1. Compiler Principles Fall 2015-2016 Compiler Principles Lecture 11: Register Allocation Roman Manevich Ben-Gurion University of the Negev

2. Syllabus Front End Scanning Top-down Parsing (LL) Bottom-up Parsing (LR) Intermediate Representation Lowering Operational Semantics Lowering Correctness Optimizations Dataflow Analysis Loop Optimizations Code Generation Register Allocation 2 mid-termexam

3. Previously Dataflow framework – Loop optimizations 3

4. agenda The need for register allocation Global register allocation 4

5. 5 Register allocation motivation

6. Registers Most machines have a set of registers, dedicated memory locations that – can be accessed quickly, – can have computations performed on them, and – exist in small quantity Using registers intelligently is a critical step in any compiler – A good register allocator can generate code orders of magnitude better than a bad register allocator 6

7. Register allocation In TAC, there are an unlimited number of variables On a physical machine there are a small number of registers: – x86 has four general-purpose registers and a number of specialized registers – MIPS has twenty-four general-purpose registers and eight special-purpose registers Register allocation is the process of assigning variables to registers and managing data transfer in and out of registers 7

8. Challenges in register allocation Registers are scarce – Often substantially more IR variables than registers – Need to find a way to reuse registers whenever possible Registers are complicated – x86: Each register made of several smaller registers; can't use a register and its constituent registers at the same time – x86: Certain instructions must store their results in specific registers; can't store values there if you want to use those instructions – MIPS: Some registers reserved for the assembler or operating system – Most architectures: Some registers must be preserved across function calls 8

9. 9 naive register allocation

10. Simple approach Problem: program execution very inefficient – moving data back and forth between memory and registers 10 x := y + z mov 16(%ebp), %eax mov 20(%ebp), %ebx add %ebx, %eax mov %eax, 24(%ebx) Straightforward solution: Allocate each variable in activation record At each instruction: bring values needed into registers (rematerialization), perform operation, then store result to memory

11. 11 Reusing registers

12. Find a register allocation 12 b := a + 2 c := b * b b := c + 1 Ret b * a eax ebx register variable ?a ?b ?c

13. Is this a valid allocation? eax ebx register 13 b := a + 2 c := b * b b := c + 1 Ret b * a registervariable eaxa ebxb eaxc ebx := eax + 2 eax := ebx * ebx ebx := eax + 1 Ret ebx * eax Overwrites previous value of ‘a’ also stored in eax

14. Is this a valid allocation? eax ebx register 14 b := a + 2 c := b * b b := c + 1 Ret b * a registervariable ebxa eaxb c eax := ebx + 2 eax := eax * eax eax := eax + 1 Ret eax * ebx Value of ‘c’ stored in eax is not needed anymore so reuse it for ‘b’

15. 15 Register interference graph

16. Main idea For every node n in CFG, we have out[n] – Set of temporaries live out of n Two variables interfere if they appear in the same out[n] of any node n – Cannot be allocated to the same register Conversely, if two variables do not interfere with each other, they can be assigned the same register – We say they have disjoint live ranges How to assign registers to variables? 16

17. Interference graph Nodes of the graph = variables Edges connect variables that interfere with one another Nodes will be assigned a color corresponding to the register assigned to the variable Two colors can’t be next to one another in the graph 17

18. Liveness analysis b := a + 2 c := b * b b := c + 1 Ret b * a 18

19. Liveness analysis b := a + 2 c := b * b b := c + 1 {b, a} Ret b * a 19

20. Liveness analysis b := a + 2 c := b * b {a, c} b := c + 1 {b, a} Ret b * a 20

21. Liveness analysis b := a + 2 {b, a} c := b * b {a, c} b := c + 1 {b, a} Ret b * a 21

22. Liveness analysis {a} b := a + 2 {b, a} c := b * b {a, c} b := c + 1 {b, a} Ret b * a 22

23. Interference graph a cb eax ebx color register 23 {a} b := a + 2 {b, a} c := b * b {a, c} b := c + 1 {b, a} Ret b * a

