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Titlebook: Automatic Parallelization; New Approaches to Co Christoph W. Ke?ler Book 1994 Springer Fachmedien Wiesbaden 1994 Fortran.Processing.Scala.a

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發(fā)表于 2025-3-21 18:46:49 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Automatic Parallelization
期刊簡稱New Approaches to Co
影響因子2023Christoph W. Ke?ler
視頻videohttp://file.papertrans.cn/167/166434/166434.mp4
圖書封面Titlebook: Automatic Parallelization; New Approaches to Co Christoph W. Ke?ler Book 1994 Springer Fachmedien Wiesbaden 1994 Fortran.Processing.Scala.a
影響因子Distributed-memory multiprocessing systems (DMS), such as Intel‘s hypercubes, the Paragon, Thinking Machine‘s CM-5, and the Meiko Computing Surface, have rapidly gained user acceptance and promise to deliver the computing power required to solve the grand challenge problems of Science and Engineering. These machines are relatively inexpensive to build, and are potentially scalable to large numbers of processors. However, they are difficult to program: the non-uniformity of the memory which makes local accesses much faster than the transfer of non-local data via message-passing operations implies that the locality of algorithms must be exploited in order to achieve acceptable performance. The management of data, with the twin goals of both spreading the computational workload and minimizing the delays caused when a processor has to wait for non-local data, becomes of paramount importance. When a code is parallelized by hand, the programmer must distribute the program‘s work and data to the processors which will execute it. One of the common approaches to do so makes use of the regularity of most numerical computations. This is the so-called Single Program Multiple Data (SPMD) or dat
Pindex Book 1994
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https://doi.org/10.1007/978-3-322-87865-6Fortran; Processing; Scala; algorithms; benchmarking; code; communication; control; management; networks; opti
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978-3-528-05401-4Springer Fachmedien Wiesbaden 1994
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Targeting Transputer Systems, Past and Future,We discuss some features of three generations of INMOS transputer: the 32 bit T800 family, the 64 bit T9000 family, for which early Silicon is now available, and the recently initiated . programme. The impact of these features on automatic high-performance compilers is discussed.
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Ancient Indian Wisdom for Modern Businesslp of these parameters we explain the mu time of the following algorithms ., . and . on the parallel machine Ncube-2. The iPSC/860 Hypercube and the vector machine VP100 are analyzed in an other paper (see [3]). Our explanations are sometimes within 0.5% and almost always within 5% of the measured r
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Global Perspectives on Insurance Todaysystem levels and comprise autoparallelizing compilers for FORTRAN, Lisp and the operating system components for Virtual Shared Memory. This article describes our approach for automatic parallelizing sequential FORTRAN programs for distributed—memory MIMD architectures. Annotations are not needed an
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