Administrative. Other Forms of Intermediate Code. Local Optimizations. Lecture 34. Code Generation Summary. Why Intermediate Languages?

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1 Administrative Other Forms of Intermediate Code. Local Optimizations HW #5 is now on-line. Due next Friday. If your test grade is not glookupable, please tell us. Please submit test regrading pleas to the TAs. Lecture 34 (Adapted from notes by R. Bodik and G. Necula) 4/18/08 Prof. Hilfinger CS 164 Lecture /18/08 Prof. Hilfinger CS 164 Lecture 34 2 Code Generation Summary We have discussed Runtime organization Simple stack machine code generation So far, compiler goes directly from AST to assembly language, and does not perform optimizations, Whereas most real compilers use an intermediate language (IL), which they later convert to assembly or machine language. Why Intermediate Languages? Slightly higher-level target simplifies translation of AST Code IL can be sufficiently machine-independent to allow multiple backends (translators from IL to machine code) for different machines, which cuts down on labor of porting a compiler. 4/18/08 Prof. Hilfinger CS 164 Lecture /18/08 Prof. Hilfinger CS 164 Lecture 34 4 Intermediate Languages and Optimization When to perform optimizations On AST Pro: Machine independent Cons: Too high level On assembly language Pro: Exposes optimization opportunities Cons: Machine dependent Cons: Must reimplement optimizations when retargetting On an intermediate language Pro: Machine independent Pro: Exposes optimization opportunities Cons: One more language to worry about Intermediate Languages Each compiler uses its own intermediate language Intermediate language = high-level assembly language Uses register names, but has an unlimited number Uses control structures like assembly language Uses opcodes but some are higher level E.g., push translates to several assembly instructions Most opcodes correspond directly to assembly opcodes 4/18/08 Prof. Hilfinger CS 164 Lecture /18/08 Prof. Hilfinger CS 164 Lecture

2 An Intermediate Language An Intermediate Language (II) P S P ε S id := id op id id := op id id := id id := *id *id := id param id call id return [ id ] if id relop id goto L L: goto L id s are register names Constants can replace id s on right-hand sides Typical operators: +, -, * param, call, return are high-level; refer to calling conventions on given machine. This style often called three-address code Typical instruction has three operands as in x := y op z and y and z can be only registers or constants, much like assembly. The AST expression x + y * z is translated as t 1 := y * z t 2 := x + t 1 Each subexpression has a home in a temporary 4/18/08 Prof. Hilfinger CS 164 Lecture /18/08 Prof. Hilfinger CS 164 Lecture 34 8 Generating Intermediate Code Similar to assembly code generation Major difference: Use any number of IL registers to hold intermediate results Problem of mapping these IL registers to real ones is for later parts of the compiler. Generating Intermediate Code (Cont.) Igen(e, t) function generates code to compute the value of e in register t Example: igen(e 1 + e 2, t) = igen(e 1, t 1 ) (t 1, t 2 are fresh registers) igen(e 2, t 2 ) t := t 1 + t 2 (means Emit code t := t 1 + t 2 ) Unlimited number of registers simple code generation 4/18/08 Prof. Hilfinger CS 164 Lecture /18/08 Prof. Hilfinger CS 164 Lecture IL for Array Access Multi-dimensional Arrays Consider a one-dimensional array. Elements laid out adjacent to each other, each of size S To access array: igen(e 1 [e 2 ], t) = igen(e 1, t 1 ) igen(e 2, t 2 ) t 3 := t 2 * S t 4 := t 1 + t 3 t := *t 4 Assumes e 1 evaluates to array address. Each t i denotes a new IL register A 2D array is a 1D array of 1D arrays Java uses arrays of pointers to arrays for >1D arrays. But if row size constant, for faster access and compactness, may prefer to represent an MxN array as a 1D array of 1D rows (not pointers to rows): row-major order FORTRAN layout is 1D array of 1D columns: column-major order. 4/18/08 Prof. Hilfinger CS 164 Lecture /18/08 Prof. Hilfinger CS 164 Lecture