24. Colored graph a cb eax ebx color register 24 {a} b := a + 2 {b, a} c := b * b {a, c} b := c + 1 {b, a} Ret b * a

25. 25

26. 26

27. 27 Graph coloring

28. This problem is equivalent to graph-coloring, which is NP-hard if there are at least three registers No good polynomial-time algorithms (or even good approximations!) are known for this problem We have to be content with a heuristic that is good enough for RIGs that arise in practice 28

29. Coloring by simplification [Kempe 1879] How to find a K-coloring of a graph Intuition: – Suppose we are trying to K-color a graph and find a node with fewer than K edges – If we delete this node from the graph and color what remains, we can find a color for this node if we add it back in – Reason: fewer than K neighbors  some color must be left over 29

30. Coloring by simplification [Kempe 1879] How to find a K-coloring of a graph Phase 1: Simplify – Repeatedly simplify graph – When a variable (i.e., graph node) is removed, push it on a stack Phase 2: Select – Unwind stack and reconstruct the graph as follows: Pop variable from the stack Add it back to the graph Color the node for that variable with a color that it doesn’t interfere with 30 simplify select

31. 31 Coloring example

32. Coloring for K=2 b ed a c stack: eax ebx color register 32

33. Coloring for K=2 b ed a stack: c c eax ebx color register 33

34. Coloring for K=2 b ed a stack: e c c eax ebx color register 34

35. Coloring for K=2 b ed a stack: a e c c eax ebx color register 35

36. Coloring for K=2 b ed a stack: b a e c c eax ebx color register 36

37. Coloring for K=2 b ed a stack: d b a e c c eax ebx color register 37

38. Coloring for K=2 b ed eax ebx color register a stack: b a e c c 38

39. Coloring for K=2 b e a stack: a e c c eax ebx color register d 39

40. Coloring for K=2 e a stack: e c c eax ebx color register b d 40

41. Coloring for K=2 e stack: c c eax ebx color register a b d 41

42. Coloring for K=2 stack: c eax ebx color register e a b d 42

43. 43 spilling

44. Failure of heuristic If the graph cannot be colored, it will eventually be simplified to graph in which every node has at least K neighbors Sometimes, the graph is still K-colorable! Finding a K-coloring in all situations is an NP-complete problem – We will have to approximate to make register allocators fast enough 44

45. Example where Kempe’s approach fails stack: c eax ebx color register e a b d 45

46. Example of non 2-colorable graph c eax ebx color register e a b d stack: c b e a d Some graphs can’t be colored in K colors: 46

47. Example of non 2-colorable graph c eax ebx color register e a b d Some graphs can’t be colored in K colors: stack: b e a d 47

48. Example of non 2-colorable graph c eax ebx color register e a b d Some graphs can’t be colored in K colors: stack: e a d 48

49. Example of non 2-colorable graph c eax ebx color register e a b d Some graphs can’t be colored in K colors: stack: e a d no colors left for e! What can we do? 49

50. Chaitin’s algorithm [CC’82] Choose and remove an arbitrary node, marking it “troublesome” – Use heuristics to choose which one: – When adding node back in, it may be possible to find a valid color – Otherwise, we have to spill that node 50 ?

51. Chaitin’s algorithm [CC’82] Choose and remove an arbitrary node, marking it “troublesome” – Use heuristics to choose which one: – When adding node back in, it may be possible to find a valid color – Otherwise, we have to spill that node (lowest priority) 51 spill priority = (uo + 10 ui) / deg uo = #use+defs outside of loop ui = #use+defs inside of loop

52. Spilling Phase 3: Spill – once all nodes have K or more neighbors, pick a node for spilling There are many heuristics that can be used to pick a node Try to pick node not used much, not in inner loop Assign its storage to activation record – Remove it from graph and mark it as potential spill (technically, push on a spill stack) We can now repeat phases 1-2 without this node 52

53. Chaitin’s algorithm [CC’82] Phase 1: Simplify Phase 2: Select – Unwind stack and reconstruct the graph as follows: Pop (non-spill) variable from the stack Add it back to the graph Color the node for that variable with a color that it doesn’t interfere with its neighbors – Unwind spill stack Pop spill variable If there is an available color add back to graph Otherwise mark variable as actual spill Phase 3: Spill – If all nodes have K or more neighbors, pick a “heavy” node for spilling and add to potential spill stack Phase 4: Rewrite – Rewrite code for actual spill variables – Recompute liveness information – Repeat phases 1-3 53

