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pro vyhledávání: '"Imamura, Toshiyuki"'
Autor:
Hasegawa, Yuta, Imamura, Toshiyuki, Ina, Takuya, Onodera, Naoyuki, Asahi, Yuuichi, Idomura, Yasuhiro
Publikováno v:
2022 IEEE/ACM Workshop on Latest Advances in Scalable Algorithms for Large-Scale Heterogeneous Systems (ScalAH), pp. 10-17 (2022)
The ensemble data assimilation of computational fluid dynamics simulations based on the lattice Boltzmann method (LBM) and the local ensemble transform Kalman filter (LETKF) is implemented and optimized on a GPU supercomputer based on NVIDIA A100 GPU
Externí odkaz:
http://arxiv.org/abs/2308.03310
Autor:
Hasegawa, Yuta, Onodera, Naoyuki, Asahi, Yuuichi, Ina, Takuya, Imamura, Toshiyuki, Idomura, Yasuhiro
Publikováno v:
Fluid Dynamics Research, 55(6) 065501, 2023
We investigate the applicability of the data assimilation (DA) to large eddy simulations (LESs) based on the lattice Boltzmann method (LBM). We carry out the observing system simulation experiment of a two-dimensional (2D) forced isotropic turbulence
Externí odkaz:
http://arxiv.org/abs/2308.03972
Autor:
Farrell, Steven, Emani, Murali, Balma, Jacob, Drescher, Lukas, Drozd, Aleksandr, Fink, Andreas, Fox, Geoffrey, Kanter, David, Kurth, Thorsten, Mattson, Peter, Mu, Dawei, Ruhela, Amit, Sato, Kento, Shirahata, Koichi, Tabaru, Tsuguchika, Tsaris, Aristeidis, Balewski, Jan, Cumming, Ben, Danjo, Takumi, Domke, Jens, Fukai, Takaaki, Fukumoto, Naoto, Fukushi, Tatsuya, Gerofi, Balazs, Honda, Takumi, Imamura, Toshiyuki, Kasagi, Akihiko, Kawakami, Kentaro, Kudo, Shuhei, Kuroda, Akiyoshi, Martinasso, Maxime, Matsuoka, Satoshi, Mendonça, Henrique, Minami, Kazuki, Ram, Prabhat, Sawada, Takashi, Shankar, Mallikarjun, John, Tom St., Tabuchi, Akihiro, Vishwanath, Venkatram, Wahib, Mohamed, Yamazaki, Masafumi, Yin, Junqi
Scientific communities are increasingly adopting machine learning and deep learning models in their applications to accelerate scientific insights. High performance computing systems are pushing the frontiers of performance with a rich diversity of h
Externí odkaz:
http://arxiv.org/abs/2110.11466
Autor:
Iakymchuk, Roman, Mukunoki, Daichi, Podobas, Artur, Jézéquel, Fabienne, Imamura, Toshiyuki, Fujita, Norihisa, Huthmann, Jens, Kudo, Shuhei, Tan, Yiyu, Domke, Jens, Ohlhus, Kai Torben, Fukaya, Takeshi, Hoshi, Takeo, Murakami, Yuki, Nakata, Maho, Ogita, Takeshi, Sano, Kentaro, Boku, Taisuke
In numerical computations, precision of floating-point computations is a key factor to determine the performance (speed and energy-efficiency) as well as the reliability (accuracy and reproducibility). However, precision generally plays a contrary ro
Externí odkaz:
http://arxiv.org/abs/2004.04628
Publikováno v:
In Computer Physics Communications March 2016 200:57-65
Publikováno v:
High Performance Computing
This paper proposes a method for implementing dense matrix multiplication on FP64 (DGEMM) and FP32 (SGEMM) using Tensor Cores on NVIDIA’s graphics processing units (GPUs). Tensor Cores are special processing units that perform \documentclass[12pt]{
Akademický článek
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Publikováno v:
RAIM (Rencontres Arithmétiques de l'Informatique Mathématique)
RAIM (Rencontres Arithmétiques de l'Informatique Mathématique), May 2021, Paris, France
RAIM (Rencontres Arithmétiques de l'Informatique Mathématique), May 2021, Paris, France
International audience
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::0acc9f0a38c8330c5e4adc03a85a6e01
https://hal.archives-ouvertes.fr/hal-03280736
https://hal.archives-ouvertes.fr/hal-03280736
Numerical validation enables one to improve the reliability of numerical computations that rely upon floating-point operations through obtaining trustful results. Discrete Stochastic Arithmetic (DSA) makes it possible to validate the accuracy of floa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::b12d4cb0e3b3535c0075f3fe1de358e9
https://hal.archives-ouvertes.fr/hal-02486753
https://hal.archives-ouvertes.fr/hal-02486753