Lecture07
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Lecture07 Document Transcript

  • 1. Static Single Assignment FormLast Time – Lattice theoretic frameworks for data-flow analysisToday – Program representations – Static single assignment (SSA) form – Program representation for sparse data-flow – Conversion to and from SSANext Time – Reuse optimizationsCIS570 Lecture 7 Static Single Assignment Form 2Data DependenceDefinition – Data dependences are constraints on the order in which statements may be executedTypes of dependences – Flow (true) dependence: s1 writes memory that s2 later reads (RAW) – Anti-dependence: s1 reads memory that s2 later writes (WAR) – Output dependences: s1 writes memory that s2 later writes (WAW) – Input dependences: s1 reads memory that s2 later reads (RAR)True dependences – Flow dependences represent actual flow of dataFalse dependences – Anti- and output dependences reflect reuse of memory, not actual data flow; can often be eliminatedCIS570 Lecture 7 Static Single Assignment Form 3 1
  • 2. Example s1 a = b; flow s2 b = c + d; anti s3 e = a + d; output s4 b = 3; input s5 f = b * 2;CIS570 Lecture 7 Static Single Assignment Form 4Representing Data DependencesImplicitly – Use variable defs and uses – Pros: simple – Cons: hides data dependence (analyses must find this info)Def-use chains (du chains) – Link each def to its uses – Pros: explicit; therefore fast – Cons: must be computed and updated, consumes spaceAlternate representations – e.g., Static single assignment form (SSA), dependence flow graphs (DFG), value dependence graphs (VDG)CIS570 Lecture 7 Static Single Assignment Form 5 2
  • 3. DU ChainsDefinition – du chains link each def to its usesExample s1 a = b; s2 b = c + d; du chain s3 e = a + d; s4 b = 3; s5 f = b * 2;CIS570 Lecture 7 Static Single Assignment Form 6UD ChainsDefinition – ud chains link each use to its defsExample s1 a = b; s2 b = c + d; ud chain s3 e = a + d; s4 b = 3; s5 f = b * 2;CIS570 Lecture 7 Static Single Assignment Form 7 3
  • 4. Role of Alternate Program RepresentationsProcess Original Code (RTL) Optimized Code (RTL) T1 T2 SSA Code1 SSA Code2 SSA Code3Advantage – Allow analyses and transformations to be simpler & more efficient/effectiveDisadvantage – May not be “executable” (requires extra translations to and from) – May be expensive (in terms of time or space)CIS570 Lecture 7 Static Single Assignment Form 8Static Single Assignment (SSA) FormIdea – Each variable has only one static definition – Makes it easier to reason about values instead of variables – Similar to the notion of functional programmingTransformation to SSA – Rename each definition – Rename all uses reached by that assignmentExample v := ... v0 := ... ... := ... v ... ... := ... v0 ... v := ... v1 := ... ... := ... v ... ... := ... v1 ...What do we do when there’s control flow?CIS570 Lecture 7 Static Single Assignment Form 9 4
  • 5. SSA and Control Flow Problem – A use may be reached by several definitions 12 v := ... 3 v := ... 1 4 ...v... 2 v0 :=... 3 v1 :=... 4 ...v?... CIS570 Lecture 7 Static Single Assignment Form 10 SSA and Control Flow (cont) Merging Definitions – φ-functions merge multiple reaching definitions Example 1 2 v0 :=... 3 v1 :=... 4 v2 := φ(v0,v1) ...v2... CIS570 Lecture 7 Static Single Assignment Form 11 5
  • 6. Another Example 1 v := 1 1 v0 := 1 2 v := v+1 v1 := φ(v0,v2) 2 v2 := v1+1CIS570 Lecture 7 Static Single Assignment Form 12SSA vs. ud/du ChainsSSA form is more constrainedAdvantages of SSA – More compact – Some analyses become simpler when each use has only one def – Value merging is explicit – Easier to update and manipulate?Furthermore – Eliminates false dependences (simplifying context) for (i=0; i<n; i++) A[i] = i; Unrelated uses of i are given for (i=0; i<n; i++) different variable names print(foo(i));CIS570 Lecture 7 Static Single Assignment Form 13 6