54. Chaitin’s algorithm [CC’82] 54 Simplify Mark potential spills Select colors and detect actual spills Rewrite code to implement actual spills Liveness analysis any actual spill done any potential spill done

55. 55 Potential spill example

56. Color the following graph 56 c a b e AX BX color register CX d f (color) stack: potential spill:

57. Potential spill example Pick a for potential spill 57 c a b e AX BX color register CX d f (color) stack: potential spill:

58. Potential spill example Simplify d 58 c a b e AX BX color register CX d f (color) stack: potential spill: a

59. Potential spill example Simplify e 59 c a b e AX BX color register CX d f (color) stack: d potential spill: a

60. Potential spill example Simplify b 60 c a b e AX BX color register CX d f (color) stack: e d potential spill: a

61. Potential spill example Simplify c 61 c a b e AX BX color register CX d f (color) stack: b e d potential spill: a

62. Potential spill example Simplify f 62 c a b e AX BX color register CX d f (color) stack: c b e d potential spill: a

63. Potential spill example Select color for f 63 c a b e AX BX color register CX d f (color) stack: f c b e d potential spill: a

64. Potential spill example Select color for c 64 c a b e AX BX color register CX d f (color) stack: c b e d potential spill: a

65. Potential spill example Select color for b 65 c a b e AX BX color register CX d f (color) stack: b e d potential spill: a

66. Potential spill example Select color for e 66 c a b e AX BX color register CX d f (color) stack: e d potential spill: a

67. Potential spill example Select color for d 67 c a b e AX BX color register CX d f (color) stack: d potential spill: a

68. Potential spill example Select color for a 68 c a b e AX BX color register CX d f (color) stack: potential spill: a

69. Potential spill example Done – no actual spill 69 c a b e AX BX color register CX d f (color) stack: potential spill:

70. 70 actual spill example

71. Actual spill example Apply Chaitin’s algorithm for the following program – The set of registers is AX, BX, CX – Use the Briggs criterion for conservative coalescing – Upon any non-deterministic choice, choose by lexicographic order 71 c := e; a := a + e; d := a + c; d := a + b; d := d + b; // live = {d}

72. Step 1: compute liveness Let’s compute liveness information 72 {a, b, e} c := e; {a, b, c, e} a := a + e; {a, b, c} d := a + c; {a, b, d} d := a + b; {b, d} d := d + b; // live = {d}

73. Step 2: draw RIG Simplify d 73 c e a b d (color) stack: potential spill: AX BX color register CX

74. Potentially spill b #use+def(a)=4, #use+def(b)=2, #use+def(c)=2, #use+def(d)=5, #use+def(e)=2 (potentially) spill b 74 c e a b d (color) stack: d potential spill: AX BX color register CX c := e; a := a + e; d := a + c; d := a + b; d := d + b; // live = {d}

75. Simplify Simplify a 75 c e a b d (color) stack: d potential spill: b AX BX color register CX

76. Simplify Simplify c 76 c e a b d (color) stack: a d potential spill: b AX BX color register CX

77. Simplify Simplify e 77 c e a b d (color) stack: c a d potential spill: b AX BX color register CX

78. Attempt to color nodes on stack Select color for e 78 c e a b d (color) stack: e c a d potential spill: b AX BX color register CX

79. Attempt to color nodes on stack Select color for c 79 c e a b d (color) stack: c a d potential spill: b AX BX color register CX

80. Attempt to color nodes on stack Select color for a 80 c e a b d (color) stack: a d potential spill: b AX BX color register CX

81. Attempt to color nodes on stack Select color for d 81 c e a b d (color) stack: potential spill: b AX BX color register CX

82. b cannot be colored Cannot color b – actual spill 82 c e a b d (color) stack: potential spill: b AX BX color register CX

