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Automatic Parallelization 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? Full description
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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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Book Description Vieweg+Teubner Verlag, 2017. Paperback. Condition: New. PRINT ON DEMAND Book; New; Publication Year 2017; Not Signed; Fast Shipping from the UK. No. book. Seller Inventory # ria9783528054014_lsuk
Book Description Vieweg+Teubner Verlag, 1994. PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Seller Inventory # IQ-9783528054014
Book Description Springer, 1994. Condition: New. Seller Inventory # I-9783528054014
Book Description Friedrich Vieweg & Sohn Verlagsgesellschaft mbH, Germany, 1994. Paperback. Condition: New. 1994 ed. Language: English. Brand new Book. 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 # AAV9783528054014
Book Description Vieweg+Teubner Verlag, 1994. Paperback. Condition: New. 1994. Ships with Tracking Number! INTERNATIONAL WORLDWIDE Shipping available. Buy with confidence, excellent customer service!. Seller Inventory # 3528054018n