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U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE Advanced Compilers CMPSCI 710 Spring 2003 Computing SSA Emery Berger University.

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Presentation on theme: "U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE Advanced Compilers CMPSCI 710 Spring 2003 Computing SSA Emery Berger University."— Presentation transcript:

1 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE Advanced Compilers CMPSCI 710 Spring 2003 Computing SSA Emery Berger University of Massachusetts, Amherst

2 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 2 More SSA Last time dominance SSA form Today Computing SSA form

3 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 3 Criteria for Inserting  Functions Brute-force: Insert one  function for each variable at every join point (point in CFG with more than one predecessor) Wasteful When should we insert  function for a variable a at node z of the CFG? Intuitively: add  if there are two definitions of a that can reach the point z through distinct paths

4 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 4 Path Convergence Criterion [Cytron-Ferrante ‘89] Insert  function for variable a at node z if all the following conditions are true: 1.There is a block x that defines a 2.There is a block y  x that defines a 3.There are non-empty paths x  z and y  z 4.Paths x  z and y  z don’t have nodes in common other than z 5.The node z does not appear within both x  z and y  z prior to the end, but it may appear in one or the other. Note: The start node contains an implicit definition of every variable.

5 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 5 Iterated Path-Convergence Criterion The  function itself is a definition of a. Therefore the path-convergence criterion is a set of equations that must be satisfied. while there are nodes x, y, z satisfying conditions 1-5 and z does not contain a  function for a do insert a   (a 0, a 1, …, a n ) at node z This algorithm is extremely costly, because it requires the examination of every triple of nodes x, y, z and every path from x to z and from y to z. Can we do better?

6 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 6 Dominance Reviewed Node x dominates node w if every path from the start node to w must go through x Node x strictly dominates node w if x dominates w and x  w

7 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 7 Dominance Property of SSA Form In SSA form, definitions dominate uses: If x is used in  function in block n  def of x dominates every predecessor of n If x is used in non-  statement in block n  def of x dominates n The dominance frontier of node x: nodes w such that x dominates predecessor of w, but x does not strictly dominate w

8 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 8 Example 1 13 2 3 4 12 1011 9 8 67 5 What is the dominance frontier of node 5?

9 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 9 Example 1 13 2 3 4 12 1011 9 8 67 5 First we must find all nodes that node 5 strictly dominates.

10 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 10 Example A node w is in the dominance frontier of node 5 if 5 dominates a predecessor of w, but 5 does not strictly dominate w itself. What is the dominance frontier of 5? 1 13 2 3 4 12 1011 9 8 67 5

11 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 11 Example 1 13 2 3 4 12 1011 9 8 67 5 DF(5) = {4, 5, 12, 13} A node w is in the dominance frontier of node 5 if 5 dominates a predecessor of w, but 5 does not strictly dominates w itself. What is the dominance frontier of 5?

12 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 12 Dominance Frontier Criterion Dominance Frontier Criterion: If a node x contains a definition of variable a, then any node z in the dominance frontier of x needs a  function for a. Can you think of an intuitive explanation for why a node in the dominance frontier of another node must be a join node?

13 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 13 Example 1 13 2 3 4 12 1011 9 8 67 5 If a node (12) is in the dominance frontier of another node (5), than there must be at least two paths converging to (12). These paths must be non-intersecting, and one of them (5,7,12) must contain a node strictly dominated by (5).

14 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 14 Dominator Tree To compute the dominance frontiers, we first compute the dominator tree of the CFG. There is an edge from node x to node y in the dominator tree if node x immediately dominates node y. I.e., x dominates y  x, and x does not dominate any other dominator of y. Dominator trees can be computed using the Lengauer-Tarjan algorithm in O(E  (E,N)) time

15 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 15 Example: Dominator Tree 1 13 2 3 4 12 1011 9 8 67 5 1 3 67 1011 45129213 8 Control Flow Graph Dominator Tree

16 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 16 Local Dominance Frontier Cytron-Ferrante define the local dominance frontier of a node n as: DF local [n] = successors of n in the CFG that are not strictly dominated by n