  • 7. SSA vs. ud/du Chains (cont)Worst case du-chains? switch (c1) { case 1: x = 1; break; case 2: x = 2; break; case 3: x = 3; break; } x4 = φ(x1, x2, x3) switch (c2) { case 1: y1 = x; break; case 2: y2 = x; break; case 3: y3 = x; break; case 4: y4 = x; break; } m defs and n uses leads to m×n du chainsCIS570 Lecture 7 Static Single Assignment Form 14Transformation to SSA FormTwo steps – Insert φ-functions – Rename variablesCIS570 Lecture 7 Static Single Assignment Form 15 7
  • 8. Where Do We Place φ-Functions?Basic Rule – If two distinct (non-null) paths x→z and y→z converge at node z, and nodes x and y contain definitions of variable v, then a φ-function for v is inserted at z x v1 :=... y v2 :=... z v3 := φ(v1,v2) ...v3...CIS570 Lecture 7 Static Single Assignment Form 16Approaches to Placing φ-FunctionsMinimal – As few as possible subject to the basic ruleBriggs-Minimal – Same as minimal, except v must be live across some edge of the CFG v = v = Briggs Minimal will not place a = v = v φ function in this case because v is not live across any CFG edge no uses of vCIS570 Lecture 7 Static Single Assignment Form 17 8
  • 9. Approaches to Placing φ-Functions (cont)Briggs-Minimal – Same as minimal, except v must be live across some edge of the CFGPruned – Same as minimal, except dead φ-functions are not inserted v = v = Will Pruned place a φ function at = v = v this merge? no uses of vWhat’s the difference between Briggs Minimal and Pruned SSA?CIS570 Lecture 7 Static Single Assignment Form 18Briggs Minimal vs. Pruned v = v = = v = v v2= φ(v0,v1) = v2 Briggs Minimal will add a φ function because v is live across the blue edge, but Pruned = φ(v2,v3) SSA will not because the φ function is dead Why would we ever use Briggs Minimal instead of Pruned SSA?CIS570 Lecture 7 Static Single Assignment Form 19 9
  • 10. Machinery for Placing φ-Functions entryRecall Dominators – d dom i if all paths from entry to node i include d d d dom i – d sdom i if d dom i and d≠iDominance Frontiers i – The dominance frontier of a node d is the set of nodes that are “just barely” not dominated by d; i.e., the set of nodes n, such that – d dominates a predecessor p of n, and – d does not strictly dominate n – DF(d) = {n | ∃p∈pred(n), d dom p and d !sdom n}Notational Convenience – DF(S) = ∪s∈S DF(s)CIS570 Lecture 7 Static Single Assignment Form 20Dominance Frontier Example DF(d) = {n | ∃p∈pred(n), d dom p and d !sdom n} Nodes in Dom(5) Dom(5) = {5, 6, 7, 8} 1DF(5) = {4, 5, 12, 13} 2 5 9 3 6 7 10 11 4 8 12 13What’s significant about the Dominance Frontier?In SSA form, definitions must dominate usesCIS570 Lecture 7 Static Single Assignment Form 21 10
  • 11. Dominance Frontier Example II DF(d) = {n | ∃p∈pred(n), d dom p and d !sdom n} Nodes in Dom(5) Dom(5) = {5, 6, 7, 8} 1DF(5) = {4, 5, 13} 2 5 3 6 7 4 8 13 In this new graph, node 4 is the first point of convergence between the entry and node 5, so do we need a φ- function at node 13?CIS570 Lecture 7 Static Single Assignment Form 22SSA Exercise 1 2 v3 :=... 73 4 8 v1 := ... 9 v2 := ... 5 10 v4:=φ(v1,v2) 6 v5:=φ(v3,v4) DF(8) = {10} DF(9) = {10} DF(2) = {6} DF(d) = {n | ∃p∈pred(n), d dom p and d !sdom n} DF({8,9}) = {10} DF(10) = {6} DF({2,6,8,9,10}) = {6,10}CIS570 Lecture 7 Static Single Assignment Form 23 11
