8. Code Generation

Generate executable code for a target machine that is a faithful representation of the semantics of the source code Depends not only on the characteristics of the source language but also on detailed information about the target architecture, the structure of the runtime environment, and the operating system running on the target machine

Contents 8.1 Intermediate Code and Data Structure for code Generation 8.2 Basic Code Generation Techniques 8.9 A Survey of Code Optimization Techniques

8.1 Intermediate Code and Data Structures for Code Generation

A data structure that represents the source program during translation is called an intermediate representation, or IR, for short Such an intermediate representation that resembles target code is called intermediate code – Intermediate code is particularly useful when the goal of the compiler is to produce extremely efficient code; – Intermediate code can also be useful in making a compiler more easily retarget-able. Study two popular forms of intermediate code: Three - Address code and P-code

8.1.1 Three-Address Code

The most basic instruction of three-address code is designed to represent the evaluation of arithmetic expressions and has the following general form: X = y op z Here op may be an arithmetic operator such as + or – or can be any other operator on the values of y and z The name “three-address code” comes from this form of instruction. x, y and z represents an address in memory. The use of address of x differs from the use of the addresses of y and z and that y and z may represent constants or literal values with no runtime address.

TAC for FOR LOOP FOR LOOP a=3; b=4; for(i=0;i<n;i++){ a=b+1; a=a*a; } c=a; in 3 TA code a=3; b=4; i=0; L1: VAR1=i<n; if(VAR1) goto L2; goto L3; L4:i++; goto L1; L2:VAR2=b+1; a=VAR2; VAR3=a*a; a=VAR3; goto L4 L3:c=a;

TAC for while LOOP WHILE Loop a=3; b=4; i=0; while(i<n){ a=b+1; a=a*a; i++; } c=a; in 3 TA code a=3; b=4; i=0; L1: VAR1=i<n; if(VAR1) goto L2; goto L3; L2:VAR2=b+1; a=VAR2; VAR3=a*a; a=VAR3; i++; goto L1 L3:c=a;

TAC for DO while LOOP DO WHILE Loop a=3; b=4; i=0; do{ a=b+1; a=a*a; i++; }while(i<n); c=a; in 3 TA code a=3; b=4; i=0; L1: VAR2=b+1; a=VAR2; VAR3=a*a; a=VAR3; i++; VAR1=i<n; if(VAR1) goto L1; goto L2; L2:c=a;

Figure 8.1 Sample TINY program: { sample program in TINY language -- computes factorial } read x ; if 0 > x then fact:=1; repeat fact:=fact*x; x:=x-1 until x=0; write fact ends

The Three-address codes for above TINY program read x t1=x>0 if_false t1 goto L1 fact=1 label L2 t2=fact*x fact=t2 t3=x-1 x=t3 t4= x= =0 if_false t4 goto L2 write fact label L1 halt

8.1.2 Data Structures for the Implementation of Three-Address Code

The most common implementation is to implement three-address code as quadruple, which means that four fields are necessary: – One for the operation and three for the addresses A different implementation of three-address code is called a triple: – Use the instructions themselves to represent the temporaries. It requires that each three-address instruction be reference-able, either as an index in an array or as a pointer in a linked list.

Quadruple implementation for the three- address code of the previous example (rd, x, _, _ ) (gt, x, 0, t1 ) (if_f, t1, L1, _ ) (asn, 1,fact, _ ) (lab, L2, _, _ ) (mul, fact, x, t2 ) (asn, t2, fact, _ ) (sub, x, 1, t3 ) (asn, t3, x, _ ) (eq, x, 0, t4 ) (if_f, t4, L2, _) (wri, fact, _, _ ) (lab, L1, _, _ ) (halt, _, _, _ ) read x ; if 0 > x then fact:=1; repeat fact:=fact*x; x:=x-1 until x=0; write fact ends

C code defining data structures for the quadruples Typedef enum { rd, gt, if_f, asn, lab, mul, sub, eq, wri, halt, … } OpKind; Typedef enum { Empty, IntConst, String } AddrKind; Typedef struct { AddrKind kind; Union { int val; char * name; } contents; } Address Typedef struct { OpKind op; Address addr1, addr2, addr3; } Quad

8.2.3 Generation of Target Code from Intermediate Code

Code generation from intermediate code involves either or both of two standard techniques: – Macro expansion and Static simulation Macro expansion involves replacing each kind of intermediate code instruction with an equivalent sequence of target code instructions. Static simulation involves a straight-line simulation of the effects of the intermediate code and generating target code to match these effects.

Code Optimization Need of Optimization Classification of Optimization – Peep hole optimization – Local optimization – Loop optimization – Global optimization – Interprocedural optimization

Code Optimization Themes of behind Optimization Techniques – Avoid redundancy – Less Code – Straight line codes, Fewer jumps – Code locality

8.9.1 Principal Sources of Code Optimizations

(1) Register Allocation Good use of registers is the most important feature of efficient code. a) Increase the number and speed of operations that can be performed directly on memory. Once it has exhausted the register space, can avoid the expense of having to reclaim registers by storing register values into temporary locations and loading new values. b) Decrease the number of operations that can be performed directly in memory. at the same time increase the number of available registers to 32, 64, or 128

(2) Unnecessary Operations The second major source of code improvement is to avoid generating code for operations that are redundant or unnecessary. Common sub-expression elimination Unreachable code or Dead Code Jump Optimizations

(3) Costly Operations A code generator should not only look for unnecessary operations, but should take advantage of opportunities to reduce the cost of operations that are necessary, but may be implemented in cheaper ways than the source code or a simple implementation might indicate. Reduction in strength Constant Folding Procedure inlineing Tail Recursion Removal Use of Machine Idioms or Instruction Selection

(4) Prediction Program Behavior To perform some of the previously described optimizations, a compiler must collect information about the uses of variables, values and procedures in programs: whether expressions are reused, whether or when variables change their values or remain constant, and whether procedures are called or not. A different approach is taken by some compilers in that statistical behavior about a program is gathered from actual executions and the used to predict which paths are most likely to be taken, which procedures are most likely to be called often, and which sections of code are likely to be executed the most frequently.