17 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 17 Example: Local Dominance Frontier 1 13 2 3 4 12 1011 9 8 67 5 Control Flow Graph In the example, what are the local dominance frontiers of nodes 5, 6 and 7? DF local [5] =  DF local [6] = {4,8} DF local [7] = {8,12} DF local [n] = successors of n in the CFG not strictly dominated by n

18 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 18 Dominance Frontier Inherited From Its Children The dominance frontier of a node n is formed by its local dominance frontier plus nodes that are passed up by the children of n in the dominator tree. The contribution of a node c to its parents’ dominance frontier is defined as [Cytron-Ferrante, 1991]: DF up [c] = nodes in the dominance frontier of c that are not strictly dominated by the immediate dominator of c

19 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 19 Example: Up Dominance Frontier 1 13 2 3 4 12 1011 9 8 67 5 Control Flow Graph In the example, what are the contributions of nodes 6, 7, and 8 to its parent dominance frontier? First we compute the DF and immediate dominator of each node: DF[6] = {4,8}, idom(6)= 5 DF[7] = {8,12}, idom(7)= 5 DF[8] = {5,13}, idom(8)= 5

20 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 20 Example: Up Dominance Frontier 1 13 2 3 4 12 1011 9 8 67 5 Control Flow Graph First, we compute the DF and the immediate dominator of each node: DF[6] = {4,8}, idom(6)= 5 DF[7] = {8,12}, idom(7)= 5 DF[8] = {5,13}, idom(8)= 5 Now, we check for the DF up condition: DF up [6] = {4} DF up [7] = {12} DF up [8] = {5,13} DF up [c] = nodes in DF[c] not strictly dominated by idom(c)

21 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 21 Dominance Frontier Inherited From Its Children The dominance frontier of a node n is formed by its local dominance frontier plus nodes that are passed up by the children of n in the dominator tree. Thus the dominance frontier of a node n is defined as [Cytron-Ferrante, 1991]:

22 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 22 Example: Local Dominance Frontier 1 13 2 3 4 12 1011 9 8 67 5 Control Flow Graph What is DF[5]? Remember that: DF local [5] =  DF up [6] = {4} DF up [7] = {12} DF up [8] = {5,13} DTchildren[5] = {6,7,8}

23 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 23 Example: Local Dominance Frontier 1 13 2 3 4 12 1011 9 8 67 5 Control Flow Graph What is DF[5]? Remember that: DF local [5] =  DF up [6] = {4} DF up [7] = {12} DF up [8] = {5,13} DTchildren[5] = {6,7,8} Thus, DF[5] = {4, 5, 12, 13}

24 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 24 Computing Dominance Frontiers Use DF local and DF up for X 2 bottom-up traversal of dominance tree DF(X) Ã  for Y 2 Succ(X) // local if idom(Y)  X then DF(X) Ã DF(X) [ {Y} for Z 2 Children(X) for Y 2 DF(Z) // up if idom(Y)  X then DF(X) Ã DF(X) [ {Y}

25 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 25 Join Sets In order to insert  -nodes for a variable x that is defined in a set of nodes S={n 1, n 2, …, n k } we need to compute the iterated set of join nodes of S. Given a set of nodes S of a control flow graph G, the set of join nodes of S, J(S), is defined as follows: J(S) ={z  G|  two paths P xz and P yz in G that have z as its first common node, x  S and y  S}

26 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 26 Iterated Join Sets Because a  -node is itself a definition of a variable, once we insert  -nodes in the join set of S, we need to find out the join set of S  J(S). Thus, Cytron-Ferrante define the iterated join set of a set of nodes S, J + (S), as the limit of the sequence:

27 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 27 Iterated Dominance Frontier We can extend the concept of dominance frontier to define the dominance frontier of a set of nodes as: Now we can define the iterated dominance frontier, DF + (S), of a set of nodes S as the limit of the sequence: Jose Nelson Amaral: Find an example in which the IDF of a set S is different from the DF of the set! Jose Nelson Amaral: Find an example in which the IDF of a set S is different from the DF of the set!

28 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 28 Location of  -Nodes Given a variable x that is defined in a set of nodes S={n 1, n 2, …, n k } the set of nodes that must receive  -nodes for x is J + (S). An important result proved by Cytron-Ferrante is that: Thus we are really interested in computing the iterated dominance frontier of a set of nodes.

