Results 171 to 180 of about 27,095 (213)
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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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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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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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Data-parallel flattening by expansion
Proceedings of the 6th ACM SIGPLAN International Workshop on Libraries, Languages and Compilers for Array Programming, 2019We present a higher-order programmer-level technique for compiling particular kinds of irregular data-parallel problems to parallel hardware. The technique, which we have named ``flattening-by-expansion'' builds on a number of segmented data-parallel operations but is itself implemented as a higher-order generic function, which makes it useful for many
Martin Elsman +2 more
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Proceedings of the 2007 workshop on Declarative aspects of multicore programming, 2007
We describe the design and current status of our effort to implement the programming model of nested data parallelism into the Glasgow Haskell Compiler. We extended the original programming model and its implementation, both of which were first popularised by the NESL language, in terms of expressiveness as well as efficiency.
Manuel M. T. Chakravarty +4 more
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We describe the design and current status of our effort to implement the programming model of nested data parallelism into the Glasgow Haskell Compiler. We extended the original programming model and its implementation, both of which were first popularised by the NESL language, in terms of expressiveness as well as efficiency.
Manuel M. T. Chakravarty +4 more
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Data parallel fault simulation
Proceedings of ICCD '95 International Conference on Computer Design. VLSI in Computers and Processors, 1999Fault simulation is a compute intensive problem. Data parallel simulation on multiple processors is one method to reduce fault simulation time. We discuss a novel technique to partition the fault set for data parallel fault simulation. When applied statically, the technique can scale well for up to eight processors. The fault set partitioning technique
Minesh B. Amin, Bapiraju Vinnakota
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Parallel algorithms for data compression
Journal of the ACM, 1985Parallel algorithms for data compression by textual substitution that are suitable for VLSI implementation are studied. Both “static” and “dynamic” dictionary schemes are considered.
M. E. Gonzalez Smith, James A. Storer
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A geometrical data-parallel language
ACM SIGPLAN Notices, 1994The HELP project proposes a model of data-parallel programming allowing a programmer to develop an algorithm the nearest of his thought. Usually, for many parts of a data-parallel program, the manipulations of data could be modelized as geometrical migrations in side a cartesian reference space.We define the language C-HELP in the frame of explicit ...
Jean-Luc Dekeyser +2 more
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1992
We have shown how graphical languages such as CODE/ROPE and PPSE can be used to design SIMD or data parallel programs. The advantages of this approach are machine independence, design clarity, automated program analysis, and accelerated software development.
Ted G. Lewis, R. Currey, Jie Liu 0026
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We have shown how graphical languages such as CODE/ROPE and PPSE can be used to design SIMD or data parallel programs. The advantages of this approach are machine independence, design clarity, automated program analysis, and accelerated software development.
Ted G. Lewis, R. Currey, Jie Liu 0026
openaire +1 more source