Hybrid Map Task Scheduling for GPU-Based Heterogeneous Clusters. - Koichi Shirahata; Hitoshi Sato; Satoshi Matsuoka (CloudCom, 2010)

A GPU Implementation of Generalized Graph Processing Algorithm GIM-V. - Koichi Shirahata; Hitoshi Sato; Toyotaro Suzumura; Satoshi Matsuoka (CLUSTER Workshops, 2012)

A Scalable Implementation of a MapReduce-based Graph Processing Algorithm for Large-Scale Heterogeneous Supercomputers. - Koichi Shirahata; Hitoshi Sato; Toyotaro Suzumura; Satoshi Matsuoka (CCGRID, 2013)

Large-scale distributed sorting for GPU-based heterogeneous supercomputers. - Hideyuki Shamoto; Koichi Shirahata; Aleksandr Drozd; Hitoshi Sato; Satoshi Matsuoka (IEEE BigData, 2014)

Out-of-core GPU memory management for MapReduce-based large-scale graph processing. - Koichi Shirahata; Hitoshi Sato; Satoshi Matsuoka (CLUSTER, 2014)

Memory reduction method for deep neural network training. - Koichi Shirahata; Yasumoto Tomita; Atsushi Ike (MLSP, 2016)

An Automated CNN Recommendation System for Image Classification Tasks. - Song Wang 007; Li Sun 007; Wei Fan 005; Jun Sun 004; Satoshi Naoi; Koichi Shirahata; Takuya Fukagai; Yasumoto Tomita; Atsushi Ike (CoRR, 2016)

GPU-Accelerated Large-Scale Distributed Sorting Coping with Device Memory Capacity. - Hideyuki Shamoto; Koichi Shirahata; Aleksandr Drozd; Hitoshi Sato; Satoshi Matsuoka (IEEE Trans. Big Data, 2016)

An automated CNN recommendation system for image classification tasks. - Song Wang 007; Li Sun 007; Wei Fan 005; Jun Sun 004; Satoshi Naoi; Koichi Shirahata; Takuya Fukagai; Yasumoto Tomita; Atsushi Ike; Tetsutaro Hashimoto (ICME, 2017)

Speed-Up of Object Detection Neural Network with GPU. - Takuya Fukagai; Kyosuke Maeda; Satoshi Tanabe; Koichi Shirahata; Yasumoto Tomita; Atsushi Ike; Akira Nakagawa (ICIP, 2018)

GUNREAL: GPU-accelerated UNsupervised REinforcement and Auxiliary Learning. - Koichi Shirahata; Youri Coppens; Takuya Fukagai; Yasumoto Tomita; Atsushi Ike (Int. J. Netw. Comput., 2018)

Acceleration of Structural Analysis Simulations using CNN-based Auto-Tuning of Solver Tolerance. - Amir Haderbache; Koichi Shirahata; Takuji Yamamoto; Yasumoto Tomita; Hiroshi Okuda (IPDPS Workshops, 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)

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)

Preliminary Performance Analysis of Distributed DNN Training with Relaxed Synchronization. - Koichi Shirahata; Amir Haderbache; Naoto Fukumoto; Kohta Nakashima (IEICE Trans. Electron., 2021)

Accelerating Hybrid DFT Simulations Using Performance Modeling on Supercomputers. - Yosuke Oyama; Takumi Honda; Atsushi Ishikawa; Koichi Shirahata (CCGrid, 2023)

Self-Supervised Learning with Atom Replacement for Catalyst Energy Prediction by Graph Neural Networks. - Yasufumi Sakai; Thang Dang; Shigeki Fukuta; Koichi Shirahata; Atsushi Ishikawa; Atsuki Inoue; Hiroshi Kawaguchi 001; Árni Björn Höskuldsson; Egill Skúlason (INNS DLIA@IJCNN, 2023)