83. Actual spill: rich assembly Suppose assembly allows commands of the form x:=y+M[offset] Rewrite program for spilled variable b – Choose location on frame: b_loc 83 c := e; a := a + e; d := a + c; d := a + M[b_loc]; d := d + M[b_loc]; // live = {d} c := e; a := a + e; d := a + c; d := a + b; d := d + b; // live = {d}

84. Actual spill: basic assembly Rewrite program for spilled variable b – Choose location on frame: b_loc – Use temporaries for reading from frame: b1, b2 – Now attempt to color all variables, including temporaries If unable don’t spill temporaries, choose other variables to spill, otherwise can go into infinite spill-color loop 84 c := e; a := a + e; d := a + c; b1 := M[b_loc]; d := a + b1; b2 := M[b_loc]; d := d + b2; // live = {d} c := e; a := a + e; d := a + c; d := a + b; d := d + b; // live = {d}

85. Compute liveness for new program 85 {a, e} c := e; {a, c, e} a := a + e; {a, c} d := a + c; {a} b1 := M[b_loc]; {a, b1} d := a + b1; {d} b2 := M[b_loc]; {d, b2} d := d + b2; // live = {d}

86. Attempt to color Simplify b1, b2, d, a, c, e Select colors 86 c e a b2 d AX BX color register CX b1

87. Attempt to color Simplify b1, b2, d, a, c, e Select colors 87 c e a b2 d AX BX color register CX b1

88. Emit code based on colors 88 c e a b2 d AX BX color register CX b1 BX := AX; CX := CX + AX; AX := CX + BX; BX := M[b_loc]; AX := CX + BX; BX := M[b_loc]; AX := AX + BX; c := e; a := a + e; d := a + c; b1 := M[b_loc]; d := a + b1; b2 := M[b_loc]; d := d + b2;

89. 89 Register constraints

90. Handling precolored nodes Some variables are pre-assigned to registers – e.g.: mul on x86/pentium Uses eax; defines eax, edx – e.g.: call on x86/pentium Defines (trashes) caller-save registers eax, ecx, edx To properly allocate registers, treat these register uses as special temporary variables and enter into interference graph as precolored nodes 90

91. Handling precolored nodes Simplify. Never remove a pre-colored node – it already has a color, i.e., it is a given register Select. Once simplified graph has all colored nodes, add other nodes back in and color them using precolored nodes as starting point 91

92. 92 Optimizing moves

93. Optimizing move instructions Code generation produces a lot of extra MOVE instructions MOVE t5, t9 If we can assign t5 and t9 to same register, we can get rid of the MOVE – Effectively, copy propagation at the register allocation level Idea: if t5 and t9 are not connected in inference graph, coalesce them into a single variable; the move will be redundant 93

94. RIG with MOVE edges Add a second type of edges – MOVE edges 94 b ed a c eax ebx color register

95. Coalescing Problem: coalescing nodes can make a graph un-colorable – Conservative coalescing heuristic What should we do ? 95 aba/b coalesce MOVE

96. Conservative coalescing Coalesce MOVE edge (a,b) if it does not affect colorability A node is heavy if its degree is ≥K and light otherwise Briggs criterion: coalesce only if the merged node ab has <K heavy neighbors Reason: – Simplify will first remove all light neighbors – ab will then be adjacent to <K neighbors – Simplify can remove ab 96

97. Conservative coalescing Coalesce MOVE edge (a,b) if it does not affect colorability A node is heavy if its degree is ≥K and light otherwise George criterion: coalesce only if all heavy neighbors of a are also neighbors of b Reason: – Simplify can remove all light neighbors of a – Remaining heavy neighbors of a are neighbors of b so coalescing does not increase the degree of any node 97

98. Coalescing criterion example c eax ebx color register e a b 98 Can we coalesce (a,e) ? By the Briggs criterion? – #(heavy-neighbors(a,e) < K By the George criterion? – All heavy neighbors of a are also neighbors of e

99. Coalescing criterion example c eax ebx color register e a b 99 Can we coalesce (a,e) ? By the Briggs criterion? NO – #(heavy-neighbors(a,e) < K By the George criterion? YES – All heavy neighbors of a are also neighbors of e

