S.P. Masticola, T.J. Marlowe, and B.G. Ryder. Lattice frameworks for multisource and bidirectional data flow problems. ACM Transactions on Programming Languages and Systems, 17(5):777--803, September 1995.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....of the resulting MHP information. By its nature, MHP information is symmetric. Achieving this takes an additional step in our algorithm. Unfortunately, this step makes it impossible to represent the MHP algorithm as a purely forward or backward flow data flow problem [8] or as a bidirectional [13] data flow problem. It is for this reason that we present the algorithm using data flow equations described in Section 4.3. First we present a simple version of the algorithm that is easy to understand and for which it is easy to prove termination and conservativeness. After that we present an ....

S. P. Masticola, T. J. Marlowe, and B. G. Ryder. Lattice frameworks for multisource and bidirectional data flow problems. ACM Transactions of Programming Languages and Systems, 17(5):777--803, Sept. 1995.

....right dependence region. 9. RELATED WORK A number of analysis frameworks have been developed for making intra and interprocedural analyses easier to write and reason about, including Sharlit [26] SPARE [27] FIAT [17] McCAT [19] System Z [33] PAG [2] the k tuple dataflow analysis framework [22], and Dwyer and Clarke s system [15] However, none of these systems address integrating transformations with analyses, nor automatically combining analyses profitably. Nelson and Oppen [23] describe how under certain conditions satisfiability programs for several theories can be combined into a ....

Stephen P. Masticola, Thomas J. Marlowe, and Barbara G. Ryder. Lattice frameworks for multisource and bidirectional data flow problems. ACM Transactions on Programming Languages and Systems, 17(5):777--803, September 1995.

....information which is uniformly applicable to unidirectional and bidirectional data flows. Thus, flows arising due to bidirectional data flow equations can be understood much the same way as the flows arising due to unidirectional data flow equations. The theory of multi source information flows [43] is a possible alternative for explaining bidirectional flows; they have formulated PRE as an example. However, they visualise information as a tuple in which each element is essentially independent until analysis is over. The values of these elements are combined to yield the final value at the ....

S. P. Masticola, T. J. Marlowe, and B. G. Ryder. Lattice frameworks for multi-source and bidirectional data flow problems. ACM Transactions on Programming Languages and Systems, 17(5):777--803, 1995.

....work builds off and is related to research in program flow analysis and static concurrency analysis. There is a large body of research on flow analysis for sequential programs, e.g. MR90] Recently these techniques have begun to be adapted to explicitly concurrent programs, e.g. GS93, CKS90, MMR95, MR93] The modular structure of sequential programs has been the focus of much work on inter procedural analysis. Modularity in concurrent programs is fundamentally different, with components of programs executing in parallel rather than in sequence, and to our knowledge has not been exploited ....

S.P. Masticola, T.J. Marlowe, and B.G. Ryder. Lattice frameworks for multisource and bidirectional data flow problems. ACM Transactions on Programming Languages and Systems, 17(5):777--803, September 1995.

.... the lattice function space view of data flow problems [7] since it makes explanations of this algorithm more intuitive and, as will be evident, one aspect of this algorithm precludes its representation as a purely forward or backward data flow problem or even as a bidirectional or multisource [16] data flow problem. At the end of this section we present a pseudo code version of the worklist version of the MHP algorithm. 3.1 High Level Overview Initially we assume that each node in the PEG may not happen in parallel with any other nodes. The data flow algorithm then uses the PEG to ....

S. P. Masticola, T. J. Marlowe, and B. G. Ryder. Lattice frameworks for multisource and bidirectional data flow problems. ACM Transactions on Programming Languages and Systems, 17(5):777--803, September 1995.

....the opportunity for further improving the accuracy of flow analysis of concurrent programs that engage in synchronous communication. These variants exploit complete lattice frameworks formulations of the analysis [Dwy95] which are equivalent to a node based formulation of meet of join frameworks [MMR95] The node ordering enforced by this algorithm causes computation of the value at a node only when it has the potential to change. If multiple predecessors of a node change their value before that node can be recomputed, the algorithm will only schedule a single recomputation for the node. ....

S.P. Masticola, T.J. Marlowe, and B.G. Ryder. Lattice frameworks for multisource and bidirectional data flow problems. ACM Transactions on Programming Languages and Systems, 17(5):777--803, September 1995.

....is not possible in Figure 1(a) since hoisting a b to node 2 introduces a new value on the rightmost path. 2 Sometimes critical edges are not split only in situations that may harm the final code generation. 1. 1 Related Work As Khedker and Dhamdhere [14] and more recently Masticola et al. [17] noticed, critical edges do not add to the worst case time complexity of iterative data flow analyses being based on a workset approach. However, this result cannot be generalized to bit vector analyses where the iteration order has to be organized in a way such that structural properties of the ....

P. M. Masticola, T. J. Marlowe, and B. G. Ryder. Lattice frameworks for multisource and bidirectional data flow problems. ACM Transactions on Programming Languages and Systems, 17(5):777 -- 802, 1995.

