Course project presentations

1. Course project presentations • Midterm project presentation • Originally scheduled for Tuesday Nov 4th • Can move to Th Nov 6th or Th Nov 13th

2. From last time: Loop-invariant code motion • Two steps: analysis and transformations • Step1: find invariant computations in loop • invariant: computes same result each time evaluated • Step 2: move them outside loop • to top if used within loop: code hoisting • to bottom if used after loop: code sinking

3. Example

4. Detecting loop invariants • An expression is invariant in a loop L iff: (base cases) • it’s a constant • it’s a variable use, all of whose defs are outside of L (inductive cases) • it’s a pure computation all of whose args are loop-invariant • it’s a variable use with only one reaching def, and the rhs of that def is loop-invariant

5. Computing loop invariants • Option 1: iterative dataflow analysis • optimistically assume all expressions loop-invariant, and propagate • Option 2: build def/use chains • follow chains to identify and propagate invariant expressions • Option 3: SSA • like option 2, but using SSA instead of ef/use chains

6. Example using def/use chains • An expression is invariant in a loop L iff: (base cases) • it’s a constant • it’s a variable use, all of whose defs are outside of L (inductive cases) • it’s a pure computation all of whose args are loop-invariant • it’s a variable use with only one reaching def, and the rhs of that def is loop-invariant

7. Example using def/use chains • An expression is invariant in a loop L iff: (base cases) • it’s a constant • it’s a variable use, all of whose defs are outside of L (inductive cases) • it’s a pure computation all of whose args are loop-invariant • it’s a variable use with only one reaching def, and the rhs of that def is loop-invariant

8. Example using def/use chains • An expression is invariant in a loop L iff: (base cases) • it’s a constant • it’s a variable use, all of whose defs are outside of L (inductive cases) • it’s a pure computation all of whose args are loop-invariant • it’s a variable use with only one reaching def, and the rhs of that def is loop-invariant

9. Loop invariant detection using SSA • An expression is invariant in a loop L iff: (base cases) • it’s a constant • it’s a variable use, all of whose single defs are outside of L (inductive cases) • it’s a pure computation all of whose args are loop-invariant • it’s a variable use whose single reaching def, and the rhs of that def is loop-invariant •  functions are not pure

10. Example using SSA • An expression is invariant in a loop L iff: (base cases) • it’s a constant • it’s a variable use, all of whose single defs are outside of L (inductive cases) • it’s a pure computation all of whose args are loop-invariant • it’s a variable use whose single reaching def, and the rhs of that def is loop-invariant •  functions are not pure

11. Example using SSA and preheader • An expression is invariant in a loop L iff: (base cases) • it’s a constant • it’s a variable use, all of whose single defs are outside of L (inductive cases) • it’s a pure computation all of whose args are loop-invariant • it’s a variable use whose single reaching def, and the rhs of that def is loop-invariant •  functions are not pure

12. Code motion

13. Example

14. Lesson from example: domination restriction

15. Domination restriction in for loops

16. Domination restriction in for loops

17. Avoiding domination restriction

18. Another example

19. Data dependence restriction

20. Avoiding data restriction

21. Summary of Data dependencies • We’ve seen SSA, a way to encode data dependencies better than just def/use chains • makes CSE easier • makes loop invariant detection easier • makes code motion easier • Now we move on to looking at how to encode control dependencies

22. Control Dependencies • A node (basic block) Y is control-dependent on another X iff X determines whether Y executes • there exists a path from X to Y s.t. every node in the path other than X and Y is post-dominated by Y • X is not post-dominated by Y

23. Control Dependencies • A node (basic block) Y is control-dependent on another X iff X determines whether Y executes • there exists a path from X to Y s.t. every node in the path other than X and Y is post-dominated by Y • X is not post-dominated by Y

24. Example

25. Example

26. Control Dependence Graph • Control dependence graph: Y descendent of X iff Y is control dependent on X • label each child edge with required condition • group all children with same condition under region node • Program dependence graph: super-impose dataflow graph (in SSA form or not) on top of the control dependence graph