100. Simplify, coalesce and freeze Phase 1: Simplify – Step 1 (simplify): simplify as much as possible without removing nodes that are the source or destination of a move (MOVE-related nodes) – Step 2 (coalesce): coalesce a MOVE-related edge provided low-degree node results – Step 3 (freeze): if neither steps 1 or 2 apply, freeze a MOVE instruction: low-degree nodes involved are marked not MOVE-related (remove MOVE edge) and try step 1 again – Step 4 (spill): if all nodes are heavy select a candidate for spilling using a priority function and move to potential spill stack Phase 2: Select – Unwind stack and reconstruct the graph as follows: Pop variable from the stack Add it back to the graph Color the node node with a color that it doesn’t interfere with – Unwind potential spill stack and attempt to color node – if unable mark corresponding variable as actual spill Phase 4: Rewrite – Allocate position in frame for spilled variable v – On each usage of v load to v i from frame 100

101. Simplify, coalesce and freeze 101 Simplify: recursively remove non-MOVE nodes with <K neighbors, pushing them onto stack Coalesce: conservatively merge unconstrained MOVE-related nodes with <K “heavy” neighbors Freeze: give up coalescing on some low-degree MOVE-related node any coalesce done any freeze done any simplify done

102. Overall algorithm 102 Simplify, freeze and coalesce Simplify, freeze and coalesce Mark potential spills Select colors and detect actual spills Rewrite code to implement actual spills Liveness analysis any actual spill done any potential spill done

103. 103 Coalesce example

104. Apply Chaitin’s algorithm for the following program – The set of registers is AX, BX, CX – Use the Briggs criterion for conservative coalescing – Upon any non-deterministic choice, choose by lexicographic order 104 a := e; d := a + c; d := d + b; // live = {d}

105. Step 1: compute liveness 105 {b, c, e} a := e; {a, b, c} d := a + c; {b, d} d := d + b; // live = {d}

106. Attempt to color Simplify d 106 c e a b d (color) stack: potential spill: AX BX color register CX

107. Coalesce step Cannot simplify a or e because they are MOVE-related Coalesce (a,e) 107 c e a b d (color) stack: d potential spill: AX BX color register CX

108. Simplify Simplify ae 108 c ae b d (color) stack: d potential spill: AX BX color register CX

109. Simplify Simplify b 109 c ae b d (color) stack: ae d potential spill: AX BX color register CX

110. Simplify Simplify c 110 c ae b d (color) stack: b ae d potential spill: AX BX color register CX

111. Select Color c 111 c ae b d (color) stack: c b ae d potential spill: AX BX color register CX

112. Select Color b 112 c ae b d (color) stack: b ae d potential spill: AX BX color register CX

113. Select Color ae 113 c ae b d (color) stack: ae d potential spill: AX BX color register CX

114. Select Color d 114 c ae b d (color) stack: d potential spill: AX BX color register CX

115. Select 115 c ae b d (color) stack: potential spill: AX BX color register CX

116. Emit code 116 CX := CX; AX := CX + AX; AX := AX + BX; c AX BX color register ae b d CX a := e; d := a + c; d := d + b;

117. 117 Freeze example

118. Step 1: compute liveness 118 {b, c, e} a := e; {a, b, c} d := a + c; {b, d} d := d + b; // live = {d}

119. Attempt to color Simplify d 119 c e a b d (color) stack: potential spill: AX BX color register

120. Attempt to color Can we coalesce a and e according to Briggs? Let’s try 120 c e a b (color) stack: d potential spill: AX BX color register

121. Unsuccessful attempt to color No, node becomes heavy Undo coalesce and try something else 121 c a&e b (color) stack: d potential spill: AX BX color register

