Results 301 to 310 of about 21,055,470 (353)
Some of the next articles are maybe not open access.
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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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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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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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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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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ACM SIGGRAPH 2008 classes, 2008
Users always care about performance. Although often it's just a matter of making sure the software is doing only what it should, there are many cases where it is vital to get down to the metal and leverage the fundamental characteristics of the processor.Until recently, performance improvement was not difficult.
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Users always care about performance. Although often it's just a matter of making sure the software is doing only what it should, there are many cases where it is vital to get down to the metal and leverage the fundamental characteristics of the processor.Until recently, performance improvement was not difficult.
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Data-parallel, volumerendering algorithms
The Visual Computer, 1995In this presentation, we consider the image-composition scheme for parallel volume rendering in which each processor is assigned a portion of the volume. A processor renders its data by using any existing volume-rendering algorithm. We describe one such parallel algorithm that also takes advantage of vector-processing capabilities. The resulting images
Raghu Machiraju, Roni Yagel
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Proceedings of the 2nd ACM SIGPLAN workshop on Functional high-performance computing, 2013
The implicit data parallelism in collective operations on aggregate data structures constitutes an attractive parallel programming model for functional languages. Beginning with our work on integrating nested data parallelism into Haskell, we explored a variety of different approaches to array-centric data parallel programming in Haskell, experimented ...
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The implicit data parallelism in collective operations on aggregate data structures constitutes an attractive parallel programming model for functional languages. Beginning with our work on integrating nested data parallelism into Haskell, we explored a variety of different approaches to array-centric data parallel programming in Haskell, experimented ...
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1991
There are a number of situations where data parallelization is to be preferred over geometric parallelization. An example of such a situation that we have already encountered is that of a system with long-range interactions, such as Coulombic or gravitational interactions.
Anthony N. Burkitt, Dieter W. Heermann
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There are a number of situations where data parallelization is to be preferred over geometric parallelization. An example of such a situation that we have already encountered is that of a system with long-range interactions, such as Coulombic or gravitational interactions.
Anthony N. Burkitt, Dieter W. Heermann
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