Search Results - "Fukumoto, Naoto"

The 16,384-node Parallelism of 3D-CNN Training on An Arm CPU based Supercomputer by Tabuchi, Akihiro, Shirahata, Koichi, Yamazaki, Masafumi, Kasagi, Akihiko, Honda, Takumi, Kurihara, Kouji, Kawakami, Kentaro, Tabaru, Tsuguchika, Fukumoto, Naoto, Kuroda, Akiyoshi, Fukai, Takaaki, Sato, Kento

“…As the computational cost and datasets available for deep neural network training continue to increase, there is a significant demand for fast distributed…”
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A traffic-aware memory-cube network using bypassing by Shikama, Yoshiya, Kawano, Ryuta, Matsutani, Hiroki, Amano, Hideharu, Nagasaka, Yusuke, Fukumoto, Naoto, Koibuchi, Michihiro

“…Three-dimensional stack memory which provides both high-bandwidth access and large capacity is a promising technology for next-generation computer systems…”
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Efficient Collision-Free MTTKRP Algorithm for Multi-core CPUs with Less Memory Usage by Nagasaka, Yusuke, Fukumoto, Naoto

“…Tensor decomposition is often used to extract underlying features in the analysis of large and multi-dimensional data. For the tensor data with sparse…”
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Performance Analysis of Multi-Containerized MD Simulations for Low-Level Resource Allocation by Okuno, Shingo, Hirai, Akira, Fukumoto, Naoto

“…This study discusses scheduling strategies to maximize ensemble throughput, which is the total throughput of multiple containers running simultaneously. Such a…”
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Towards Straggler-Tolerant and Accuracy-Aware Distributed DNN Training in Clouds by Okuno, Shingo, Miwa, Masahiro, Fukumoto, Naoto

“…This study investigated how straggler mitigation affects accuracy during distributed training. While distributed training is one promising way to shorten…”
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Performance Analysis of Quantum Computer Simulators Across Different Environments by Aoki, Nozomi, Yamazaki, Masafumi, Hirai, Akira, Yamaoka, Mari, Fukumoto, Naoto, Kasagi, Akihiko, Oguchi, Masato

“…Quantum computers can achieve extremely fast computations for certain problems using quantum properties. Due to these factors, research and development in the…”
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3D implemented SRAM/DRAM hybrid cache architecture for high-performance and low power consumption by Inoue, Koji, Hashiguchi, Shinya, Ueno, Shinya, Fukumoto, Naoto, Murakami, Kazuaki

“…This paper introduces our research status focusing on 3D-implemented microprocessors. 3D-IC is one of the most interesting techniques to achieve…”
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mpiQulacs: A Scalable Distributed Quantum Computer Simulator for ARM-based Clusters by Tabuchi, Akihiro, Imamura, Satoshi, Yamazaki, Masafumi, Honda, Takumi, Kasagi, Akihiko, Nakao, Hiroshi, Fukumoto, Naoto, Nakashima, Kohta

“…Quantum computer simulators running on classical computers are essential for developing real quantum computers and emerging quantum applications. In…”
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mpiQulacs: A Distributed Quantum Computer Simulator for A64FX-based Cluster Systems by Imamura, Satoshi, Yamazaki, Masafumi, Honda, Takumi, Kasagi, Akihiko, Tabuchi, Akihiro, Nakao, Hiroshi, Fukumoto, Naoto, Nakashima, Kohta

“…Quantum computer simulators running on classical computers are essential for developing real quantum computers and emerging quantum applications. In…”
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MLPerf™ HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems by 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, Mendonca, 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…”
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Low-Latency Low-Energy Memory-Cube Networks using Dual-Voltage Datapaths by Shikama, Yoshiya, Kawano, Ryuta, Matsutani, Hiroki, Amano, Hideharu, Nagasaka, Yusuke, Fukumoto, Naoto, Koibuchi, Michihiro

“…Three-dimensional stack memory that provides both high-bandwidth access and large capacity is a promising technology for next-generation computer systems…”
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Yet Another Accelerated SGD: ResNet-50 Training on ImageNet in 74.7 seconds by Yamazaki, Masafumi, Kasagi, Akihiko, Tabuchi, Akihiro, Honda, Takumi, Miwa, Masahiro, Fukumoto, Naoto, Tabaru, Tsuguchika, Ike, Atsushi, Nakashima, Kohta

“…There has been a strong demand for algorithms that can execute machine learning as faster as possible and the speed of deep learning has accelerated by 30…”
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MLPerf HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems by 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…”
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Analyzing the impact of data prefetching on Chip MultiProcessors by Fukumoto, N., Mihara, T., Inoue, K., Murakami, K.

“…Data prefetching is a well known approach to compensating for poor memory performance, and has been employed in commercial processor chips. Although a number…”
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