Code Optimization 1 Course Overview PART I: overview material 1Introduction 2Language processors (tombstone diagrams, bootstrapping) 3Architecture of a compiler PART II: inside a compiler 4Syntax analysis 5Contextual analysis 6Runtime organization 7Code generation PART III: conclusion 8Interpretation 9Review Supplementary material: Code optimization

Code Optimization 2 What This Topic is About The code generated by our compiler is not efficient: It computes some values at runtime that could be known at compile time It computes some values more times than necessary We can do better! Constant folding Common sub-expression elimination Code motion Dead code elimination

Code Optimization 3 Constant folding Consider: The compiler could compute 4 * pi / 3 as before the program runs. How many instructions would this save at run time? Why shouldn’t the programmer just write * r * r * r ? static double pi = ; double volume = 4 * pi / 3 * r * r * r;

Code Optimization 4 Constant folding II Consider: If the address of holidays is x, what is the address of holidays[2].m ? Could the programmer evaluate this at compile time? Should the programmer do this? struct { int y, m, d; } holidays[6]; holidays[2].m = 12; holidays[2].d = 25;

Code Optimization 5 Constant folding III An expression that the compiler should be able to compute the value of is called “manifest”. How can the compiler know if the value of an expression is manifest?

Code Optimization 6 Common sub-expression elimination Consider: Computing x – y takes three instructions; could we save some of them? t = (x – y) * (x – y + z);

Code Optimization 7 Common sub-expression elimination II t = (x – y) * (x – y + z); Naïve code: load x load y sub load x load y sub load z add mult store t Better code: load x load y sub dup load z add mult store t

Code Optimization 8 Common sub-expression elimination III Consider: The address of holidays[i] is a common subexpression. struct { int y, m, d; } holidays[6]; holidays[i].m = 12; holidays[i].d = 25;

Code Optimization 9 But, be careful! Is x – y++ still a common sub-expression? Common sub-expression elimination IV t = (x – y++) * (x – y++ + z);

Code Optimization 10 Code motion Consider: Computing the address of name[i][j] is address[name] + (i * 10) + j Most of that computation is constant throughout the inner loop char name[3][10]; for (int i = 0; i < 3; i++) { for (int j = 0; j < 10; j++) { name[i][j] = ‘a’; address[name] + (i * 10)

Code Optimization 11 Code motion II You can think of this as rewriting the original code: as: char name[3][10]; for (int i = 0; i < 3; i++) { for (int j = 0; j < 10; j++) { name[i][j] = ‘a’; char name[3][10]; for (int i = 0; i < 3; i++) { char *x = &(name[i][0]); for (int j = 0; j < 10; j++) { x[j] = ‘a’;

Code Optimization 12 Code motion III However, this might be a bad idea in some cases. Why? Consider very small values of variable k : char name[3][10]; for (int i = 0; i < 3; i++) { for (int j = 0; j < k; j++) { name[i][j] = ‘a’; char name[3][10]; for (int i = 0; i < 3; i++) { char *x = &(name[i][0]); for (int j = 0; j < k; j++) { x[j] = ‘a’;

Code Optimization 13 Dead code elimination Consider: Computing t takes many instructions, but the value of t is never used. We call the value of t “dead” (or the variable t dead) because it can never affect the final value of the computation. Computing dead values and assigning to dead variables is wasteful. int f(int x, int y, int z) { int t = (x – y) * (x – y + z); return 6; }

Code Optimization 14 Dead code elimination II But consider: Now t is only dead for part of its existence. So it requires a careful algorithm to identify which code is dead, and therefore which code can be safely removed. int f(int x, int y, int z) { int t = x * y; int r = t * z; t = (x – y) * (x – y + z); return r; }

Code Optimization 15 Optimization implementation What do we need to know in order to apply an optimization? –Constant folding –Common sub-expression elimination –Code motion –Dead code elimination –Many other kinds of optimizations Is the optimization correct or safe? Is the optimization really an improvement? What sort of analyses do we need to perform to get the required information?