29 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 29 Algorithms to Compute  Node Placement The algorithm to insert  -nodes, due to Cytron and Ferrante (1991), computes the dominance frontier of each node in the set S before computing the iterated dominance frontier of the set. In 1994, Shreedar and Gao proposed a simple, linear algorithm for the insertion of  -nodes. In the worst case, the combination of the dominance frontier of the sets can be quadratic in the number of nodes in the CFG. Thus, Cytron-Ferrante’s algorithm has a complexity O(N 2 ).

30 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 30 Sreedhar and Gao’s DJ Graph 1 13 2 3 4 12 1011 9 8 67 5 1 3 67 1011 45129213 8 Control Flow Graph Dominator Tree

31 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 31 Sreedhar and Gao’s DJ Graph 1 13 2 3 4 12 1011 9 8 67 5 1 3 67 1011 45129213 8 Control Flow Graph Dominator Tree D nodes

32 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 32 Sreedhar and Gao’s DJ Graph 1 13 2 3 4 12 1011 9 8 67 5 1 3 67 1011 45129213 8 Control Flow Graph Dominator Tree D nodes J nodes

33 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 33 Shreedar-Gao’s Dominance Frontier Algorithm DominanceFrontier(x) 0: DF[x] =  1: foreach y  SubTree(x) do 2: if((y  z == J-edge) and 3: (z.level  x.level)) 4: then DF[x] = DF[x]  z 1 3 67 1011 45129213 8 What is the DF[5]?

34 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 34 Shreedar-Gao’s Dominance Frontier Algorithm DominanceFrontier(x) 0: DF[x] =  1: foreach y  SubTree(x) do 2: if((y  z == J-edge) and 3: (z.level  x.level)) 4: then DF[x] = DF[x]  z 1 3 67 1011 45129213 8 SubTree(5) = {5, 6, 7, 8} Initialization: DF[5] = 

35 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 35 Shreedar-Gao’s Dominance Frontier Algorithm DominanceFrontier(x) 0: DF[x] =  1: foreach y  SubTree(x) do 2: if((y  z == J-edge) and 3: (z.level  x.level)) 4: then DF[x] = DF[x]  z 1 3 67 1011 45129213 8 SubTree(5) = {5, 6, 7, 8} There are three edges originating in 5: {5  6, 5  7, 5  8} but they are all D-edges Initialization: DF[5] = 

36 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 36 Shreedar-Gao’s Dominance Frontier Algorithm DominanceFrontier(x) 0: DF[x] =  1: foreach y  SubTree(x) do 2: if((y  z == J-edge) and 3: (z.level  x.level)) 4: then DF[x] = DF[x]  z 1 3 67 1011 45129213 8 SubTree(5) = {5, 6, 7, 8} There are two edges originating in 6: {6  4, 6  8} but 8.level > 5.level Initialization: DF[5] =  After visiting 6: DF = {4}

37 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 37 Shreedar-Gao’s Dominance Frontier Algorithm DominanceFrontier(x) 0: DF[x] =  1: foreach y  SubTree(x) do 2: if((y  z == J-edge) and 3: (z.level  x.level)) 4: then DF[x] = DF[x]  z 1 3 67 1011 45129213 8 SubTree(5) = {5, 6, 7, 8} There are two edges originating in 7: {7  8, 7  12} again 8.level > 5.level Initialization: DF[5] =  After visiting 6: DF = {4} After visiting 7: DF = {4,12}

38 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 38 Shreedar-Gao’s Dominance Frontier Algorithm DominanceFrontier(x) 0: DF[x] =  1: foreach y  SubTree(x) do 2: if((y  z == J-edge) and 3: (z.level  x.level)) 4: then DF[x] = DF[x]  z 1 3 67 1011 45129213 8 SubTree(5) = {5, 6, 7, 8} There are two edges originating in 8: {8  5, 8  13} both satisfy cond. in steps 2-3 Initialization: DF[5] =  After visiting 6: DF = {4} After visiting 7: DF = {4,12} After visiting 8: DF = {4, 12, 5, 13}

39 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 39 Shreedhar-Gao’s  -Node Insertion Algorithm Using the D-J graph, Shreedhar and Gao propose a linear time algorithm to compute the iterated dominance frontier of a set of nodes. A important intuition in Shreedhar-Gao’s algorithm is: If two nodes x and y are in S, and y is an ancestor of x in the dominator tree, than if we compute DF[x] first, we do not need to recompute it when computing DF[y].

