Automatic Parallelization: New Approaches to Code Generation, Data Distribution, and Performance Prediction (Vieweg Advanced Studies in Computer Science) - Softcover
Synopsis
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 data parallel model of computation. With this method, the data arrays in the original program are each distributed to the processors, establishing an ownership relation, and computations defining a data item are performed by the processors owning the data.
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Synopsis
This work contains 11 contributions which deal with true automatic parallelization, and the focus is on automatic methods. Some of the questions under discussion are: up to which degree is automatic parallelization for DMS possible today? What are currently the most important problems for automatic parallelization and which new ideas are there? In which cases can knowledge-based methods help? Are there promising methods for automatic data distribution and redistribution? Why is performance prediction problematic?
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Automatic Parallelization
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Vieweg+Teubner, Vieweg+Teubner Verlag Jan 1994, 1994
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 data parallel model of computation. With this method, the data arrays in the original program are each distributed to the processors, establishing an ownership relation, and computations defining a data item are performed by the processors owning the data. 224 pp. Englisch. Seller Inventory # 9783528054014
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Automatic Parallelization: New Approaches to Code Generation, Data Distribution, and Performance Prediction (Vieweg Advanced Studies of Computer Science)
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Automatic Parallelization
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Condition: New. Contains 11 contributions which deal with true automatic parallelization, and the focus is on automatic methods. Some of the questions under discussion are: up to which degree is automatic parallelization for DMS possible today? In which cases can knowledge-based methods help? Editor(s): Kessler, Christoph W. Series: Vieweg Advanced Studies in Computer Science. Num Pages: 224 pages, biography. BIC Classification: UYFP. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 210 x 148 x 12. Weight in Grams: 318. . 1994. Paperback. . . . . Seller Inventory # V9783528054014
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 c. Seller Inventory # 4866723
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Automatic Parallelization
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Automatic Parallelization | New Approaches to Code Generation, Data Distribution, and Performance Prediction
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Taschenbuch. Condition: Neu. Automatic Parallelization | New Approaches to Code Generation, Data Distribution, and Performance Prediction | Christoph W. Kessler | Taschenbuch | xi | Englisch | 1994 | Vieweg & Teubner | EAN 9783528054014 | Verantwortliche Person für die EU: Springer Vieweg in Springer Science + Business Media, Abraham-Lincoln-Str. 46, 65189 Wiesbaden, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Seller Inventory # 102072671
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Automatic Parallelization
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ISBN 13: 9783528054014
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 data parallel model of computation. With this method, the data arrays in the original program are each distributed to the processors, establishing an ownership relation, and computations defining a data item are performed by the processors owning the data.Springer Vieweg in Springer Science + Business Media, Abraham-Lincoln-Straße 46, 65189 Wiesbaden 240 pp. Englisch. Seller Inventory # 9783528054014
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Automatic Parallelization : New Approaches to Code Generation, Data Distribution, and Performance Prediction
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ISBN 10: 3528054018
ISBN 13: 9783528054014
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 data parallel model of computation. With this method, the data arrays in the original program are each distributed to the processors, establishing an ownership relation, and computations defining a data item are performed by the processors owning the data. Seller Inventory # 9783528054014