Code Optimization 16 Basic blocks A basic block is a sequence of instructions that is entered only at the beginning and exited only at the end. A flow graph is a collection of basic blocks connected by edges indicating the flow of control.

Code Optimization 17 Finding basic blocks (Example: JVM code) iconst_1 istore 2 iconst_2 istore 3 Label_1: iload 3 iload 1 if_icmplt Label_4 iconst_0 goto Label_5 Label_4: iconst_1 Label_5: ifeq Label_2 iload 2 iload 3 imul dup istore 2 pop Label_3: iload 3 dup iconst_1 iadd istore 3 pop goto Label_1 Label_2: iload 2 ireturn Mark the first instruction, labelled instructions, and following jumps.

Code Optimization 18 Finding basic blocks II Label_2: iload 2 ireturn Label_3: iload 3 dup iconst_1 iadd istore 3 pop goto Label_1 iload 2 iload 3 imul dup istore 2 pop Label_5: ifeq Label_2 Label_4: iconst_1 iconst_0 goto Label_5 Label_1: iload 3 iload 1 if_icmplt Label_4 iconst_1 istore 2 iconst_2 istore 3

Code Optimization 19 Flow graphs 7:iload 2 ireturn 6:iload 3 dup iconst_1 iadd istore 3 pop goto 1 5:iload 2 iload 3 imul dup istore 2 pop 4:ifeq 7 3:iconst_1 2:iconst_0 goto 4 1:iload 3 iload 1 if_icmplt 3 0:iconst_1 istore 2 iconst_2 istore 3

Code Optimization 20 Local optimizations (within a basic block) Everything you need to know is easy to determine For example: live variable analysis –Start at the end of the block and work backwards –Assume everything is live at the end of the basic block –Copy live/dead info for the instruction –If you see an assignment to x, then mark x “dead” –If you see a reference to y, then mark y “live” 5:iload 2 iload 3 imul dup istore 2 pop live: 1, 2, 3 live: 1, 3 live: 1, 2, 3 live: 1, 3 live: 1, 2, 3 live: 1, 3

Code Optimization 21 Global optimizations Global means “across all basic blocks” We must know what happens across block boundaries For example: live variable analysis –The liveness of a value depends on its later uses perhaps in other blocks –What values does this block define and use? 5:iload 2 iload 3 imul dup istore 2 pop Define: 2 Use: 2, 3

Code Optimization 22 Global live variable analysis We define four sets for each basic block B –def[B] = variables defined in B before they are used in B –use[B] = variables used in B before they are defined in B –in[B] = variables live at the beginning of B –out[B] = variables live at the end of B These sets are related by the following equations: –in[B] = use[B]  (out[B] – def[B]) –out[B] =  S in[S] where S is a successor of B

Code Optimization 23 Solving data flow equations We want a fixed-point solution for this system of equations (there are two equations per each basic block). Start with conservative initial values for each in[B] and out[B], and apply the formulas to update the values of each in[B] and out[B]. Repeat until no further changes can occur. –The best conservative initial value is {}, because no variables are live at the end of the program.

Code Optimization 24 Dead code elimination Suppose we have now computed all global live variable information We can redo the local live variable analysis using correct liveness information at the end of each block: out[B] Whenever we see an assignment to a variable that is marked dead, we can safely eliminate it

Code Optimization 25 Dead code examples iload 1 iload 2 imul istore 4 iload 4 iload 3 imul istore 5 iload 1 iload 2 isub iload 1 iload 2 isub iload 3 iadd imul dup istore 4 pop iload 5 ireturn live: live: 5 live: 3, 5 live: 2, 3, 5 live: 1, 2, 3, 5 live: 1, 2, 3 live: 1, 2, 3, 4 live: 1, 2, 3

Code Optimization 26 Code optimization? Code optimization should be called “code improvement” It is not practical to generate absolutely optimal code (too expensive at compile time ==> NP-hard) There is a trade-off between compiler speed and execution speed Many compilers have options that permit the programmer to choose between generating either optimized or non-optimized code Non-optimized => debugging; optimized => release Some compilers even allow the programmer to select which kinds of optimizations to perform