著書論文等- 滝沢 寛之 -
表示方法: 表示形式: 表示順:
[]:著書 []:論文 []:総説・解説記事
件数:228件
[2020]
1.[] DeLoc: A Locality and Memory Congestion-aware Task Mapping Method for Modern NUMA Systems.[IEEE Access,(2020)]Mulya Agung, Muhammad Alfian Amrizal, Ryusuke Egawa, and Hiroyuki Takizawa
2.[] Xevolver: A Code Transformation Framework for Separation of System-awareness from Application Codes.[Concurrency and Computation: Practice and Experience,(2020)]Kazuhiko Komatsu, Ayumu Gomi, Ryusuke Egawa, Daisuke Takahashi, Reiji Suda, and Hiroyuki Takizawa
[2019]
3.[] An OpenCL-like Offload Programming Framework for SX-Aurora TSUBASA.[The 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT 2019),(2019),285-291]Hiroyuki Takizawa, Shinji Shiotsuki, Naoki Ebata, and Ryusuke Egawa
4.[] An Automatic MPI Process Mapping Method Considering Locality and Memory Congestion on NUMA Systems.[2019 IEEE 13th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC),(2019),17-24]Mulya Agung, Muhammad Alfian Amrizal, Ryusuke Egawa and Hiroyuki Takizawa
5.[] Optimization of a gas-particle flow solver on vector supercomputers.[The 31st International Conference on Parallel Computational Fluid Dynamics (ParCFD’2019),(2019),1-4]Yoichi Shimomura, Midori Kano, Takashi Soga, Kenta Yamaguchi, Akihiro Musa, Yusuke Mizuno, Shun Takahashi, Ryusuke Egawa, and Hiroyuki Takizawa
6.[] Memory First : A Performance Tuning Strategy Focusing on Memory Access Patterns.[The ISC High Performance conference 2019 (poster),(2019)]Naoki Ebata, Ryusuke Egawa, Yoko Isobe, Ryoji Takaki, and Hiroyuki Takizawa
7.[] Scaling performance for n-body stream computation with a ring of FPGAs.[The International Symposium on Highly-Efficient Accelerators and Reconfigurable Technologies (HEART2019),(2019),1-6]Jens Huthmann, Abiko Shin, Artur Podobas, Kentaro Sano, and Hiroyuki Takizawa
8.[] Scalability Analysis of Deeply Pipelined Tsunami Simulation with Multiple FPGAs.[IEICE Transactions on Information and Systems,E102-D(5),(2019),1029-1036]Antoniette Mondigo, Tomohiro Ueno, Kentaro Sano, and Hiroyuki Takizawa
10.1587/transinf.2018RCP0007
9.[] An Energy Optimization Method for Hybrid In-Memory Checkpointing.[2019 IEEE Symposium in Low-Power and High-Speed Chips (COOL CHIPS)(poster),(2019)]Muhammad Alfian Amrizal, Mulya Agung, Ryusuke Egawa, and Hiroyuki Takizawaza
10.[] The Impacts of Locality and Memory Congestion-aware Thread Mapping on Energy Consumption of Modern NUMA Systems.[2019 IEEE Symposium in Low-Power and High-Speed Chips (COOL CHIPS),(2019)]Mulya Agung, Muhammad Alfian Amrizal, Ryusuke Egawa and Hiroyuki Takizawa
11.[] Performance Evaluation of Different Implementation Schemes of an Iterative Flow Solver on Modern Vector Machines.[Supercomputing Frontiers and Innovations,6(1),(2019),36-47]Kenta Yamaguchi and Takashi Soga and Yoichi Shimomura and Thorsten Reimann and Kazuhiko Komatsu and Ryusuke Egawa and Akihiro Musa and Hiroyuki Takizawa and Hiroaki Kobayashi
10.14529/jsfi190106
12.[] VLSI Design and Test for Systems Dependability.[Springer Japan,(2019)]Hiroyuki Takizawa, Ye Gao, Masayuki Sato, Ryusuke Egawa, and Hiroaki Kobayashi
[2018]
13.[] Advanced Software Technologies for Post-Peta Scale Computing.[Springer,(2018)]Hiroyuki Takizawa, Reiji Suda, Daisuke Takahashi, and Ryusuke Egawa
14.[] Automatic hyperparameter tuning of machine learning models under time constraints.[IEEE Big Data 2018 Workshop,(2018)]Zhen Wang, Agung Mulya, Ryusuke Egawa, Reiji Suda, and Hiroyuki Takizawa
15.[] Enhancing memory bandwidth in a single stream computation with multiple FPGAs.[The 2018 International Conference on Field-Programmable Technology (FPT’18),(2018)]Antoniette Mondigo, Kentaro Sano, and Hiroyuki Takizawa
16.[] A Locality and Memory Congestion-aware Thread Mapping Method for Modern NUMA Systems.[ACM/IEEE Supercomputing Conference 2018 (SC18) (poster),(2018)]Mulya Agung, Muhammad Alfian Amrizal, Ryusuke Egawa, and Hiroyuki Takizawa
17.[] Investigating the Effects of Dynamic Thread Team Size Adjustment for Irregular Applications.[The Sixth International Symposium on Computing and Networking (CANDAR 2018),(2018)]Xiong Xiao, Mulya Agung, Muhammad Alfian Amrizal, Ryusuke Egawa and Hiroyuki Takizawa
18.[] Preconditioner auto-tuning with deep learning for sparse iterative algorithms.[The Sixth International Symposium on Computing and Networking Workshops (CANDARW 2018), LHAM workshop,(2018)]Kenya Yamada, Takahiro Katagiri, Hiroyuki Takizawa, Kazuo Minami, Mitsuo Yokokawa, Toru Nagai and Masao Ogino
19.[] A Machine Learning-based Approach for Selecting SpMV Kernels and Matrix Storage Formats.[IEICE Transactions on Information and Systems,E101-D(9),(2018),2307-2314]Hang Cui, Shoichi Hirasawa, Hiroaki Kobayashi, and Hiroyuki Takizawa
20.[] A Failure Prediction-based Adaptive Checkpointing Method with Less Reliance on Temperature Monitoring for HPC Applications.[2018 IEEE International Conference on Cluster Computing, FTS workshop,(2018),483-491]Muhammad Alfian Amrizal, Pei Li, Mulya Agung, Ryusuke Egawa, and Hiroyuki Takizawa
Page: [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [next]
戻るこのページのトップへ
copyright(c)2005 Tohoku University