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Titlebook: Memory Performance of Prolog Architectures; Evan Tick Book 1988 Kluwer Academic Publishers 1988 Broadcast.Fortran.Logic.Prolog.algorithms.

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發(fā)表于 2025-3-21 17:57:03 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Memory Performance of Prolog Architectures
編輯Evan Tick
視頻videohttp://file.papertrans.cn/631/630480/630480.mp4
叢書(shū)名稱The Springer International Series in Engineering and Computer Science
圖書(shū)封面Titlebook: Memory Performance of Prolog Architectures; Evan Tick Book 1988 Kluwer Academic Publishers 1988 Broadcast.Fortran.Logic.Prolog.algorithms.
描述One suspects that the people who use computers for their livelihood are growing more "sophisticated" as the field of computer science evolves. This view might be defended by the expanding use of languages such as C and Lisp in contrast to the languages such as FORTRAN and COBOL. This hypothesis is false however - computer languages are not like natural languages where successive generations stick with the language of their ancestors. Computer programmers do not grow more sophisticated - programmers simply take the time to muddle through the increasingly complex language semantics in an attempt to write useful programs. Of course, these programmers are "sophisticated" in the same sense as are hackers of MockLisp, PostScript, and Tex - highly specialized and tedious languages. It is quite frustrating how this myth of sophistication is propagated by some industries, universities, and government agencies. When I was an undergraduate at MIT, I distinctly remember the convoluted questions on exams concerning dynamic scoping in Lisp - the emphasis was placed solely on a "hacker‘s" view of computation, i. e. , the control and manipulation of storage cells. No consideration was given to the
出版日期Book 1988
關(guān)鍵詞Broadcast; Fortran; Logic; Prolog; algorithms; architecture; compiler; computer; design; language; natural lan
版次1
doihttps://doi.org/10.1007/978-1-4613-2017-3
isbn_softcover978-1-4612-9202-9
isbn_ebook978-1-4613-2017-3Series ISSN 0893-3405
issn_series 0893-3405
copyrightKluwer Academic Publishers 1988
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Memory Performance of Prolog Architectures978-1-4613-2017-3Series ISSN 0893-3405
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The Springer International Series in Engineering and Computer Sciencehttp://image.papertrans.cn/m/image/630480.jpg
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Introduction,plications and the languages and architectures in which they are implemented. Applications such as natural language understanding and symbolic equation solving, as compared with conventional applications such as numerical modeling and simulation, are further removed from conventional procedural/func
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Prolog Architectures,defined from the semantics of Prolog in the tradition of Flynn and Hoevel’s work on canonical architectures for procedural languages [27]. The most notable member of the Prolog architecture family is the Warren Abstract Machine (WAM) architecture [96], currently implemented on general purpose hosts
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Prolog Architecture Measurements,tures described in the previous chapter. The benchmarks measured with this experimental approach are then described. Next, high-level statistical characterizations of Prolog’s memory request behavior are presented. From these high-level statistics, problem areas and performance bottlenecks are noted
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Uniprocessor Memory Organizations,evel consists of a local memory. The second level consists of an interleaved main memory. Both traditional local memory models, as well as models suited specifically to the Prolog architectures previously introduced, are examined. Queueing models are used to determine the main memory interleaving re
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Multiprocessor Memory Organizations, 2.3). PWAM is chosen for study in this book for several reasons. It is an extension of the Warren Abstract Machine (WAM), which allows fair comparison between sequential and parallel Prolog architectures. It is designed to execute sequential code efficiently with a modified WAM storage model. High-
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