Results 1 to 10 of about 178,091 (333)
Fiber Clustering Acceleration With a Modified Kmeans++ Algorithm Using Data Parallelism [PDF]
Frontiers in Neuroinformatics, 2021 Fiber clustering methods are typically used in brain research to study the organization of white matter bundles from large diffusion MRI tractography datasets.
Isaac Goicovich +8 more
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Algebraic data parallelism implementation in HPF
Lietuvos Matematikos Rinkinys, 2002 There is not abstract.
Valdona Judickaitė +2 more
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An efficient algorithm for data parallelism based on stochastic optimization
Alexandria Engineering Journal, 2022 Deep neural network models can achieve greater performance in numerous machine learning tasks by raising the depth of the model and the amount of training data samples.
Khalid Abdulaziz Alnowibet +3 more
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Skeletons for data parallelism in p31
, 1997 This paper addresses the application of a skeleton/template compiling strategy to structured data parallel computations. In particular, we discuss how data parallelism is expressed and implemented in p31, a structured parallel language based on skeletons.
Marco Danelutto +2 more
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Quantum data parallelism in quantum neural networks
Physical Review ResearchQuantum neural networks hold promise for achieving lower generalization error bounds and enhanced computational efficiency in processing certain datasets.
Sixuan Wu, Yue Zhang, Jian Li
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Adaptive memory reservation strategy for heavy workloads in the Spark environment [PDF]
PeerJ Computer ScienceThe rise of the Internet of Things (IoT) and Industry 2.0 has spurred a growing need for extensive data computing, and Spark emerged as a promising Big Data platform, attributed to its distributed in-memory computing capabilities.
Bohan Li +6 more
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Accelerating Distributed SGD With Group Hybrid Parallelism
IEEE Access, 2021 The scale of model parameters and datasets is rapidly growing for high accuracy in various areas. To train a large-scale deep neural network (DNN) model, a huge amount of computation and memory is required; therefore, a parallelization technique for ...
Kyung-No Joo, Chan-Hyun Youn
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Applied Sciences, 2021 The recent unprecedented success of deep learning (DL) in various fields is underlied by its use of large-scale data and models. Training a large-scale deep neural network (DNN) model with large-scale data, however, is time-consuming.
Yunyong Ko, Sang-Wook Kim
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Relating data-parallelism and (and-) parallelism in logic programs [PDF]
Computer Languages, 1995 Much work has been done in the áreas of and-parallelism and data parallelism in Logic Programs. Such work has proceeded to a certain extent in an independent fashion. Both types of parallelism offer advantages and disadvantages. Traditional (and-) parallel models offer generality, being able to exploit parallelism in a large class of programs ...
Hermenegildo, Manuel V. +1 more
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Achieving new SQL query performance levels through parallel execution in SQL Server [PDF]
E3S Web of Conferences, 2023 This article provides an in-depth look at implementing parallel SQL query processing using the Microsoft SQL Server database management system. It examines how parallelism can significantly accelerate query execution by leveraging multi-core processors ...
Nuriev Marat +3 more
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