/17) Lecture 15-16: Intermediate Code-Generation 2017-2018 1 /

More information

Rui Wang, Assistant professor Dept. of Information and Communication Tongji University.

Rui Wang, Assistant professor Dept. of Information and Communication Tongji University. Instructions: ti Language of the Computer Rui Wang, Assistant professor Dept. of Information and Communication Tongji University it Email: ruiwang@tongji.edu.cn Computer Hierarchy Levels Language understood

More information

CS301 - Data Structures Glossary By

CS301 - Data Structures Glossary By CS301 - Data Structures Glossary By Abstract Data Type : A set of data values and associated operations that are precisely specified independent of any particular implementation. Also known as ADT Algorithm

More information

Three address representation

Three address representation Three address representation A typical inetrnal representation is three address ce. In many cases, the compound statements (e.g. for or do loops and if statements) are transformed into sequences of instructions

More information

Control-Flow Graphs & Dataflow Analysis. CS4410: Spring 2013

Control-Flow Graphs & Dataflow Analysis. CS4410: Spring 2013 Control-Flow Graphs & Dataflow Analysis CS4410: Spring 2013 Past Few Lectures: High-level Intermediate Languages: Monadic Normal Form Optimization as algebraic transformations: 3+4 7, (λx.e) v e[v/x],

More information

CS553 Lecture Profile-Guided Optimizations 3

CS553 Lecture Profile-Guided Optimizations 3 Profile-Guided Optimizations Last time Instruction scheduling Register renaming alanced Load Scheduling Loop unrolling Software pipelining Today More instruction scheduling Profiling Trace scheduling CS553

More information

Compilers. Compiler Construction Tutorial The Front-end

Compilers. Compiler Construction Tutorial The Front-end Compilers Compiler Construction Tutorial The Front-end Salahaddin University College of Engineering Software Engineering Department 2011-2012 Amanj Sherwany http://www.amanj.me/wiki/doku.php?id=teaching:su:compilers

More information

Code Analysis and Optimization. Pat Morin COMP 3002

Code Analysis and Optimization. Pat Morin COMP 3002 Code Analysis and Optimization Pat Morin COMP 3002 Outline Basic blocks and flow graphs Local register allocation Global register allocation Selected optimization topics 2 The Big Picture By now, we know

More information

Multi-dimensional Arrays

Multi-dimensional Arrays Multi-dimensional Arrays IL for Arrays & Local Optimizations Lecture 26 (Adapted from notes by R. Bodik and G. Necula) A 2D array is a 1D array of 1D arrays Java uses arrays of pointers to arrays for >1D

More information

CMPT 379 Compilers. Anoop Sarkar. 11/13/07 1. TAC: Intermediate Representation. Language + Machine Independent TAC

CMPT 379 Compilers. Anoop Sarkar.  11/13/07 1. TAC: Intermediate Representation. Language + Machine Independent TAC CMPT 379 Compilers Anoop Sarkar http://www.cs.sfu.ca/~anoop 11/13/07 1 TAC: Intermediate Representation Language Specific Language + Machine Independent Machine Dependent Front End AST Intermediate Code

More information

Flow Graph Theory. Depth-First Ordering Efficiency of Iterative Algorithms Reducible Flow Graphs

Flow Graph Theory. Depth-First Ordering Efficiency of Iterative Algorithms Reducible Flow Graphs Flow Graph Theory Depth-First Ordering Efficiency of Iterative Algorithms Reducible Flow Graphs 1 Roadmap Proper ordering of nodes of a flow graph speeds up the iterative algorithms: depth-first ordering.

More information

Pipelining and Exploiting Instruction-Level Parallelism (ILP)

Pipelining and Exploiting Instruction-Level Parallelism (ILP) Pipelining and Exploiting Instruction-Level Parallelism (ILP) Pipelining and Instruction-Level Parallelism (ILP). Definition of basic instruction block Increasing Instruction-Level Parallelism (ILP) &

More information

Intermediate representations IR #1: CPS/L 3. Code example. Advanced Compiler Construction Michel Schinz

Intermediate representations IR #1: CPS/L 3. Code example. Advanced Compiler Construction Michel Schinz Intermediate representations Intermediate representations Advanced Compiler Construction Michel Schinz 2016 03 03 The term intermediate representation (IR) or intermediate language designates the data-structure(s)

More information

Lecture Notes on Static Single Assignment Form

Lecture Notes on Static Single Assignment Form Lecture Notes on Static Single Assignment Form 15-411: Compiler Design Frank Pfenning Lecture 6 September 12, 2013 1 Introduction In abstract machine code of the kind we have discussed so far, a variable