....in Section 5. In the final section, we conclude and discuss some directions for future work. 2. Related Work There is a large body of research on flow analysis for sequential programs, e.g. 14] Recently these techniques have begun to be adapted to explicitly concurrent programs, e.g. [12, 15, 17]. The modular structure of sequential programs has been the focus of much work on inter procedural analysis. Conceptually, we can think of a modular analysis of concurrent software in much the same way as interprocedural analysis. Both include the notion of a summary of the behavior of the ....

S. Masticola, T. Marlowe, and B. Ryder. Lattice frameworks for multisource and bidirectional data flow problems. ACM Transactions on Programming Languages and Systems, 17(5):777--803, Sept. 1995.

....from n final . We say that a property holds on all executions of the program if all tuples remaining in n final contain only accepting states of the property automaton. This approach of representing information propagated around the flow graph as tuples is reminiscent of K Tuple frameworks from [9]. An important distinction is that each component of a tuple in K Tuple frameworks corresponds to a special edge kind in a graph. In our approach, an event associated with a TFG node can be present in alphabets of several constraint and property automata and thus components of tuples are not ....

S. P. Masticola, T. J. Marlowe, and B. G. Ryder. Lattice frameworks for multisource and bidirectional data flow problems. ACM Transactions on Programming Languages and Systems, 17(5):777-- 803, September 1995.

....for TFG nodes. This is done for two reasons. First, it makes explanations and especially proving properties of this algorithm more intuitive. Second, one aspect of the algorithm precludes its representation as a purely forward or backward flow data flow problem or even as a bidirectional [10] data flow problem. We conclude the description of the algorithm by giving pseudo code for its worklist version. Our algorithm associates three sets with each node n of the TFG: GEN (n) IN (n) and M(n) The set M(n) is the current approximation to the set of nodes that may happen in parallel ....

S. P. Masticola, T. J. Marlowe, and B. G. Ryder. Lattice frameworks for multisource and bidirectional data flow problems. ACM Transactions on Programming Languages and Systems, 17(5):777-- 803, September 1995.

....for TFG nodes. This is done for two reasons. First, it makes explanations and especially proving properties of this algorithm more intuitive. Second, one aspect of the algorithm precludes its representation as a purely forward or backward flow data flow problem or even as a bidirectional [10] data flow problem. We conclude the description of the algorithm by giving pseudo code for its worklist version. Our algorithm associates three sets with each node n of the TFG: GEN (n) IN (n) and M(n) The set M(n) is the current approximation to the set of nodes that may happen in parallel ....

S. P. Masticola, T. J. Marlowe, and B. G. Ryder. Lattice frameworks for multisource and bidirectional data flow problems. ACM Transactions on Programming Languages and Systems, 17(5):777-- 803, September 1995.

....edge, or of its two incident vertices, can reasonably be considered to be flow sensitive. We now view this edge classification as an orthogonal dimension to alg flow sensitivity. Recent work discusses how to transform these multisource data flow problems into a k tuple framework formulation [MMR95] This model is especially useful for data flow analysis of explicitly parallel programs, where there may be control, synchronization, and call and return edges [MR93, GS93, CH92] Bidirectionality Bidirectional data flow problems were introduced by Morel and Renvoise for Partial Redundancy ....

....how to solve Partial Redundancy as a sequence of unidirectional problems. Since bidirectional problems typically involve non trivial equations at both node entry and exit, and often different meets, we can view these as a subclass of the heterogeneous problems mentioned above. Discussions in [MMR95] also show how bidirectional problems can be transformed into k tuple frameworks. Convergence Properties and Flow Sensitivity Because of the tight restrictions it places on the equation sets of problems, flow insensitivity for [AC] implies 1 semiboundedness, and in fact distributivity and ....

Stephen P. Masticola, Thomas J. Marlowe, and Barbara G. Ryder. Lattice frameworks for multi-source and bidirectional data flow analysis problems. Technical Report LCSR-TR-241, Department of Computer Science, Rutgers University, April 1995.

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S.P. Masticola, T.J. Marlowe, and B.G. Ryder. Lattice frameworks for multisource and bidirectional data flow problems. ACM Transactions on Programming Languages and Systems, 17(5):777--803, September 1995.

No context found.

S.P. Masticola, T.J. Marlowe, and B.G. Ryder. Lattice frameworks for multisource and bidirectional data flow problems. ACM Transactions on Programming Languages and Systems, 17(5):777--803, September 1995.

No context found.

S. P. Masticola, T. J. Marlowe, and B. G. Ryder. Lattice frameworks for multisource and bidirectional data flow problems. ACM Transactions of Programming Languages and Systems, 17(5):777--803, Sept. 1995.

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Stephen Masticola, Thomas J. Marlowe, and Barbara G. Ryder. Lattice Frameworks for Multisource and Bidirectional Data Flow Problems. ACM Transactions on Programming Languages and Systems, 17(5):777--803, September 1995.

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S. P. Masticola, T. J. Marlowe, and B. G. Ryder. Lattice frameworks for multisource and bidirectional data flow problems. ACM Transactions on Programming Languages and Systems, 17(5):777--803, September 1995.

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