PUBLICATIONS

Academic JournalTeam members are underlined.

  • 今村俊幸, コンピュータを用いた大規模な固有値計算, 数理科学 2016年12月号, No.642, p.6-12, 2016.12.1

  • Narimasa Sasa, Susumu Yamada, Masahiko Machida, and Toshiyuki Imamura,

    Accumulated Error in Iterative Use of FFT,

    Nonlinear Theory and Its Applications, IEICS, Vol. 7 (2016) No. 3 pp. 354-361, 2016.7.1.

  • Yosuke Kumagai, Akihiro Fujii, Teruo Tanaka, Yusuke Hirota, Takeshi Fukaya, Toshiyuki Imamura and Reiji Suda
    Performance Analysis of the Chebyshev Basis Conjugate Gradient Method on the K Computer,
    Proc. PPAM2015, Lecture Notes in Computer Science (LNCS), Vol. 9573, pp. 74–85, 2016.

  • Susumu Yamada, Toshiyuki Imamura and Masahiko MACHIDA,

    High Performance Eigenvalue Solver in Exact-diagonalization Method for Hubbard Model on CUDA GPU,

    Proc. ParCo2015, Advances in Parallel Computing, Vol. 27: Parallel Computing: On the Road to Exascale, pp.361-369, IO-Press. 2016.4

  • Toshiyuki Imamura, Takeshi Fukaya, Yusuke Hirota, Susumu Yamada and Masahiko Machida,
    CAHTR: Communication-Avoiding Householder TRidiagonalization,
    Proc. ParCo2015, Advances in Parallel Computing, Vol. 27: Parallel Computing: On the Road to Exascale, pp. 381–390, 2016.

International ConferenceTeam members are underlined and speakers of oral presentations are bold faced.

  • Toshiyuki Imamura, Tetsuya Sakura, Yasunori Futamura,

    Experiences on K computer from a topic focused on the large-scale eigenvalue solver project,

    24th　Workshop on Sustained Simulation Performance, HLRS, Stuttgart, Germany, December 6th, 2016

  • Toshiyuki Imamura,

    Large-scale eigenvalue computation for dense matrices on the K computer,

    1st International Symposium on Research and Education of Computational Science (RECS), U. Tokyo, 2016.11.29 (invited talk)

  • A. Mayumi, Y. Idomura, T.Ina, S.Yamada, T.Imamura,

    Left-Preconditioned Communication-Avoiding Conjugate Gradient Methods for Multiphase CFD Simulations on the K Computer, proceedings of ScalA’16, IEEE. Vol. 7 (2016) No. 3 pp. 354-361, November 13, 2016.

  • Daichi Mukunoki and Toshiyuki Imamura: Reduced-Precision Floating-Point Formats on GPUs for High Performance and Energy Efficient Computation, Proc. IEEE International Conference on Cluster Computing (Cluster 2016), pp. 144-145, Sep. 2016.

  • Toshiyuki Imamura,

    Parallel dense eigenvalue solver and SVD solver for post-petascale computing systems,

    The 9th International Workshop on Parallel Matrix Algorithms and Applications (PMAA16), Bordeaux, France, 2016.7.7.

  • Yusuke Hirota and Toshiyuki Imamura,
    Performance Analysis of the Quadruple Precision Eigensolver Library QPEigenK on the K Computer,
    9th International Workshop on Parallel Matrix Algorithms and Applications (PMAA16), Place de la Victoire, Bordeaux, France, July 6–8, 2016.

  • Yusuke Hirota, Susumu Yamada, Toshiyuki Imamura, Narimasa Sasa and Masahiko Machida,
    Performance of Quadruple Precision Eigenvalue Solver Libraries QPEigenK and QPEigenG on the K Computer,
    HPC in Asia Poster, in conjunction with International Supercomputing Conference (ISC’16), Messe Frankfurt, Frankfurt, Germany, June 19–23, 2016 (poster and ceremony talk) [HPC in Asia Poster Award].

  • Yusuke Morikura, Daichi Mukunoki, Takeshi Fukaya, Naoya Yamanaka, Shin’ichi Oishi: Performance Evaluation of Verified Computation for Linear Systems on Supercomputer, SIAM: East Asian Section Conference (EASIAM 2016), University of Macau, Jun. 20-22, 2016.

  • Toshiyuki Imamura,

    Auto-Tuning for Eigenvalue Solver on the Post Moore’s Era,

    SIAM Conference on Parallel Processing for Scientific Computing (PP16), Paris, France, 2016.4.16.