8.9.2 Classification of Optimizations

Two useful classifications are the time during the compilation process when an optimization can be applied and the area of the program over which the optimization applies: – The time of application during compilation. Optimizations can be performed at practically every stage of compilation. For example, constant folding …. – Some optimizations can be delayed until after target code has been generated － the target code is examined and rewritten to reflect the optimization. For example, jump optimization ….

The majority of optimizations are performed either during intermediate code generation, just after intermediate code generation, or during target code generation. To the extent that an optimization does not depend on the characteristics of the target machine (called source-level optimizations) They can be performed earlier than those that do depend on the target architecture (target-level optimizations). Sometimes both optimizations do.

Consider the effect that one optimization may have on another. For instance, propagate constants before performing unreachable code elimination. Occasionally, a phase problem may arise in that each of two optimizations may uncover further opportunities for the other. For example, consider the code x = 1;... y = 0;... if (y) x = 0;... if (x) y = 1;

A first pass at constant propagation might result in the code x = 1;... y = 0;... if (0) x = 0;... if (x) y = 1; Now, the body of the first if is unreachable code; eliminating it yields: x = 1;... y = 0;... if (x) y = 1;

The second classification scheme for optimizations that we consider is by the area of the program over which the optimization applies The categories for this classification are called local, global and inter-procedural optimizations （ 1 ） Local optimizations: applied to straight-line segments of code(code sequence with no jumps into or out of the sequence), or basic blocks(maximum sequence of a straight line code). （ 2 ） Global optimizations: applied beyond the basic block, but confined to an individual procedure. （ 3 ） Inter-procedural optimizations: beyond the boundaries of procedures to the entire program.

8.9.3 Data Structures and Implementation Techniques for Optimizations

Some optimizations can be made by transformations on the syntax tree itself – Including constant folding and unreachable code elimination. – However the syntax tree is an unwieldy or unsuitable structure for collecting information and performing optimizations An optimizer that performs global optimizations will construct from the intermediate code of each procedure – A graphical representation of the code called a flow graph. – The nodes of a flow graph are the basic blocks, and the edges are formed from the conditional and unconditional jumps. – Each basic block node contains the sequence of intermediate code instructions of the block.

A single pass can construct a flow graph, together with each of its basic blocks, over the intermediate code Each new basic block is identified as follows: – The first instruction begins a new basic block; – Each label that is the target of a jump begin a new basic block; – Each instruction that follows a jump begins a new basic block;

read x t1=x>0 if_false t1 goto L1 fact=1 label L2 t2=fact*x fact=t2 t3=x-1 x=t3 t4= x= =0 if_false t4 goto L2 write fact label L1 halt

A standard data flow analysis problem is to compute, for each variable, the set of reaching definitions of that variable at the beginning of each basic block. – Here a definition is an intermediate code instruction that can set the value of the variable, such as an assignment or a read Another data structure is frequently constructed for each block, called the DAG of a basic block. – DAG traces the computation and reassignment of values and variables in a basic block as follows. – Values that are used in the block that come from elsewhere are represented as leaf nodes.

Operations on those and other values are represented by interior nodes. – Assignment of a new value is represented by attaching the name of target variable or temporary to the node representing the value assigned For example:

Repeated use of the same value also is represented in the DAG structure. For example, the C assignment x = (x+1)*(x+1) translates into the three-address instructions: t1 = x + 1 t2 = x + 1 t3 = t1 * t2 x = t3 DAG for this sequence of instructions is given, showing the repeated use of the expression x+1

The DAG of a basic block can be constructed by maintaining two dictionaries. – A table containing variable names and constants, with a lookup operation that returns the DAG node to which a variable name is currently assigned. – A table of DAG nodes, with a lookup operation that, given an operation and child node Target code, or a revised version of intermediate code, can be generated from a DAG by a traversal according to any of the possible topological sorts of the nonleaf nodes.

t3 = x - 1 t2 = fact * x x = t3 t4 = x == 0 fact = t2 Of course, wish to avoid the unnecessary use of temporaries, and so would want to generate the following equivalent three-address code, whose order must remain fixed: fact = fact * x x = x - 1 t4 = x == 0

A similar traversal of the DAG of above Figure results in the following revised three-address code: t1 = x + 1 x = t1 * t1 Using DAG to generate target code for a basic block, we automatically get local common sub expression elimination The DAG representation also makes it possible to eliminate redundant stores and tells us how many references to each value there are the C assignment x = (x+1)*(x+1) translates into the three-address instructions: t1 = x + 1 t2 = x + 1 t3 = t1 * t2 x = t3