122. Freeze: simplify a 122 c e a b (color) stack: d potential spill: AX BX color register

123. Simplify c 123 c e b (color) stack: a d potential spill: AX BX color register

124. Simplify b 124 e b (color) stack: c a d potential spill: AX BX color register

125. Simplify e 125 e (color) stack: b c a d potential spill: AX BX color register

126. Select color for e 126 (color) stack: e b c a d potential spill: AX BX color register

127. Select color for b 127 (color) stack: b c a d potential spill: AX BX color register e

128. Select color for c 128 (color) stack: c a d potential spill: AX BX color register e b

129. Select color for a 129 (color) stack: a d potential spill: AX BX color register e bc

130. Select color for d 130 (color) stack: d potential spill: AX BX color register e bc a

131. Select color for d 131 (color) stack: potential spill: AX BX color register e bc a d

132. 132 Linear scan register allocation

133. Live ranges and live intervals The live range for a variable is the set of program points at which the variable is alive The live interval for a variable is the smallest sub-range of the IR code containing all of a variable’s live ranges – A property of the IR code, not the CFG – Less precise than live ranges, but simpler to work with 133

134. d Live ranges and live intervals 134 e := d + a f := b + c f := f + b IfZ e Goto _L0 d := e + f d := e – f Goto _L1 _L0: d := e-f _L1: g := d Ret g abc e fg

135. Register allocation with live ranges Given the live intervals for all the variables in the program, we can allocate registers using a simple greedy algorithm Idea: Track which registers are free at each point When a live interval begins, give that variable a free register When a live interval ends, the register is once again free We can't always fit everything into a register (spill) 135 dabc e fg

136. Example 136 R0R0 R1R1 R2R2 R3R3 dabc e fg Free registers

137. Example 137 R0R0 R1R1 R2R2 R3R3 dabc e fg Free registers

138. Example 138 R0R0 R1R1 R2R2 R3R3 dabc e fg Free registers

139. Example 139 R0R0 R1R1 R2R2 R3R3 dabc e fg Free registers

140. Example 140 R0R0 R1R1 R2R2 R3R3 dabc e fg Free registers

141. Example 141 R0R0 R1R1 R2R2 R3R3 dabc e fg Free registers

142. Example 142 R0R0 R1R1 R2R2 R3R3 dabc e fg Free registers

143. Example 143 R0R0 R1R1 R2R2 R3R3 dabc e fg Free registers

144. Example 144 R0R0 R1R1 R2R2 R3R3 dabc e fg Free registers

145. Example 145 dabc e fg Free registers R0R0 R1R1 R2R2 R3R3

146. Example 146 dabc e fg Free registers R0R0 R1R1 R2R2 R3R3

147. Example 147 dabc e fg Free registers R0R0 R1R1 R2R2 R3R3

148. Another example 148 R0R0 R1R1 R2R2 dabc e fg Free registers

149. Another example 149 R0R0 R1R1 R2R2 dabc e fg Free registers

150. Another example 150 R0R0 R1R1 R2R2 dabc e fg Free registers

151. Another example 151 R0R0 R1R1 R2R2 dabc e fg Free registers

152. Another example 152 R0R0 R1R1 R2R2 dabc e fg Free registers Which register should we use?

153. Another example 153 R0R0 R1R1 R2R2 dabc e fg Free registers Which variable should we spill? One with longer interval

154. Another example 154 R0R0 R1R1 R2R2 dabc e fg Free registers

155. Another example 155 R0R0 R1R1 R2R2 dabc e fg Free registers

156. Another example 156 R0R0 R1R1 R2R2 dabc e fg Free registers

157. Another example 157 R0R0 R1R1 R2R2 dabc e fg Free registers

158. Another example 158 R0R0 R1R1 R2R2 dabc e fg Free registers

159. Another example 159 R0R0 R1R1 R2R2 dabc e fg Free registers

160. Another example 160 R0R0 R1R1 R2R2 dabc e fg Free registers

161. Another example 161 R0R0 R1R1 R2R2 dabc e fg Free registers

162. Another example 162 R0R0 R1R1 R2R2 dabc e fg Free registers

163. Linear scan register allocation A relatively new algorithm (Polleto and Sarkar) Advantages: – Very efficient (after computing live intervals, runs in linear time) – Produces good code in many instances – Allocation step works in one pass; can generate code during iteration – Often used in JIT compilers like Java HotSpot Disadvantage – Imprecise due to use of live intervals rather than live ranges 163

164. Comparison to RA by coloring 164

165. RA recap Graph-coloring Linear scan 165

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