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Titlebook: Memory Storage Patterns in Parallel Processing; Mary E. Mace Book 1987 Kluwer Academic Publishers 1987 algorithms.complexity.computer.grap

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書目名稱Memory Storage Patterns in Parallel Processing
編輯Mary E. Mace
視頻videohttp://file.papertrans.cn/631/630482/630482.mp4
叢書名稱The Springer International Series in Engineering and Computer Science
圖書封面Titlebook: Memory Storage Patterns in Parallel Processing;  Mary E. Mace Book 1987 Kluwer Academic Publishers 1987 algorithms.complexity.computer.grap
描述This project had its beginnings in the Fall of 1980. At that time Robert Wagner suggested that I investigate compiler optimi- zation of data organization, suitable for use in a parallel or vector machine environment. We developed a scheme in which the compiler, having knowledge of the machine‘s access patterns, does a global analysis of a program‘s operations, and automatically determines optimum organization for the data. For example, for certain architectures and certain operations, large improvements in performance can be attained by storing a matrix in row major order. However a subsequent operation may require the matrix in column major order. A determination must be made whether or not it is the best solution globally to store the matrix in row order, column order, or even have two copies of it, each organized differently. We have developed two algorithms for making this determination. The technique shows promise in a vector machine environ- ment, particularly if memory interleaving is used. Supercomputers such as the Cray, the CDC Cyber 205, the IBM 3090, as well as superminis such as the Convex are possible environments for implementation.
出版日期Book 1987
關(guān)鍵詞algorithms; complexity; computer; graph algorithm; knowledge; memory; supercomputer
版次1
doihttps://doi.org/10.1007/978-1-4613-2001-2
isbn_softcover978-1-4612-9194-7
isbn_ebook978-1-4613-2001-2Series ISSN 0893-3405
issn_series 0893-3405
copyrightKluwer Academic Publishers 1987
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Solution for Graphs without Shared Nodes,A tree is a directed acyclic graph with no shared nodes. In a tree every node has exactly one immediate ancestor. If the graph representing a program is a tree, then each result is used in exactly one operation after it is computed. Below is a diagram of a tree (left) and a graph which is not a tree (right).
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Solution for Graphs with Shared Nodes,epends on the graph having a certain structure which we call collapsible. A collapsible graph is a directed, acyclic graph which is series-parallel and includes treelike subgraphs. . A more precise definition of the collapsible graphs, which may be found in Appendix A, is not necessary to understand the collapsible graph algorithm.
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Book 1987ion. The technique shows promise in a vector machine environ- ment, particularly if memory interleaving is used. Supercomputers such as the Cray, the CDC Cyber 205, the IBM 3090, as well as superminis such as the Convex are possible environments for implementation.
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