More information

Problem with Scanning an Infix Expression

Problem with Scanning an Infix Expression Operator Notation Consider the infix expression (X Y) + (W U), with parentheses added to make the evaluation order perfectly obvious. This is an arithmetic expression written in standard form, called infix

More information

Using GCC as a Research Compiler

Using GCC as a Research Compiler Using GCC as a Research Compiler Diego Novillo dnovillo@redhat.com Computer Engineering Seminar Series North Carolina State University October 25, 2004 Introduction GCC is a popular compiler, freely available

More information

Chapter 6 Intermediate Code Generation

Chapter 6 Intermediate Code Generation Chapter 6 Intermediate Code Generation Outline Variants of Syntax Trees Three-address code Types and declarations Translation of expressions Type checking Control flow Backpatching Introduction Intermediate

More information

We can express this in dataflow equations using gen and kill sets, where the sets are now sets of expressions.

We can express this in dataflow equations using gen and kill sets, where the sets are now sets of expressions. Available expressions Suppose we want to do common-subexpression elimination; that is, given a program that computes x y more than once, can we eliminate one of the duplicate computations? To find places

More information

Topics Power tends to corrupt; absolute power corrupts absolutely. Computer Organization CS Data Representation

Topics Power tends to corrupt; absolute power corrupts absolutely. Computer Organization CS Data Representation Computer Organization CS 231-01 Data Representation Dr. William H. Robinson November 12, 2004 Topics Power tends to corrupt; absolute power corrupts absolutely. Lord Acton British historian, late 19 th

More information

A Propagation Engine for GCC

A Propagation Engine for GCC A Propagation Engine for GCC Diego Novillo Red Hat Canada dnovillo@redhat.com May 1, 2005 Abstract Several analyses and transformations work by propagating known values and attributes throughout the program.

More information

Intermediate Code, Object Representation, Type-Based Optimization

Intermediate Code, Object Representation, Type-Based Optimization CS 301 Spring 2016 Meetings March 14 Intermediate Code, Object Representation, Type-Based Optimization Plan Source Program Lexical Syntax Semantic Intermediate Code Generation Machine- Independent Optimization

More information

COMP 303 Computer Architecture Lecture 3. Comp 303 Computer Architecture

COMP 303 Computer Architecture Lecture 3. Comp 303 Computer Architecture COMP 303 Computer Architecture Lecture 3 Comp 303 Computer Architecture 1 Supporting procedures in computer hardware The execution of a procedure Place parameters in a place where the procedure can access

More information

Summary: Direct Code Generation

Summary: Direct Code Generation Summary: Direct Code Generation 1 Direct Code Generation Code generation involves the generation of the target representation (object code) from the annotated parse tree (or Abstract Syntactic Tree, AST)

More information

Loop Unrolling. Source: for i := 1 to 100 by 1 A[i] := A[i] + B[i]; endfor

Loop Unrolling. Source: for i := 1 to 100 by 1 A[i] := A[i] + B[i]; endfor Source: for i := 1 to 100 by 1 A[i] := A[i] + B[i]; Loop Unrolling Transformed Code: for i := 1 to 100 by 4 A[i ] := A[i ] + B[i ]; A[i+1] := A[i+1] + B[i+1]; A[i+2] := A[i+2] + B[i+2]; A[i+3] := A[i+3]

More information

Compiler Design and Construction Optimization

Compiler Design and Construction Optimization Compiler Design and Construction Optimization Generating Code via Macro Expansion Macroexpand each IR tuple or subtree A := B+C; D := A * C; lw $t0, B, lw $t1, C, add $t2, $t0, $t1 sw $t2, A lw $t0, A

More information

More Dataflow Analysis

More Dataflow Analysis More Dataflow Analysis Steps to building analysis Step 1: Choose lattice Step 2: Choose direction of dataflow (forward or backward) Step 3: Create transfer function Step 4: Choose confluence operator (i.e.,

More information

Semantic Analysis. Outline. The role of semantic analysis in a compiler. Scope. Types. Where we are. The Compiler Front-End

Semantic Analysis. Outline. The role of semantic analysis in a compiler. Scope. Types. Where we are. The Compiler Front-End Outline Semantic Analysis The role of semantic analysis in a compiler A laundry list of tasks Scope Static vs. Dynamic scoping Implementation: symbol tables Types Static analyses that detect type errors

More information