1. Administrative info Subscribe to the class mailing list –instructions are on the class web page, which is accessible from my home page, which is accessible by searching for Sorin Lerner on google Project page is up –Not much info there yet. More will be available by Thursday You will need to create groups for the project by Thursday. So start thinking about who you would like to work with.

2. Tour of common optimizations

3. Simple example foo(z) { x := 3 + 6; y := x – 5 return z * y }

4. Simple example foo(z) { x := 3 + 6; y := x – 5 return z * y }

5. Another example x := a + b;... y := a + b;

6. Another example x := a + b;... y := a + b;

7. Another example if (...) { x := a + b; }... y := a + b;

8. Another example if (...) { x := a + b; }... y := a + b;

9. Another example x := y... z := z + x

10. Another example x := y... z := z + x

11. Another example x := y... z := z + y What if we run CSE now?

12. Another example x := y... z := z + y What if we run CSE now?

13. Another example x := y**z... x :=...

14. Another example Often used as a clean-up pass x := y**z... x :=... x := y z := z + x x := y z := z + y Copy propDAE x := y z := z + y

15. Another example if (false) {... }

16. Another example if (false) {... }

17. Another example In Java: a := new int [10]; for (index = 0; index < 10; index ++) { a[index] := 100; }

18. Another example In “lowered” Java: a := new int [10]; for (index = 0; index < 10; index ++) { if (index = a.length()) { throw OutOfBoundsException; } a[index] := 0; }

19. Another example In “lowered” Java: a := new int [10]; for (index = 0; index < 10; index ++) { if (index = a.length()) { throw OutOfBoundsException; } a[index] := 0; }

20. Another example p := &x; *p := 5 y := x + 1;

21. Another example p := &x; *p := 5 y := x + 1; x := 5; *p := 3 y := x + 1; ???

22. Another example for j := 1 to N for i := 1 to N a[i] := a[i] + b[j]

23. Another example for j := 1 to N for i := 1 to N a[i] := a[i] + b[j]

24. Another example area(h,w) { return h * w } h :=...; w := 4; a := area(h,w)

25. Another example area(h,w) { return h * w } h :=...; w := 4; a := area(h,w)

26. Optimization themes Don’t compute if you don’t have to –unused assignment elimination Compute at compile-time if possible –constant folding, loop unrolling, inlining Compute it as few times as possible –CSE, PRE, PDE, loop invariant code motion Compute it as cheaply as possible –strength reduction Enable other optimizations –constant and copy prop, pointer analysis Compute it with as little code space as possible –unreachable code elimination

27. Dataflow analysis

28. Dataflow analysis: what is it? A common framework for expressing algorithms that compute information about a program Why is such a framework useful?

29. Dataflow analysis: what is it? A common framework for expressing algorithms that compute information about a program Why is such a framework useful? Provides a common language, which makes it easier to: –communicate your analysis to others –compare analyses –adapt techniques from one analysis to another –reuse implementations (eg: dataflow analysis frameworks)

30. Control Flow Graphs For now, we will use a Control Flow Graph representation of programs –each statement becomes a node –edges between nodes represent control flow Later we will see other program representations –variations on the CFG (eg CFG with basic blocks) –other graph based representations

31. x :=... y :=... p :=... if (...) {... x... x :=...... y... } else {... x... x :=... *p :=... }... x...... y... y :=... p :=...... x... x :=...... y...... x... x :=... *p :=...... x... y :=... if (...) Example CFG

32. An example DFA: reaching definitions For each use of a variable, determine what assignments could have set the value being read from the variable Information useful for: –performing constant and copy prop –detecting references to undefined variables –presenting “def/use chains” to the programmer –building other representations, like the DFG Let’s try this out on an example

33. 1: x :=... 2: y :=... 3: y :=... 4: p :=...... x... 5: x :=...... y...... x... 6: x :=... 7: *p :=...... x...... y... 8: y :=... x :=... y :=... p :=...... x... x :=...... y...... x... x :=... *p :=...... x... y :=... if (...) Visual sugar

34. 1: x :=... 2: y :=... 3: y :=... 4: p :=...... x... 5: x :=...... y...... x... 6: x :=... 7: *p :=...... x...... y... 8: y :=...

35. 1: x :=... 2: y :=... 3: y :=... 4: p :=...... x... 5: x :=...... y...... x... 6: x :=... 7: *p :=...... x...... y... 8: y :=...

36. Safety Recall intended use of this info: –performing constant and copy prop –detecting references to undefined variables –presenting “def/use chains” to the programmer –building other representations, like the DFG Safety: –can have more bindings than the “true” answer, but can’t miss any

37. Reaching definitions generalized DFA framework is geared towards computing information at each program point (edge) in the CFG –So generalize the reaching definitions problem by stating what should be computed at each program point For each program point in the CFG, compute the set of definitions (statements) that may reach that point Notion of safety remains the same

38. Reaching definitions generalized Computed information at a program point is a set of var ! stmt bindings –eg: { x ! s 1, x ! s 2, y ! s 3 } How do we get the previous info we wanted? –if a var x is used in a stmt whose incoming info is in, then: { s | (x ! s) 2 in } This is a common pattern –generalize the problem to define what information should be computed at each program point –use the computed information at the program points to get the original info we wanted

39. 1: x :=... 2: y :=... 3: y :=... 4: p :=...... x... 5: x :=...... y...... x... 6: x :=... 7: *p :=...... x...... y... 8: y :=...

40. 1: x :=... 2: y :=... 3: y :=... 4: p :=...... x... 5: x :=...... y...... x... 6: x :=... 7: *p :=...... x...... y... 8: y :=...

41. Using constraints to formalize DFA Now that we’ve gone through some examples, let’s try to precisely express the algorithms for computing dataflow information We’ll model DFA as solving a system of constraints Each node in the CFG will impose constraints relating information at predecessor and successor points Solution to constraints is result of analysis

42. Constraints for reaching definitions s: x :=... in out s: *p :=... in out

43. Constraints for reaching definitions Using may-point-to information: out = in [ { x ! s | x 2 may-point-to(p) } Using must-point-to aswell: out = in – { x ! s’ | x 2 must-point-to(p) Æ s’ 2 stmts } [ { x ! s | x 2 may-point-to(p) } s: x :=... in out s: *p :=... in out out = in – { x ! s’ | s’ 2 stmts } [ { x ! s }

44. Constraints for reaching definitions s: if (...) in out[0]out[1] merge out in[0]in[1]

45. Constraints for reaching definitions s: if (...) in out[0]out[1] more generally: 8 i. out [ i ] = in out [ 0 ] = in Æ out [ 0 ] = in merge out in[0]in[1] more generally: out =  i in [ i ] out = in [ 0 ] [ in [ 1 ]

46. Flow functions The constraint for a statement kind s often have the form: out = F s (in) F s is called a flow function –other names for it: dataflow function, transfer function Given information in before statement s, F s (in) returns information after statement s Other formulations have the statement s as an explicit parameter to F: given a statement s and some information in, F(s,in) returns the outgoing information after statement s

47. Flow functions Issue: what does one do when there are multiple input edges to a node? –the flow functions takes as input a tuple of values, one value for each incoming edge Issue: what does one do when there are multiple outgoing edges to a node? –the flow function returns a tuple of values, one value for each outgoing edge –can also have one flow function per outgoing edge

48. Flow functions Flow functions are a central component of a dataflow analysis They state constraints on the information flowing into and out of a statement This version of the flow functions is local –it applies to a particular statement kind –we’ll see global flow functions shortly...