40 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 40 Shreedhar-Gao’s  -Node Insertion Algorithm Shreedhar-Gao’s algorithm also use a work list of nodes hashed by their level in the dominator tree and a visited flag to avoid visiting the same node more than once. The basic operation of the algorithm is similar to their dominance frontier algorithm, but it requires a careful implementation to deliver the linear time complexity.

41 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 41 Dead-Code Elimination in SSA Form Only one definition for each variable ) if the list of uses of the variable is empty, definition is dead When a statement v  x  y is eliminated because v is dead, this statement must be removed from the list of uses of x and y, which might cause those definitions to become dead. Thus we need to iterate the dead code elimination algorithm.

42 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 42 Simple Constant Propagation in SSA If there is a statement v  c, where c is a constant, then all uses of v can be replaced for c. A  function of the form v   (c 1, c 2, …, c n ) where all c i are identical can be replaced for v  c. Using a work list algorithm in a program in SSA form, we can perform constant propagation in linear time In the next slide we assume that x, y, z are variables and a, b, c are constants.

43 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 43 Linear Time Optimizations in SSA form Copy propagation: The statement x   (y) or the statement x  y can be deleted and y can substitute every use of x. Constant folding: If we have the statement x  a  b, we can evaluate c  a  b at compile time and replace the statement for x  c Constant conditions: The conditional if a < b goto L1 else L2 can be replaced for goto L1 or goto L2, according to the compile time evaluation of a < b, and the CFG, use lists, adjust accordingly Unreachable Code: eliminate unreachable blocks.

44 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 44 Next Time Implementing other optimizations with SSA

45 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 45 Single Assignment Form i=1; j=1; k=0; while(k<100) { if(j<20) { j=i; k=k+1; } else { j=k; k=k+2; } return j; } i  1 j  1 k  0 j  i k  k+1 j  k k  k+2 return j if j<20 if k<100 B1 B2 B3 B5 B6 B4 B7

46 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 46 Single Assignment Form i=1; j=1; k=0; while(k<100) { if(j<20) { j=i; k=k+1; } else { j=k; k=k+2; } return j; } i  1 j  1 k1  0 j  i k3  k+1 j  k k5  k+2 return j if j<20 if k<100 B1 B2 B3 B5 B6 B4 B7

47 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 47 Single Assignment Form i=1; j=1; k=0; while(k<100) { if(j<20) { j=i; k=k+1; } else { j=k; k=k+2; } return j; } i  1 j  1 k1  0 j  i k3  k+1 j  k k5  k+2 return j if j<20 if k<100 k4   (k3,k5) B1 B2 B3 B5 B6 B4 B7

48 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 48 Single Assignment Form i=1; j=1; k=0; while(k<100) { if(j<20) { j=i; k=k+1; } else { j=k; k=k+2; } return j; } i  1 j  1 k1  0 j  i k3  k+1 j  k k5  k+2 return j if j<20 k2   (k4,k1) if k<100 k4   (k3,k5) B1 B2 B3 B5 B6 B4 B7

49 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 49 Single Assignment Form i=1; j=1; k=0; while(k<100) { if(j<20) { j=i; k=k+1; } else { j=k; k=k+2; } return j; } i  1 j  1 k1  0 j  i k3  k2+1 j  k k5  k2+2 return j if j<20 k2   (k4,k1) if k2<100 k4   (k3,k5) B1 B2 B3 B5 B6 B4 B7

50 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 50 Single Assignment Form i=1; j=1; k=0; while(k<100) { if(j<20) { j=i; k=k+1; } else { j=k; k=k+2; } return j; } i1  1 j1  1 k1  0 j3  i1 k3  k2+1 j5  k2 k5  k2+2 return j2 if j2<20 j2   (j4,j1) k2   (k4,k1) if k2<100 j4   (j3,j5) k4   (k3,k5) B1 B2 B3 B5 B6 B4 B7