3 IL for 2D Arrays (Row-Major Order) Again, let S be size of one element, so that a row of length N has size NxS. igen(e 1 [e 2,e 3 ], t) = igen(e 1, t 1 ); igen(e 2,t 2 ); igen(e 3,t 3 ) igen(n, t 4 ) (N need not be constant) t 5 := t 4 * t 2 ; t 6 := t 5 + t 3 ; t 7 := t 6 *S; t 8 := t 7 + t 1 t := *t 8 Array Descriptors Calculation of element address for e 1 [e 2,e 3 ] has form VO + S 1 x e 2 + S 2 x e 3, where VO (address of e 1 [0,0]) is the virtual origin S 1 and S 2 are strides All three of these are constant throughout lifetime of array Common to package these up into an array descriptor, which can be passed in lieu of the array itself. 4/18/08 Prof. Hilfinger CS 164 Lecture /18/08 Prof. Hilfinger CS 164 Lecture Array Descriptors (II) By judicious choice of descriptor values, can make the same formula work for different kinds of array. For example, if lower bounds of indices are 1 rather than 0, must compute address of e[1,1] + S 1 x (e 2-1) + S 2 x (e 3-1) But some algebra puts this into the form Observation These examples show profligate use of registers. Doesn t matter, because this is Intermediate Code. Rely on later optimization stages to do the right thing. VO + S 1 x e 2 + S 2 x e 3 where VO = address of e[1,1] - S 1 - S 2 4/18/08 Prof. Hilfinger CS 164 Lecture /18/08 Prof. Hilfinger CS 164 Lecture Definition. Basic Blocks Code Optimization: Basic Concepts A basic block is a maximal sequence of instructions with: no labels (except at the first instruction), and no jumps (except in the last instruction) Idea: Cannot jump in a basic block (except at beginning) Cannot jump out of a basic block (except at end) Each instruction in a basic block is executed after all the preceding instructions have been executed 4/18/08 Prof. Hilfinger CS 164 Lecture /18/08 Prof. Hilfinger CS 164 Lecture

4 Basic Block Example Consider the basic block 1. L: 2. t := 2 * x 3. w := t + x 4. if w > 0 goto L No way for (3) to be executed without (2) having been executed right before We can change (3) to w := 3 * x Can we eliminate (2) as well? Definition. Control-Flow Graphs A control-flow graph is a directed graph with Basic blocks as nodes An edge from block A to block B if the execution can flow from the last instruction in A to the first instruction in B E.g., the last instruction in A is jump L B E.g., the execution can fall-through from block A to block B Frequently abbreviated as CFG 4/18/08 Prof. Hilfinger CS 164 Lecture /18/08 Prof. Hilfinger CS 164 Lecture Control-Flow Graphs. Example. Optimization Overview x := 1 i := 1 L: x := x * x i := i + 1 if i < 10 goto L The body of a method (or procedure) can be represented as a controlflow graph There is one initial node All return nodes are terminal Optimization seeks to improve a program s utilization of some resource Execution time (most often) Code size Network messages sent Battery power used, etc. Optimization should not alter what the program computes The answer must still be the same 4/18/08 Prof. Hilfinger CS 164 Lecture /18/08 Prof. Hilfinger CS 164 Lecture A Classification of Optimizations For languages like C and Cool there are three granularities of optimizations 1. Local optimizations Apply to a basic block in isolation 2. Global optimizations Apply to a control-flow graph (method body) in isolation 3. Inter-procedural optimizations Apply across method boundaries Most compilers do (1), many do (2) and very few do (3) Cost of Optimizations In practice, a conscious decision is made not to implement the fanciest optimization known Why? Some optimizations are hard to implement Some optimizations are costly in terms of compilation time The fancy optimizations are both hard and costly The goal: maximum improvement with minimum of cost 4/18/08 Prof. Hilfinger CS 164 Lecture /18/08 Prof. Hilfinger CS 164 Lecture

5 Local Optimizations The simplest form of optimizations No need to analyze the whole procedure body Just the basic block in question Example: algebraic simplification Algebraic Simplification Some statements can be deleted x := x + 0 x := x * 1 Some statements can be simplified x := x * 0 x := 0 y := y ** 2 y := y * y x := x * 8 x := x << 3 x := x * 15 t := x << 4; x := t - x (on some machines << is faster than *; but not on all!) 4/18/08 Prof. Hilfinger CS 164 Lecture /18/08 Prof. Hilfinger CS 164 Lecture Constant Folding Operations on constants can be computed at compile time In general, if there is a statement x := y op z And y and z are constants Then y op z can be computed at compile time Example: x := x := 4 Example: if 2 < 0 jump L can be deleted When might constant folding be dangerous? 4/18/08 Prof. Hilfinger CS 164 Lecture Flow of Control Optimizations Eliminating unreachable code: Code that is unreachable in the control-flow graph Basic blocks that are not the target of any jump or fall through from a conditional Such basic blocks can be eliminated Why would such basic blocks occur? Removing unreachable code makes the program smaller And sometimes also faster, due to memory cache effects (increased spatial locality) 4/18/08 Prof. Hilfinger CS 164 Lecture Single Assignment Form Some optimizations are simplified if each assignment is to a temporary that has not appeared already in the basic block Intermediate code can be rewritten to be in single assignment form x := a + y x := a + y a := x a 1 := x x := a * x x 1 := a 1 * x b := x + a b := x 1 + a 1 (x 1 and a 1 are fresh temporaries) Common Subexpression Elimination Assume Basic block is in single assignment form All assignments with same rhs compute the same value Example: x := y + z x := y + z w := y + z w := x Why is single assignment important here? 4/18/08 Prof. Hilfinger CS 164 Lecture /18/08 Prof. Hilfinger CS 164 Lecture

