Global Data Flow Analysis and Iterative Algorithms
Journal of the ACM (JACM)
A Fast and Usually Linear Algorithm for Global Flow Analysis
Journal of the ACM (JACM)
Global optimization by suppression of partial redundancies
Communications of the ACM
A program data flow analysis procedure
Communications of the ACM
Monoids for rapid data flow analysis
POPL '78 Proceedings of the 5th ACM SIGACT-SIGPLAN symposium on Principles of programming languages
A unified approach to global program optimization
POPL '73 Proceedings of the 1st annual ACM SIGACT-SIGPLAN symposium on Principles of programming languages
Applications of a graph grammar for program control flow analysis
POPL '77 Proceedings of the 4th ACM SIGACT-SIGPLAN symposium on Principles of programming languages
Calculating properties of programs by valuations on specific models
Proceedings of ACM conference on Proving assertions about programs
Solving path problems on directed graphs.
Solving path problems on directed graphs.
Principles of Compiler Design (Addison-Wesley series in computer science and information processing)
Principles of Compiler Design (Addison-Wesley series in computer science and information processing)
Predicated array data-flow analysis for run-time parallelization
ICS '98 Proceedings of the 12th international conference on Supercomputing
Evaluation of predicated array data-flow analysis for automatic parallelization
Proceedings of the seventh ACM SIGPLAN symposium on Principles and practice of parallel programming
A Case for Combining Compile-Time and Run-Time Parallelization
LCR '98 Selected Papers from the 4th International Workshop on Languages, Compilers, and Run-Time Systems for Scalable Computers
Combining compile-time and run-time parallelization[1]
Scientific Programming
The trace partitioning abstract domain
ACM Transactions on Programming Languages and Systems (TOPLAS) - Special Issue ESOP'05
Probabilistic dataflow analysis using path profiles on structure graphs
Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering
Path-Sensitive dataflow analysis with iterative refinement
SAS'06 Proceedings of the 13th international conference on Static Analysis
Trace partitioning in abstract interpretation based static analyzers
ESOP'05 Proceedings of the 14th European conference on Programming Languages and Systems
Satisfiability solvers are static analysers
SAS'12 Proceedings of the 19th international conference on Static Analysis
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It is known that not all paths are possible in the run time control flow of many programs. It is also known that data flow analysis cannot restrict attention to exactly those paths that are possible. It is therefore usual for analytic methods to consider all paths. Sharper information can be obtained by considering a recursive set of paths that is large enough to include all possible paths but small enough to exclude many of the impossible ones. This paper presents a simple uniform methodology for sharpening data flow information by considering certain recursive path sets of practical importance. Associated with each control flow arc there is a relation on a finite set Q. The paths that qualify to be considered are (essentially) those for which the composition of the relations encountered is nonempty. For example, Q might be the set of all assignments of values to each of several bit variables used by a program to remember some facts about the past and branch accordingly in the future. Given any data flow problem together with qualifying relations on Q associated with the control flow arcs, we construct a new problem. Considering all paths in the new problem is equivalent to considering only qualifying paths in the old one. Preliminary experiments (with a small set of real programs) indicate that qualified analysis is feasible and substantially more informative than ordinary analysis. The methodology also has a beneficial feedback effect on the delicate task of passing from programs to meaningful data flow analysis problems. Even when all paths qualify, unusually sharp information can be obtained by passing from programs to problems in ways suggested by our theorems.