27. Example

28. Example

29. Another example

30. Another example

31. Another example

32. Summary of Control Depence Graph • More flexible way of representing control-depencies than CFG (less constraining) • Makes code motion a local transformation • However, much harder to convert back to an executable form

33. Course summary so far • Dataflow analysis • flow functions, lattice theoretic framework, optimistic iterative analysis, precision, MOP • Advanced Program Representations • SSA, CDG, PDG • Along the way, several analyses and opts • reaching defns, const prop & folding, available exprs & CSE, liveness & DAE, loop invariant code motion • Next: dealing with procedures

34. Interprocedural analyses and optimizations

35. Costs of procedure calls • Up until now, we treated calls conservatively: • make the flow function for call nodes return top • start iterative analysis with incoming edge of the CFG set to top • This leads to less precise results: “lost-precision” cost • Calls also incur a direct runtime cost • cost of call, return, argument & result passing, stack frame maintainance • “direct runtime” cost

36. Addressing costs of procedure calls • Technique 1: try to get rid of calls, using inlining and other techniques • Technique 2: interprocedural analysis, for calls that are left

37. Inlining • Replace call with body of callee • Turn parameter- and result-passing into assignments • do copy prop to eliminate copies • Manage variable scoping correctly • rename variables where appropriate

38. Call graph nodes are procedures edges are calls, labelled by invocation counts/frequency Hard cases for builing call graph calls to/from external routines calls through pointers, function values, messages Where in the compiler should inlining be performed? Program representation for inlining

39. Inlining pros and cons (discussion)

40. Inlining pros and cons • Pros • eliminate overhead of call/return sequence • eliminate overhead of passing args & returning results • can optimize callee in context of caller and vice versa • Cons • can increase compiled code space requirements • can slow down compilation • recursion? • Virtual inlining: simulate inlining during analysis of caller, but don’t actually perform the inlining

41. Which calls to inline (discussion) • What affects the decision as to which calls to inline?

42. Which calls to inline • What affects the decision as to which calls to inline? • size of caller and callee (easy to compute size before inlining, but what about size after inlining?) • frequency of call (static estimates or dynamic profiles) • call sites where callee benefits most from optimization (not clear how to quantify) • programmer annotations (if so, annotate procedure or call site? Also, should the compiler really listen to the programmer?)

43. Inlining heuristics • Strategy 1: superficial analysis • examine source code of callee to estimate space costs, use this to determine when to inline • doesn’t account for post-inlining optimizations • How can we do better?

44. Inlining heuristics • Strategy 2: deep analysis • perform inlining • perform post-inlining analysis/optimizations • estimate benefits from opts, and measure code space after opts • undo inlining if costs exceed benefits • better accounts for post-inlining effects • much more expensive in compile-time • How can we do better?

45. Inlining heuristics • Strategy 3: amortized version of 2 [Dean & Chambers 94] • perform strategy 2: an inlining “trial” • record cost/benefit trade-offs in persistent database • reuse previous cost/benefit results for “similar” call sites

46. Inlining heuristics • Strategy 4: use machine learning techniques • For example, use genetic algorithms to evolve heuristics for inlining • fitness is evaluated on how well the heuristics do on a set of benchmarks • cross-populate and mutate heuristics • Can work surprisingly well to derive various heuristics for compilres

47. Another way to remove procedure calls int f(...) { if (...) return g(...); ... return h(i(....), j(...)); }

48. Tail call eliminiation • Tail call: last thing before return is a call • callee returns, then caller immediately returns • Can splice out one stack frame creation and destruction by jumping to callee rather than calling • callee reuses caller’s stack frame & return address • callee will return directly to caller’s caller • effect on debugging?

49. Tail recursion elimination • If last operation is self-recursive call, what does tail call elimination do?

50. Tail recursion elimination • If last operation is self-recursive call, what does tail call elimination do? • Transforms recursion into loop: tail recursion elimination • common optimization in compilers for functional languages • required by some language specifications, eg Scheme • turns stack space usage from O(n) to O(1)