Results 171 to 180 of about 1,316 (216)
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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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Data-Parallel Sparse Factorization
SIAM Journal on Scientific Computing, 1998The data-parallel implementation of the multifrontal algorithm for the LU factorization, without pivoting, of matrices having symmetric structure and nonsymmetric coefficients is considered. A simple yet efficient and scalable implementation of the multifrontal sparse LU factorization is presented.
John M. Conroy +3 more
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Journal of Parallel and Distributed Computing, 1994
Abstract In data-parallel programming, operations are performed simultaneously on all elements of large data structures. Backus′s FP functional language promotes this view. FP provides a large set of data rearrangement primitives, and a useful set of functional combining forms that are applied to entire data structures.
Clifford Walinsky, Deb Banerjee
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Abstract In data-parallel programming, operations are performed simultaneously on all elements of large data structures. Backus′s FP functional language promotes this view. FP provides a large set of data rearrangement primitives, and a useful set of functional combining forms that are applied to entire data structures.
Clifford Walinsky, Deb Banerjee
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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 ...
Christian Lessig, Paolo Bientinesi
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Polarized data parallel data flow
Proceedings of the 5th International Workshop on Functional High-Performance Computing, 2016We present an approach to writing fused data parallel data flow programs where the library API guarantees that the client programs run in constant space. Our constant space guarantee is achieved by observing that binary stream operators can be provided in several polarity versions.
Ben Lippmeier, Fil Mackay, Amos Robinson
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Parallel DEPSO-Scout: Data Parallelism
2018 International Electrical Engineering Congress (iEECON), 2018DEPSO-Scout is a hybrid optimization algorithm combining Differential Evolution (DE), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC). The solution convergence is balanced between exploration of PSO and exploitation from DE. The suboptimal solution has reduced by the scout bee property of ABC.
Prasitchai Boonserm +1 more
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Parallel functional programming on recursively defined data via data-parallel recursion
Journal of Functional Programming, 1999This article proposes a new language mechanism for data-parallel processing of dynamically allocated recursively defined data. Different from the conventional array-based data- parallelism, it allows parallel processing of general recursively defined data such as lists or trees in a functional way.
Susumu Nishimura, Atsushi Ohori
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International Journal of Data Science and Analytics, 2019
The major strength of hierarchical clustering algorithms is that it allows visual interpretations of clusters through dendrograms. Users can cut the dendrogram at different levels to get desired number of clusters. A major problem with hierarchical algorithms is their quadratic runtime complexity, which limits the amount of data that can be clustered ...
Poonam Goyal +4 more
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The major strength of hierarchical clustering algorithms is that it allows visual interpretations of clusters through dendrograms. Users can cut the dendrogram at different levels to get desired number of clusters. A major problem with hierarchical algorithms is their quadratic runtime complexity, which limits the amount of data that can be clustered ...
Poonam Goyal +4 more
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Exploiting potential of deep neural networks by layer-wise fine-grained parallelism
Future Generation Computer Systems, 2020Hai Jin, Laurence T Yang
exaly