mdx: A Cloud Platform for Supporting Data Science and Cross-Disciplinary Research Collaborations

Suzumura, Toyotaro; Sugiki, Akiyoshi; Takizawa, Hiroyuki; Imakura, Akira; Nakamura, Hiroshi; Taura, Kenjiro; Kudoh, Tomohiro; Hanawa, Toshihiro; Sekiya, Yuji; Kobayashi, Hiroki; Kuga, Yohei; Nakamura, Ryo; Jiang, Renhe; Kawase, Junya; Hanai, Masatoshi; Miyazaki, Hiroshi; Ishizaki, Takayoshi; Shimotoku, Daisuké; Miyamoto, Daisuke; Aida, Kento; Takefusa, Atsuko; Kurimoto, Takashi; Sasayama, Koji; Kitagawa, Naoya; Fujiwara, Ikki; Tanimura, Yusuke; Aoki, Takayuki; Endo, Toshio; Ohshima, Satoshi; Fukazawa, Keiichiro; Date, Susumu; Uchibayashi, Toshihiro

doi:10.1109/dasc/picom/cbdcom/cy55231.2022.9927975

Cited by 16 publications

(2 citation statements)

References 17 publications

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“…The experiments used Nvidia A100 GPU × 8 with 40 GB × 8 memory on a high-performance nationwide platform named "mdx," which accelerates Society 5.0 and can be operated by the University of Tokyo and eight other universities and two research institutes [52]. Considering the computational resources, we set the batchsize as 64 for each GPU and 100 epochs for each algorithm.…”

Section: B Classification Experimentsmentioning

confidence: 99%

Deep Learning Approach for Classifying the Built Year and Structure of Individual Buildings by Automatically Linking Street View Images and GIS Building Data

Ogawa

Zhao

Oki

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Section: B Classification Experimentsmentioning

confidence: 99%

Deep Learning Approach for Classifying the Built Year and Structure of Individual Buildings by Automatically Linking Street View Images and GIS Building Data

Ogawa

Zhao

Oki

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…1 and Table 1, respectively. The computer resources and storage consisted of on-premise (System A), a supercomputer system at Kyoto University (System B: https:// www.iimc.kyoto-u.ac.jp/en/services/comp/supercomputer/), a supercomputer system at the University of Tokyo (System C: https://www.cc.utokyo.ac.jp/en/guide/hpc/obcx/), an academic cloud mdx (System D: https://mdx.jp/en/) 17 , and a public cloud, Amazon Web Services (AWS), in the region of Japan (System E). Systems A to E are connected via the Japanese academic network called the science information network (SINET: https://www.sinet.ad.jp/en) 18 .…”

Section: Hybrid Cloud Systemmentioning

confidence: 99%

Design and implementation of a hybrid cloud system for large-scale human genomic research

et al. 2023

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In the field of genomic medical research, the amount of large-scale information continues to increase due to advances in measurement technologies, such as high-performance sequencing and spatial omics, as well as the progress made in genomic cohort studies involving more than one million individuals. Therefore, researchers require more computational resources to analyze this information. Here, we introduce a hybrid cloud system consisting of an on-premise supercomputer, science cloud, and public cloud at the Kyoto University Center for Genomic Medicine in Japan as a solution. This system can flexibly handle various heterogeneous computational resource-demanding bioinformatics tools while scaling the computational capacity. In the hybrid cloud system, we demonstrate the way to properly perform joint genotyping of whole-genome sequencing data for a large population of 11,238, which can be a bottleneck in sequencing data analysis. This system can be one of the reference implementations when dealing with large amounts of genomic medical data in research centers and organizations.

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Web application of an integrated simulation for aquatic environment assessment in coastal and estuarine areas

Matsuzaki,

Inoue,

Kubota

et al. 2024

Environmental Modelling & Software

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mdx: A Cloud Platform for Supporting Data Science and Cross-Disciplinary Research Collaborations

Cited by 16 publications

References 17 publications

Deep Learning Approach for Classifying the Built Year and Structure of Individual Buildings by Automatically Linking Street View Images and GIS Building Data

Deep Learning Approach for Classifying the Built Year and Structure of Individual Buildings by Automatically Linking Street View Images and GIS Building Data

Design and implementation of a hybrid cloud system for large-scale human genomic research

Web application of an integrated simulation for aquatic environment assessment in coastal and estuarine areas

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