Results 241 to 250 of about 158,911 (285)
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Data Parallelism, Control Parallelism, and Related Issues
2000This Chapter focuses on the differences between control parallelism and data parallelism, which are important to understand the discussion about parallel data mining in later Chapters of this book. After an introduction to control and data parallelism, we discuss the effect of exploiting these two kinds of parallelism in three important issues, namely ...
Simon H. Lavington, Alex A. Freitas
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Communications of the ACM, 1986
Parallel computers with tens of thousands of processors are typically programmed in a data parallel style, as opposed to the control parallel style used in multiprocessing. The success of data parallel algorithms—even on problems that at first glance seem inherently serial—suggests that this style of programming has much wider applicability than was ...
W. Daniel Hillis, Guy L. Steele
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Parallel computers with tens of thousands of processors are typically programmed in a data parallel style, as opposed to the control parallel style used in multiprocessing. The success of data parallel algorithms—even on problems that at first glance seem inherently serial—suggests that this style of programming has much wider applicability than was ...
W. Daniel Hillis, Guy L. Steele
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[Proceedings 1992] The Fourth Symposium on the Frontiers of Massively Parallel Computation, 1992
The authors present Data Parallel Fortran (DPF), a set of extensions to Fortran aimed at programming scientific applications on a variety of parallel machines. DPF portrays a global name space to programmers and allows programs to be written in a clear, data-parallel style.
P.M. Elustondo +6 more
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The authors present Data Parallel Fortran (DPF), a set of extensions to Fortran aimed at programming scientific applications on a variety of parallel machines. DPF portrays a global name space to programmers and allows programs to be written in a clear, data-parallel style.
P.M. Elustondo +6 more
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1994
This article presents a data-parallel language, which has been designed around the concepts of relations and reduction operations. Many parallel machines provide hardware support for reduction operations (such as summing all elements of an array), and these operations are widely used in parallel scientific computing.
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This article presents a data-parallel language, which has been designed around the concepts of relations and reduction operations. Many parallel machines provide hardware support for reduction operations (such as summing all elements of an array), and these operations are widely used in parallel scientific computing.
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On Parallelizing the MRRR Algorithm for Data-Parallel Coprocessors
2010The eigenvalues and eigenvectors of a symmetric matrix are of interest in a myriad of applications. One of the fastest and most accurate numerical techniques for the eigendecomposition is the Algorithm of Multiple Relatively Robust Representations (MRRR), the first stable algorithm that computes the eigenvalues and eigenvectors of a tridiagonal ...
Paolo Bientinesi, Christian Lessig
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Data parallelism in logic programming [PDF]
, 1991 Many researchers have been trying to use the implicit parallelism of logic languages parallelizing the execution of independent clauses. However this approach has the disadvantage of requiring a heavy overhead for processes scheduling and synchronizing, for data migration and for collecting the results. In this paper it is proposed a different approach,
Succi G, Marino G
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Data communication in parallel architectures
Parallel Computing, 1989Timing estimates for various forms of data exchange in a variety of parallel architectures are investigated. Data exchange methods emphasized are: one to one, one to all, all to all, scatter, and multiscatter. Parallel architectures, emphasized are: bus, shared memory, ring, grid, hypercube and switch. Tables summarizing the timing estimates are given.
Martin H. Schultz, Youcef Saad
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2013
In the following, we discuss how to achieve parallelism in in-memory and traditional database management systems. Pipelined parallelism and data parallelism are two approaches to speed up query processing.
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In the following, we discuss how to achieve parallelism in in-memory and traditional database management systems. Pipelined parallelism and data parallelism are two approaches to speed up query processing.
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2002
Data mining refers to a process on nontrivial extraction of implicit, previously unknown and potential useful information (such as knowledge rules, constraints, regularities) from data in databases. With the availability of inexpensive storage and the progress in data capture technology, many organizations have created ultra-large databases of business
David Taniar, J. Wenny Rahayu
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Data mining refers to a process on nontrivial extraction of implicit, previously unknown and potential useful information (such as knowledge rules, constraints, regularities) from data in databases. With the availability of inexpensive storage and the progress in data capture technology, many organizations have created ultra-large databases of business
David Taniar, J. Wenny Rahayu
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1993
Is the owner-computes style of parallelism, captured in a variety of data parallel languages, attractive as a paradigm for designing explicitly parallel codes? This question gives rise to a number of others. Will such use be unwieldy? Will the resulting code run well?
Nicholas Carriero, David Gelernter
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Is the owner-computes style of parallelism, captured in a variety of data parallel languages, attractive as a paradigm for designing explicitly parallel codes? This question gives rise to a number of others. Will such use be unwieldy? Will the resulting code run well?
Nicholas Carriero, David Gelernter
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