Results 11 to 20 of about 1,316 (216)
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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Parallel Optimization Method of Unstructured-grid Computing in CFD for DomesticHeterogeneous Many-core Architecture [PDF]
Jisuanji kexue, 2022 Sunway TaihuLight ranked first in the global supercomputer top 500 list 2016－2018 with a peak performance of 125.4 PFlops.Its computing power is mainly attributed to the domestic SW26010 many-core RISC processor.CFD unstructured-grid computing has always
CHEN Xin, LI Fang, DING Hai-xin, SUN Wei-ze, LIU Xin, CHEN De-xun, YE Yue-jin, HE Xiang
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ISPRS International Journal of Geo-Information, 2017 Parallel processing in the real-time visualization of three-dimensional Geographic Information Systems (3DGIS) has tended to concentrate on algorithm levels in recent years, and most of the existing methods employ multiple threads in a Central Processing
Dongbo Zhou +4 more
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Novel VLSI Architectures and Micro-Cell Libraries for Subscalar Computations
IEEE Access, 2022 Parallelism is the key to enhancing the throughput of computing structures. However, it is well established that the presence of data-flow dependencies adversely impacts the exploitation of such parallelism. This paper presents a case for a new computing
Kumar Sambhav Pandey, Hitesh Shrimali
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Towards accelerating model parallelism in distributed deep learning systems.
PLoS ONE, 2023 Modern deep neural networks cannot be often trained on a single GPU due to large model size and large data size. Model parallelism splits a model for multiple GPUs, but making it scalable and seamless is challenging due to different information sharing ...
Hyeonseong Choi +3 more
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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
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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
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Symmetries in data parallelism [PDF]
The Computer Journal, 1995 A comprehensive formalization of data-parallel (DP) symmetries in an imperative language paradigm without nesting is presented, which includes translational, affine and access symmetries. A subtyping system which takes these symmetries into account is discussed.
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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
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