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Barry K. Rosen
Researcher at IBM
Publications - 56
Citations - 5760
Barry K. Rosen is an academic researcher from IBM. The author has contributed to research in topics: Fault (power engineering) & Graph (abstract data type). The author has an hindex of 28, co-authored 56 publications receiving 5566 citations.
Papers
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Journal ArticleDOI
Efficiently computing static single assignment form and the control dependence graph
TL;DR: In this article, the authors present new algorithms that efficiently compute static single assignment forms and control dependence graphs for arbitrary control flow graphs using the concept of {\em dominance frontiers} and give analytical and experimental evidence that these data structures are usually linear in the size of the original program.
Proceedings ArticleDOI
Global value numbers and redundant computations
TL;DR: This work proposes a redundancy elimination algorithm that is global (in that it deals with the entire program), yet able to recognize redundancy among expressions that are lexitally different, and takes advantage of second order effects.
Proceedings ArticleDOI
An efficient method of computing static single assignment form
TL;DR: This paper presents strong evidence that static single assignment form and the control dependence graph can be of practical use in optimization, and presents a new algorithm that efficiently computes these data structures for arbitrary control flow graph.
Journal ArticleDOI
Transition Fault Simulation
TL;DR: The authors present a model, called a transition fault, which when used with parallel-pattern, single-fault propagation, is an efficient way to simulate delay faults and shows that delay fault simulation can be done of random patterns in less than 10% more time than needed for a stuck fault simulation.
Patent
Data mining based underwriting profitability analysis
Chidanand Apte,Edna K. Grossman,Edwin P. D. Pednault,Barry K. Rosen,Fateh A. Tipu,Hsueh-ju Wang,Brian F. White +6 more
TL;DR: In this paper, a computer implemented method of underwriting profitability analysis delivers the analytic process to a wide cross-section of insurance decision makers, which leverages an existing investment in databases and improves underwriting business processes.