lecture-11-6up

lecture-11-6up - Program optimizations Dataow Analysis So...

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Datafow Analysis Wednesday, November 9, 2011 Program optimizations So Far we have talked about diFFerent kinds oF optimizations Peephole optimizations Local common sub-expression elimination Loop optimizations What about global optimizations Optimizations across multiple basic blocks (usually a whole procedure) Not just a single loop Wednesday, November 9, 2011 UseFul optimizations Common subexpression elimination (global) Need to know which expressions are available at a point Dead code elimination Need to know iF the eFFects oF a piece oF code are never needed, or iF code cannot be reached Constant Folding Need to know iF variable has a constant value Loop invariant code motion Need to know where and when variables are live So how do we get this inFormation? Wednesday, November 9, 2011 Datafow analysis ±ramework For doing compiler analyses to drive optimization Works across basic blocks Examples Constant propagation: determine which variables are constant Liveness analysis: determine which variables are live Available expressions: determine which expressions are have valid computed values Reaching de²nitions: determine which de²nitions could “reach” a use Wednesday, November 9, 2011 Example: constant propagation Goal: determine when variables take on constant values Why? Can enable many optimizations Constant Folding Create dead code x = 1; y = x + 2; if (x > z) then y = 5 ... y . .. x = 1; y = x + 2; if (y > x) then y = 5 ... y . .. Wednesday, November 9, 2011 Example: constant propagation Goal: determine when variables take on constant values Why? Can enable many optimizations Constant Folding Create dead code x = 1; y = x + 2; if (x > z) then y = 5 ... y . .. x = 1; y = 3; if (x > z) then y = 5 ... y . .. x = 1; y = x + 2; if (y > x) then y = 5 ... y . .. Wednesday, November 9, 2011
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Example: constant propagation Goal: determine when variables take on constant values Why? Can enable many optimizations Constant folding Create dead code x = 1; y = x + 2; if (x > z) then y = 5 ... y . .. x = 1; y = 3; if (x > z) then y = 5 ... y . .. x = 1; y = x + 2; if (y > x) then y = 5 ... y . .. x = 1; y = 3; //dead code if (true) then y = 5 //simplify! ... y . .. Wednesday, November 9, 2011 How can we Fnd constants? Ideal: run program and see which variables are constant Problem: variables can be constant with some inputs, not others – need an approach that works for all inputs! Problem: program can run forever (inFnite loops?) – need an approach that we know will Fnish Idea: run program symbolically Essentially, keep track of whether a variable is constant or not constant (but nothing else) Wednesday, November 9, 2011 Overview of algorithm Build control ±ow graph We’ll use statement-level C²G (with merge nodes) for this Perform symbolic evaluation Keep track of whether variables are constant or not Replace constant-valued variable uses with their values, try to simplify expressions and control ±ow Wednesday, November 9, 2011 Build C²G x = 1; y = x + 2; if (y > x) then y = 5; ... y . ..
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lecture-11-6up - Program optimizations Dataow Analysis So...

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