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  1. 会議発表論文

Multi-GPU Acceleration of Optical Flow Computation in Visual Functional Simulation

https://uec.repo.nii.ac.jp/records/1920
https://uec.repo.nii.ac.jp/records/1920
01bdc8da-a3a4-40ab-b050-140ac3eb9e8b
名前 / ファイル ライセンス アクション
9000000555.pdf 9000000555.pdf (1.5 MB)
Item type 会議発表論文 / Conference Paper(1)
公開日 2016-09-15
タイトル
言語 en
タイトル Multi-GPU Acceleration of Optical Flow Computation in Visual Functional Simulation
言語
言語 eng
キーワード
言語 en
主題Scheme Other
主題 Parallel computing
キーワード
言語 en
主題Scheme Other
主題 CUDA
キーワード
言語 en
主題Scheme Other
主題 GPU
キーワード
言語 en
主題Scheme Other
主題 MPI
キーワード
言語 en
主題Scheme Other
主題 Numerical simulation
キーワード
言語 en
主題Scheme Other
主題 Convolution
キーワード
言語 en
主題Scheme Other
主題 Visual neuron systemsimulation
資源タイプ
資源タイプ識別子 http://purl.org/coar/resource_type/c_5794
資源タイプ conference paper
著者 Junichi, Ohmura

× Junichi, Ohmura

WEKO 6356

en Junichi, Ohmura

Search repository
Akira, Egashira

× Akira, Egashira

WEKO 6357

en Akira, Egashira

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Shunji, Satoh

× Shunji, Satoh

WEKO 6358

en Shunji, Satoh

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Takefumi, Miyoshi

× Takefumi, Miyoshi

WEKO 6359

en Takefumi, Miyoshi

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Hidetsugu, Irie

× Hidetsugu, Irie

WEKO 6360

en Hidetsugu, Irie

Search repository
Tsutomu, Yoshinaga

× Tsutomu, Yoshinaga

WEKO 6361

en Tsutomu, Yoshinaga

Search repository
内容記述
内容記述タイプ Other
内容記述 Numerical simulation for visual processing of thehuman brain is one of time-consuming applications. This papershows acceleration techniques for a simulation program of thevisual processing. We parallelize convolution calculations, whichare core operations, which the simulation program requests, on aGPU-accelerated PC cluster. Our implementation includes threeimprovement points. Firstly, we consider efficient data mappingonto global and shared memories1 of the GPU. Secondly, multipleconvolutions for the same input data are computed by eachnode’s GPU, referred to as package execution. Finally, an input2-dimensional image is divided into regions and convolutions forthese regions are executed in parallel utilizing MPI (MessagePassing Interface). Our experimental results show a linearspeedup up to 12 nodes in the PC cluster for the convolutionprogram. We also show the effects of the package executionand reduced communication on NVIDIA tesla C1060 and C2070,respectively.
書誌情報 en : Second International Conference on Networking and Computing

p. 228-234, 発行日 2011-09-09
出版者
出版者 IEEE
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