Results 11 to 20 of about 21,441,590 (245)
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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A snapshot of parallelism in distributed deep learning training
Revista Colombiana De ComputacionThe accelerated development of applications related to artificial intelligence has generated the creation of increasingly complex neural network models with enormous amounts of parameters, currently reaching up to trillions of parameters.
Hairol Romero-Sandí +2 more
exaly +3 more sources
Enhancing parallelism by removing cyclic data dependencies [PDF]
, 1994 D'Hollander, Erik, Zhang, Fubo
core +3 more sources
A Hybrid Tensor-Expert-Data Parallelism Approach to Optimize Mixture-of-Experts Training [PDF]
International Conference on Supercomputing, 2023 Mixture-of-Experts (MoE) is a neural network architecture that adds sparsely activated expert blocks to a base model, increasing the number of parameters without impacting computational costs.
Siddharth Singh +5 more
semanticscholar +1 more source
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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IEEE Access, 2023 Deep learning (DL) has gained increasing prominence in latency-critical artificial intelligence (AI) applications. Due to the intensive computational requirements of these applications, cloud-centric approaches have been attempted to address this issue ...
Emre Kilcioglu +2 more
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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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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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Communication Optimization Schemes for Accelerating Distributed Deep Learning Systems
Applied Sciences, 2020 In a distributed deep learning system, a parameter server and workers must communicate to exchange gradients and parameters, and the communication cost increases as the number of workers increases.
Jaehwan Lee +4 more
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