  • 12. Dominance Frontiers Revisited Suppose that node 3 defines variable x x ∈ Def(3) DF(3) = {5} 1 2 3 4 5 6Do we need to insert a φ- function for x anywhere else? Yes. At node 6. Why?CIS570 Lecture 7 Static Single Assignment Form 24Dominance Frontiers and SSALet – DF1(S) = DF(S) – DFi+1(S) = DF(S ∪ DFi(S))Iterated Dominance Frontier – DF∞(S)Theorem – If S is the set of CFG nodes that define variable v, then DF∞(S) is the set of nodes that require φ-functions for vCIS570 Lecture 7 Static Single Assignment Form 25 12
  • 13. Algorithm for Inserting φ-Functionsfor each variable v WorkList ← ∅ EverOnWorkList ← ∅ AlreadyHasPhiFunc ← ∅ for each node n containing an assignment to v Put all defs of v on the worklist WorkList ← WorkList ∪ {n} EverOnWorkList ← WorkList while WorkList ≠ ∅ Remove some node n from WorkList for each d ∈ DF(n) if d ∉ AlreadyHasPhiFunc Insert at most one φ function per node Insert a φ-function for v at d AlreadyHasPhiFunc ← AlreadyHasPhiFunc ∪ {d} if d ∉ EverOnWorkList Process each node at most once WorkList ← WorkList ∪ {d} EverOnWorkList ← EverOnWorkList ∪ {d}CIS570 Lecture 7 Static Single Assignment Form 26Variable RenamingBasic idea – When we see a variable on the LHS, create a new name for it – When we see a variable on the RHS, use appropriate subscriptEasy for straightline code x= x0 = =x = x0 x= x1 = =x = x1Use a stack when there’s control flow – For each use of x, find the definition of x that dominates it x= x0 = Traverse the dominance tree =x = x0CIS570 Lecture 7 Static Single Assignment Form 27 13
  • 14. Dominance Tree Example The dominance tree shows the dominance relation 1 1 2 5 9 2 4 5 13 9 12 3 6 7 10 11 3 6 8 7 10 11 4 8 12 Dominance Tree 13 CFGCIS570 Lecture 7 Static Single Assignment Form 28Variable Renaming (cont)Data Structures – Stacks[v] ∀v Holds the subscript of most recent definition of variable v, initially empty – Counters[v] ∀v Holds the current number of assignments to variable v; initially 0 pAuxiliary Routine po sh 1 procedure GenName(variable v) pu i := Counters[v] 2 4 5 13 9 12 push i onto Stacks[v] Counters[v] := i + 1 3 6 8 7 10 11 Use the Dominance Tree to remember the most recent definition of each variableCIS570 Lecture 7 Static Single Assignment Form 29 14
  • 15. Variable Renaming Algorithmprocedure Rename(block b) Call Rename(entry-node) for each φ-function p in b GenName(LHS(p)) and replace v with vi, where i=Top(Stack[v]) for each statement s in b (in order) for each variable v ∈ RHS(s) replace v by vi, where i = Top(Stacks[v]) for each variable v ∈ LHS(s) GenName(v) and replace v with vi, where i=Top(Stack[v]) for each s ∈ succ(b) (in CFG) j ← position in s’s φ-function corresponding to block b for each φ-function p in s Φ( , , ) replace the j th operand of RHS(p) by v , where i = Top(Stack[v]) i for each s ∈ child(b) (in DT) Rename(s) Recurse using Depth First Search for each φ-function or statement t in b for each vi ∈ LHS(t) Unwind stack when done with this node Pop(Stack[v])CIS570 Lecture 7 Static Single Assignment Form 30Transformation from SSA FormProposal – Restore original variable names (i.e., drop subscripts) – Delete all φ-functionsComplications x0 = − What if versions get out of order? x1 = (simultaneously live ranges) = x0 = x1Alternative − Perform dead code elimination (to prune φ-functions) − Replace φ-functions with copies in predecessors − Rely on register allocation coalescing to remove unnecessary copiesCIS570 Lecture 7 Static Single Assignment Form 31 15
  • 16. ConceptsData dependences – Three kinds of data dependences – du-chainsAlternate representationsSSA formConversion to SSA form – φ-function placement – Dominance frontiers – Variable renaming – Dominance treesConversion from SSA formCIS570 Lecture 7 Static Single Assignment Form 32Next TimeAssignments – Project proposals dueLecture – Reuse optimizationsCIS570 Lecture 7 Static Single Assignment Form 33 16