Dependence flow graphs: an algebraic approach to program dependencies
POPL '91 Proceedings of the 18th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Value dependence graphs: representation without taxation
POPL '94 Proceedings of the 21st ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Combining analyses, combining optimizations
ACM Transactions on Programming Languages and Systems (TOPLAS)
A simple graph-based intermediate representation
IR '95 Papers from the 1995 ACM SIGPLAN workshop on Intermediate representations
Optimizing sparse representations for dataflow analysis
IR '95 Papers from the 1995 ACM SIGPLAN workshop on Intermediate representations
Sparse functional stores for imperative programs
IR '95 Papers from the 1995 ACM SIGPLAN workshop on Intermediate representations
CC'10/ETAPS'10 Proceedings of the 19th joint European conference on Theory and Practice of Software, international conference on Compiler Construction
Intermediate representations in imperative compilers: A survey
ACM Computing Surveys (CSUR)
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The topic of intermediate languages for optimizing and parallelizing compilers has received much attention lately. In this paper, we argue that any good representation must have two crucial properties: first, the representation of a program must be a data structure that can be rapidly traversed to determine dependence information; second, the representation must be a program in its own right, with a parallel, local, model of execution. In this paper, we illustrate the importance of these points by examining algorithms for standard optimization-global constant propagation. We discuss the problems in working with current representations. Then, we propose a novel representation called the dependence flow graph which has each of the properties mentioned above. In this representation, dependencies are part of the computational mode, in that there is an algebra of operators over dependencies. We show that this representation leads to a simple algorithm, based on abstract interpretation, for solving the constant propagation problem. Our algorithm is simpler than, and as fast as, the best known algorithms for the problem. An interesting feature of our representation is that it naturally incorporates the best aspects of many other representations, including continuation-passing style, data and program dependence graphs, static single assignment form and dataflow program graphs.