51 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 51 Example: Constant Propagation i1  1 j1  1 k1  0 j3  i1 k3  k2+1 j5  k2 k5  k2+2 return j2 if j2<20 j2   (j4,j1) k2   (k4,k1) if k2<100 j4   (j3,j5) k4   (k3,k5) B1 B2 B3 B5 B6 B4 B7 i1  1 j1  1 k1  0 j3  1 k3  k2+1 j5  k2 k5  k2+2 return j2 if j2<20 j2   (j4,1) k2   (k4,0) if k2<100 j4   (j3,j5) k4   (k3,k5) B1 B2 B3 B5 B6 B4 B7

52 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 52 Example: Dead-code Elimination i1  1 j1  1 k1  0 j3  1 k3  k2+1 j5  k2 k5  k2+2 return j2 if j2<20 j2   (j4,1) k2   (k4,0) if k2<100 j4   (j3,j5) k4   (k3,k5) B1 B2 B3 B5 B6 B4 B7 j3  1 k3  k2+1 j5  k2 k5  k2+2 return j2 if j2<20 j2   (j4,1) k2   (k4,0) if k2<100 j4   (j3,j5) k4   (k3,k5) B2 B3 B5 B6 B4 B7

53 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 53 Constant Propagation and Dead Code Elimination j3  1 k3  k2+1 j5  k2 k5  k2+2 return j2 if j2<20 j2   (j4,1) k2   (k4,0) if k2<100 j4   (1,j5) k4   (k3,k5) B2 B3 B5 B6 B4 B7 j3  1 k3  k2+1 j5  k2 k5  k2+2 return j2 if j2<20 j2   (j4,1) k2   (k4,0) if k2<100 j4   (j3,j5) k4   (k3,k5) B2 B3 B5 B6 B4 B7

54 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 54 Example: Is this the end? But block 6 is never executed! How can we find this out, and simplify the program? SSA conditional constant propagation finds the least fixed point for the program and allows further elimination of dead code. k3  k2+1j5  k2 k5  k2+2 return j2 if j2<20 j2   (j4,1) k2   (k4,0) if k2<100 j4   (1,j5) k4   (k3,k5) B2 B3 B5 B6 B4 B7

55 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 55 Example: Dead code elimination k3  k2+1j5  k2 k5  k2+2 return j2 if j2<20 j2   (j4,1) k2   (k4,0) if k2<100 j4   (1,j5) k4   (k3,k5) B2 B3 B5 B6 B4 B7 B4 k3  k2+1 return j2 j2   (j4,1) k2   (k4,0) if k2<100 j4   (1) k4   (k3) B2 B5 B7

56 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 56 Example: Single Argument  - Function Elimination k3  k2+1 return j2 j2   (j4,1) k2   (k4,0) if k2<100 j4   (1) k4   (k3) B2 B5 B7 B4 k3  k2+1 return j2 j2   (j4,1) k2   (k4,0) if k2<100 j4  1 k4  k3 B2 B5 B7 B4

57 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 57 Example: Constant and Copy Propagation k3  k2+1 return j2 j2   (j4,1) k2   (k4,0) if k2<100 j4  1 k4  k3 B2 B5 B7 k3  k2+1 return j2 j2   (1,1) k2   (k3,0) if k2<100 j4  1 k4  k3 B2 B5 B7 B4

58 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 58 Example: Dead Code Elimination k3  k2+1 return j2 j2   (1,1) k2   (k3,0) if k2<100 j4  1 k4  k3 B2 B5 B7 B4 k3  k2+1 return j2 j2   (1,1) k2   (k3,0) if k2<100 B2 B5 B4

59 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 59 Example:  -Function Simplification k3  k2+1 return j2 j2   (1,1) k2   (k3,0) if k2<100 B2 B5 B4 k3  k2+1 return j2 j2  1 k2   (k3,0) if k2<100 B2 B5 B4

60 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 60 Example: Constant Propagation k3  k2+1 return j2 j2  1 k2   (k3,0) if k2<100 B2 B5 B4 k3  k2+1 return 1 j2  1 k2   (k3,0) if k2<100 B2 B5 B4

61 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 61 Example: Dead Code Elimination k3  k2+1 return 1 j2  1 k2   (k3,0) if k2<100 B2 B5 B4 return 1 B4

62 U NIVERSITY OF M ASSACHUSETTS, A MHERST D EPARTMENT OF C OMPUTER S CIENCE 62 Next Time

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