Lecture 5. Data Flow Analysis
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1 Lecture 5. Data Flow Analysis Wei Le
2 Abstraction-based Analysis dataflow analysis: combines model checking s fix point engine with abstract interpretation of data values abstract interpretation: interpretation of program executions over an abstract domain; the semantics of program statements is lifted to operate on a set of states, an element of an abstract domain, rather than a single concrete statements note: credit to Matt Dwyer
3 Dataflow Analysis What is dataflow analysis? The flow of data values The global properties of data at a program point
4 Reaching Definitions
5 Available Expressions
6 Live Variable Analysis
7 Generalizing Dataflow Analysis An instance of dataflow problem: What is the problem? The properties of dataflow problems: backward or forward (determine the direction of the dataflow) may or must (determine how to merge dataflow) Dataflow equation: Gen, Kill, In, Out (local information)
8 Generalizing Dataflow Analysis Goal: solving dataflow equations - determine dataflow for the whole program Reaching definitions: for each program point, what are the definitions can reach Available expressions: for each program point, what are the expressions available Live variables: for each program point, what are the variables available Framework: a set of data propagation algorithms for a set of dataflow problems Key of the algorithm: Stabilize in presence of loops fixpoint, using worklist algorithm Data structure for efficiency: bit vector to represent the global information Reaching definitions: each definition is a bit, each program point has a bitvector Available expressions: each expression is a bit, each program point has a bitvector Live variable analysis: each variable is a bit, each program point has a bitvector
9 Dataflow Algorithms Exhaustive (Dragon Book) Demand-driven [1] for forward problem, considering all possible paths from the entry to the given point, compute flow value for all the paths, and then meet the flow value at the end of the points
10 Conservative Analysis
11 Formal Model to Describe Dataflow Analysis [3, 2] Lattice: define domain of program properties computed by dataflow analysis (flow value) (the set of elements plus the order of these elements) Define flow functions (transfer functions: how each node affects the flow value) and merge functions over this domain using standard lattice operations
12 Lattice
13 Lattice
14 From Lattice to Dataflow Problems
15 From Lattice to Dataflow Problems
16 Transfer Functions
17 Transfer Functions Give a flow function, more conservative input leads to more conservative output IDFA is guaranteed to terminate if the flow function is monotonic and the lattice has a finite height
18 Flow Functions
19 WorkList Algorithm
20 Correctness
21 Termination
22 Evelyn Duesterwald, Rajiv Gupta, and Mary Lou Soffa. A practical framework for demand-driven interprocedural data flow analysis. ACM Transactions on Programming Languages and Systems, 19: , John B. Kam and Jeffrey D. Ullman. Global data flow analysis and iterative algorithms. J. ACM, 23(1): , January Gary A. Kildall. A unified approach to global program optimization. In Proceedings of the 1st Annual ACM SIGACT-SIGPLAN Symposium on Principles of Programming Languages, POPL 73, pages , New York, NY, USA, ACM.
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