Results 21 to 30 of about 27,095 (213)
Parallel Efficient Data Loading [PDF]
Proceedings of the 8th International Conference on Data Science, Technology and Applications, 2019 In this paper we discuss how we architected and developed a parallel data loader for LeanXcale database. The loader is characterized for its efficiency and parallelism. LeanXcale can scale up and scale out to very large numbers and loading data in the traditional way it is not exploiting its full potential in terms of the loading rate it can reach. For
Jiménez Peris, Ricardo +5 more
openaire +2 more sources
Applied Sciences, 2021 The rapid development of artificial intelligence technology has made deep neural networks (DNNs) widely used in various fields. DNNs have been continuously growing in order to improve the accuracy and quality of the models.
Yingchi Mao +4 more
doaj +1 more source
Spatial Computing as Intensional Data Parallelism [PDF]
, 2010 International audienceIn this paper, we show that various concepts and tools developed in the 90's in the field of data-parallelism provide a relevant spatial programming framework. It allows high level spatial computation specifications to be translated
Jean-Louis Giavitto +5 more
core +1 more source
SingleCaffe: An Efficient Framework for Deep Learning on a Single Node
IEEE Access, 2018 Deep learning (DL) is currently the most promising approach in complicated applications such as computer vision and natural language processing. It thrives with large neural networks and large datasets.
Chenxu Wang +5 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
doaj +1 more source
Integrating parallelism and asynchrony for high-performance software development [PDF]
E3S Web of Conferences, 2023 This article delves into the crucial roles of parallelism and asynchrony in the development of high-performance software programs. It provides an insightful exploration into how these methodologies enhance computing systems' efficiency and performance ...
Zaripova Rimma +2 more
doaj +1 more source
Real-Time Cryptocurrency Price Prediction by Exploiting IoT Concept and Beyond: Cloud Computing, Data Parallelism and Deep Learning [PDF]
, 2020 Cryptocurrency has as of late pulled in extensive consideration in the fields of economics, cryptography, and computer science due to it is an encrypted digital currency, peer- to- peer virtual forex produced using codes, and it is much the same as ...
R, S +3 more
core +1 more source
Dynamically Changing Parallelism with the Asynchronous Sequential Data Flows
Моделирование и анализ информационных систем, 2020 A statically typed version of the data driven functional parallel computing model is proposed. It enables a representation of dynamically changing parallelism by means of asynchronous serial data flows.
Alexander I. Legalov +3 more
doaj +1 more source
Collective Communication Performance Evaluation for Distributed Deep Learning Training
Applied SciencesIn distributed deep learning, the improper use of the collective communication library can lead to a decline in deep learning performance due to increased communication time.
Sookwang Lee, Jaehwan Lee
doaj +1 more source
Impact of Design Decisions on Performance of Embarrassingly Parallel .NET Database Application
Vietnam Journal of Computer ScienceThe implementation of parallel applications is always a challenge. It embraces many distinctive design decisions that are to be taken. The paper presents issues of parallel processing with use of .NET applications and popular Database Management Systems (
Piotr Karwaczyński +6 more
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