Results 61 to 70 of about 27,095 (213)

Pipeline Parallelism With Elastic Averaging

IEEE Access
To accelerate the training speed of massive DNN models on large-scale datasets, distributed training techniques, including data parallelism and model parallelism, have been extensively studied.
Bongwon Jang, In-Chul Yoo, Dongsuk Yook
doaj   +1 more source

Exploiting data parallelism in artificial neural networks with Haskell [PDF]

, 2009
textFunctional parallel programming techniques for feed-forward artificial neural networks trained using backpropagation learning are analyzed. In particular, the Data Parallel Haskell extension to the Glasgow Haskell Compiler is considered as a tool for ...
Heartsfield, Gregory Lynn
core  

Quantum data parallelism in quantum neural networks

Physical Review Research
Quantum neural networks hold promise for achieving lower generalization error bounds and enhanced computational efficiency in processing certain datasets.
Sixuan Wu, Yue Zhang, Jian Li
doaj   +1 more source

Data Parallelism in Java

, 1998
Java supports threads and remote method invocation, but it does not support either data-parallel or distributed programming. This paper discusses Java’s shortcomings with respect to data-parallel programming and presents workarounds. The technical contributions of this paper are twofold: a source-to-source transformation that maps foral l statements ...
openaire   +2 more sources

Object Template Abstractions for Light-Weight Data-Parallelism

, 2008
Data-parallelism is a widely used model for parallel programming. Control structures like parallel DO loops, and data structures like collections have been used to express data-parallelism. In typical implementations, these constructs are ' at '
Neelakantan Sundaresan, Dennis Gannon
core  

Data parallelism in training sparse neural networks

, 2020
Network pruning is an effective methodology to compress large neural networks, and sparse neural networks obtained by pruning can benefit from their reduced memory and computational costs at use. Notably, recent advances have found that it is possible to
Lee, N, Ajanthan, T, Jaggi, M, LEE, NAMHOON, Torr, Philip, Ajanthan, Thalaiyasingam, Torr, Phs, Jaggi, Martin   +7 more
core   +1 more source

A note on data-parallelism and (and-parallel) prolog

, 1994
Abstract is not available.
Hermenegildo, Manuel V., Carro Liñares, Manuel   +1 more
openaire   +1 more source

Study on Distributed Training Optimization Based on Hybrid Parallel [PDF]

Jisuanji kexue
Large-scale neural network training is a hot topic in the field of deep learning,and distributed training stands out as one of the most effective methods for training large neural networks across multiple nodes.Distributed training typically involves ...
XU Jinlong, LI Pengfei, LI Jianan, CHEN Biaoyuan, GAO Wei, HAN Lin
doaj   +1 more source

Integrated Support for Task and Data Parallelism

, 1994
We present an overview of research at the CRPC designed to provide an efficient, portable programming model for scientific applications possessing both task and data parallelism.
Ken Kennedy, Chau-wen Tseng, K. Mani Chandy, Ian Foster, Mani Chandy, Charles Koelbel   +5 more
core  

Braid: Integrating task and data parallelism

, 1995
Archetype data parallel or task parallel applications are well served by contemporary languages. However, for applications containing a balance of task and data parallel-ism the choice of language is less clear.
Emily A. West, Andrew S. Grimshaw
core