Design of a Shared Memory mechanism for efficient paralell processing in PostgreSQL. - Yoshifumi Ujibashi; Minoru Nakamura; Tsuguchika Tabaru; Takushi Hashida; Motoyuki Kawaba; Lilian Harada (IISA, 2015)

Fast algorithm using summed area tables with unified layer performing convolution and average pooling. - Akihiko Kasagi; Tsuguchika Tabaru; Hirotaka Tamura (MLSP, 2017)

Structured Sparse Fully-Connected Layers in the CNNs and Its GPU Acceleration. - Naoki Matsumura; Yasuaki Ito; Koji Nakano; Akihiko Kasagi; Tsuguchika Tabaru (CANDAR Workshops, 2019)

Efficient cuDNN-Compatible Convolution-Pooling on the GPU. - Shunsuke Suita; Takahiro Nishimura; Hiroki Tokura; Koji Nakano; Yasuaki Ito; Akihiko Kasagi; Tsuguchika Tabaru (PPAM (2), 2019)

Yet Another Accelerated SGD: ResNet-50 Training on ImageNet in 74.7 seconds. - Masafumi Yamazaki; Akihiko Kasagi; Akihiro Tabuchi; Takumi Honda; Masahiro Miwa; Naoto Fukumoto; Tsuguchika Tabaru; Atsushi Ike; Kohta Nakashima (CoRR, 2019)

An Efficient Technique for Large Mini-batch Challenge of DNNs Training on Large Scale Cluster. - Akihiko Kasagi; Akihiro Tabuchi; Masafumi Yamazaki; Takumi Honda; Masahiro Miwa; Naoto Fukumoto; Tsuguchika Tabaru; Atsushi Ike; Kohta Nakashima (HPDC, 2020)

Huffman Coding with Gap Arrays for GPU Acceleration. - Naoya Yamamoto; Koji Nakano; Yasuaki Ito; Daisuke Takafuji; Akihiko Kasagi; Tsuguchika Tabaru (ICPP, 2020)

An Efficient Multicore CPU Implementation for Convolution-Pooling Computation in CNNs. - Hiroki Kataoka; Kohei Yamashita; Yasuaki Ito; Koji Nakano; Akihiko Kasagi; Tsuguchika Tabaru (IPDPS Workshops, 2020)

Efficient convolution pooling on the GPU. - Shunsuke Suita; Takahiro Nishimura; Hiroki Tokura; Koji Nakano; Yasuaki Ito; Akihiko Kasagi; Tsuguchika Tabaru (J. Parallel Distributed Comput., 2020)

The 16, 384-node Parallelism of 3D-CNN Training on An Arm CPU based Supercomputer. - Akihiro Tabuchi; Koichi Shirahata; Masafumi Yamazaki; Akihiko Kasagi; Takumi Honda; Kouji Kurihara; Kentaro Kawakami; Tsuguchika Tabaru; Naoto Fukumoto; Akiyoshi Kuroda; Takaaki Fukai; Kento Sato (HiPC, 2021)

Efficient and Large Scale Pre-training Techniques for Japanese Natural Language Processing. - Akihiko Kasagi; Masahiro Asaoka; Akihiro Tabuchi; Yosuke Oyama; Takumi Honda; Yasufumi Sakai; Thang Dang; Tsuguchika Tabaru (CANDAR, 2021)

MLPerf™ HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems. - Steven Farrell; Murali Emani; Jacob Balma; Lukas Drescher; Aleksandr Drozd; Andreas Fink; Geoffrey C. Fox; David Kanter; Thorsten Kurth; Peter Mattson; Dawei Mu; Amit Ruhela; et al. (MLHPC@SC, 2021)

MLPerf HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems. - Steven Farrell; Murali Emani; Jacob Balma; Lukas Drescher; Aleksandr Drozd; Andreas Fink; Geoffrey C. Fox; David Kanter; Thorsten Kurth; Peter Mattson; Dawei Mu; Amit Ruhela; et al. (CoRR, 2021)

Regularizing Data for Improving Execution Time of NLP Model. - Thang Dang; Yasufumi Sakai; Tsuguchika Tabaru; Akihiko Kasagi (FLAIRS, 2022)

Automatic Pruning Rate Derivation for Structured Pruning of Deep Neural Networks. - Yasufumi Sakai; Akinori Iwakawa; Tsuguchika Tabaru; Atsuki Inoue; Hiroshi Kawaguchi 001 (ICPR, 2022)

Structured Pruning with Automatic Pruning Rate Derivation for Image Processing Neural Networks. - Yasufumi Sakai; Akinori Iwakawa; Tsuguchika Tabaru (ISMSI, 2022)

Accelerating LiNGAM Causal Discovery with Massive Parallel Execution on Supercomputer Fugaku. - Kazuhito Matsuda; Kouji Kurihara; Kentaro Kawakami; Masafumi Yamazaki; Fuyuka Yamada; Tsuguchika Tabaru; Ken Yokoyama (IEICE Trans. Inf. Syst., 2022)

Optimization of NumPy Transcendental Functions for Arm SVE. - Fuyuka Yamada; Kentaro Kawakami; Kouji Kurihara; Kazuhito Matsuda; Tsuguchika Tabaru (HPC Asia Workshops, 2023)

A novel structured sparse fully connected layer in convolutional neural networks. - Naoki Matsumura; Yasuaki Ito; Koji Nakano; Akihiko Kasagi; Tsuguchika Tabaru (Concurr. Comput. Pract. Exp., 2023)

NEEBS: Nonexpert large-scale environment building system for deep neural network. - Yoshiharu Tajima; Masahiro Asaoka; Akihiro Tabuchi; Akihiko Kasagi; Tsuguchika Tabaru (Concurr. Comput. Pract. Exp., 2023)

Direct Quantized Training of Language Models with Stochastic Rounding. - Kaiyan Zhao; Tsuguchika Tabaru; Kenichi Kobayashi 001; Takumi Honda; Masafumi Yamazaki; Yoshimasa Tsuruoka (CoRR, 2024)

Revisiting Replay and Gradient Alignment for Continual Pre-Training of Large Language Models. - Istabrak Abbes; Gopeshh Subbaraj; Matthew Riemer; Nizar Islah; Benjamin Thérien; Tsuguchika Tabaru; Hiroaki Kingetsu; Sarath Chandar; Irina Rish (CoRR, 2025)