6 Copy Propagation If w := x appears in a block, all subsequent uses of w can be replaced with uses of x Example: b := z + y b := z + y a := b a := b x := 2 * a x := 2 * b This does not make the program smaller or faster but might enable other optimizations Constant folding Dead code elimination Again, single assignment is important here. 4/18/08 Prof. Hilfinger CS 164 Lecture Copy Propagation and Constant Folding Example: a := 5 a := 5 x := 2 * a x := 10 y := x + 6 y := 16 t := x * y t := x << 4 4/18/08 Prof. Hilfinger CS 164 Lecture Dead Code Elimination If w := rhs appears in a basic block w does not appear anywhere else in the program Then the statement w := rhs is dead and can be eliminated Dead = does not contribute to the program s result Example: (a is not used anywhere else) x := z + y b := z + y b := z + y a := x a := b x := 2 * b x := 2 * a x := 2 * b Applying Local Optimizations Each local optimization does very little by itself Typically optimizations interact Performing one optimizations enables other opt. Typical optimizing compilers repeatedly perform optimizations until no improvement is possible The optimizer can also be stopped at any time to limit the compilation time 4/18/08 Prof. Hilfinger CS 164 Lecture /18/08 Prof. Hilfinger CS 164 Lecture Initial code: a := x ** 2 d := c * c e := b * 2 Algebraic optimization: a := x ** 2 d := c * c e := b * 2 4/18/08 Prof. Hilfinger CS 164 Lecture /18/08 Prof. Hilfinger CS 164 Lecture

7 Algebraic optimization: d := c * c e := b + b Copy propagation: d := c * c e := b + b 4/18/08 Prof. Hilfinger CS 164 Lecture /18/08 Prof. Hilfinger CS 164 Lecture Copy propagation: d := x * x e := Constant folding: d := x * x e := /18/08 Prof. Hilfinger CS 164 Lecture /18/08 Prof. Hilfinger CS 164 Lecture Constant folding: d := x * x Common subexpression elimination: d := x * x 4/18/08 Prof. Hilfinger CS 164 Lecture /18/08 Prof. Hilfinger CS 164 Lecture

8 Common subexpression elimination: d := a Copy propagation: d := a 4/18/08 Prof. Hilfinger CS 164 Lecture /18/08 Prof. Hilfinger CS 164 Lecture Copy propagation: d := a f := a + a g := 6 * f Dead code elimination: d := a f := a + a g := 6 * f 4/18/08 Prof. Hilfinger CS 164 Lecture /18/08 Prof. Hilfinger CS 164 Lecture Dead code elimination: f := a + a g := 6 * f This is the final form 4/18/08 Prof. Hilfinger CS 164 Lecture Peephole Optimizations on Assembly Code The optimizations presented before work on intermediate code They are target independent But they can be applied on assembly language also Peephole optimization is an effective technique for improving assembly code The peephole is a short sequence of (usually contiguous) instructions The optimizer replaces the sequence with another equivalent (but faster) one 4/18/08 Prof. Hilfinger CS 164 Lecture

9 Peephole Optimizations (Cont.) Write peephole optimizations as replacement rules i 1,, i n j 1,, j m where the rhs is the improved version of the lhs Examples: move $a $b, move $b $a move $a $b Works if move $b $a is not the target of a jump addiu $a $b k, lw $c ($a) lw $c k($b) - Works if $a not used later (is dead ) Peephole Optimizations (Cont.) Many (but not all) of the basic block optimizations can be cast as peephole optimizations Example: addiu $a $b 0 move $a $b Example: move $a $a These two together eliminate addiu $a $a 0 Just like for local optimizations, peephole optimizations need to be applied repeatedly to get maximum effect 4/18/08 Prof. Hilfinger CS 164 Lecture /18/08 Prof. Hilfinger CS 164 Lecture Local Optimizations. Notes. Intermediate code is helpful for many optimizations Many simple optimizations can still be applied on assembly language Program optimization is grossly misnamed Code produced by optimizers is not optimal in any reasonable sense Program improvement is a more appropriate term Local Optimizations. Notes (II). Serious problem: what to do with pointers? *t may change even if local variable t does not: Aliasing Arrays are a special case (address calculation) What to do about globals? What to do about calls? Not exactly jumps, because they (almost) always return. Can modify variables used by caller Next: global optimizations 4/18/08 Prof. Hilfinger CS 164 Lecture /18/08 Prof. Hilfinger CS 164 Lecture