  • Takeshi Fukaya, Toshiyuki Imamura,

    An Impact of Tuning the Kernel of the Structured QR Factorization in the TSQR,

    SIAM Conference on Parallel Processing for Scientific Computing (PP16), Paris, France, 2016.4.15

  • Susumu Yamada, Toshiyuki Imamura, Masahiko Machida,

    High performance eigenvalue solver for Hubbard model on CPU-GPU hybrid platform,

    SIAM Conference on Parallel Processing for Scientific Computing (PP16), Paris, France, 2016.4.13.

  • Daichi Mukunoki, Toshiyuki Imamura and Daisuke Takahashi: Automatic Thread-Block Size Adjustment for Memory-Bound BLAS Kernels on GPUs, Proc. IEEE 10th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC-16). pp. 377-384, Sep. 2016.

  • Daichi Mukunoki, Toshiyuki Imamura and Daisuke Takahashi: Automatic Thread-Block Size Adjustment for Dense Matrix-Vector Multiplication on CUDA, 2016 Conference on Advanced Topics and Auto Tuning in High-Performance Scientific Computing (ATAT2016), Mathematics Research Center, National Taiwan University, Taipei, Feb. 19, 2016 (Invited).

  • Daichi Mukunoki, Toshiyuki Imamura and Daisuke Takahashi: Introduction of Research Activities for GPU Computing at Large-scale Parallel Numerical Computing Technology Research Team on AICS, The 6th AICS International Symposium, Feb. 2016.

Domestic ConferenceTeam members are underlined.

  • 今村俊幸,

    非同期的な数学的アルゴリズムのソフトウェアの可能性,

    第8回 自動チューニング技術の現状と応用に関するシンポジウム(ATTA2016), 東京大学, 2016.12.25

  • 今村俊幸, 椋木大地: コンシューマレンジGPUに最適化した固有値ソルバーの実装と評価, 情報処理学会研究報, Vol. 2016-HPC-157, No. 7, 2016年12月 (in Japanese).

  • 森倉悠介, 椋木大地, 深谷猛, 山中脩也, 大石進一: 大規模並列計算機における連立1次方程式の精度保証付き数値計算に対する性能評価, 情報処理学会研究報告, Vol. 2016-HPC-157, No. 1, 2016年12月 (in Japanese).

  • 大井 祥栄, 時間並列計算手法に関する研究開発動向の調査について, 平成28年度自動チューニング研究会マイクロワークショップ, 登別温泉 第一滝本館, 北海道登別市, 2016/10/31. (in Japanese)

  • 廣田悠輔，今村俊幸，
    メニーコアCPUにおける割統治法ルーチンの性能評価，
    平成28年度自動チューニング研究会マイクロワークショップ， 第一滝本館，登別市，2016年10月30–31日 (in Japanese)．

  • 椋木大地, 今村俊幸, 高橋大介: PascalアーキテクチャGPUにおける線形計算カーネルの実装技術の検討, GTC Japan 2016, 2016年10月 (in Japanese).

  • 大井祥栄, 廣田悠輔, 椋木大地, 今村俊幸: KMATHLIB -High Performance and Scalable Numerical Library for the K Computer-, 応用数理学会2016年度年会, 2016年9月 (in Japanese).

  • 永井佑紀, 篠原康，山田進、二村保徳，今村俊幸、櫻井鉄也

    動的平均場理論に対する、LOBPCG法とshifted　COCG法を用いた厳密対角化ソルバー,

    日本物理学会・2016年秋季大会, 金沢大学角間キャンパス, 2016.9.13.

  • 廣田悠輔，山田進，今村俊幸，佐々成正，町田昌彦
    4倍精度固有値ソルバライブラリQPEigenKの京コンピュータにおける性能分析，
    日本応用数理学会 2016年度 年会，北九州国際会議場，北九州市，2016年9月12–14日 (in Japanese)．

  • 廣田悠輔，今村俊幸，
    4倍精度固有値ソルバの京コンピュータにおける性能分析，
    研究集会「応用可積分系の進展」， しあわせの村，神戸市，2016年8月28–30日 (in Japanese)．

  • 折居 茂夫, 今村 俊幸, 山本 義郎

    モデルパラメータに非負制約を課した回帰モデルによる大規模並列計算の性能予測,

    情報処理学会研究報告 2016-HPC-155, 19, 2016.8.

  • 大井 祥栄, 3次元Meshless Time-Domain Methodの高速化, 第45回数値解析シンポジウム (NAS2016), 霧島ホテル, 鹿児島県霧島市, 2016/06/08.

  • 園田 大二郎, 大井 祥栄, 龍野 智哉, 渦度方程式への仮想吸収層への実装, 第45回数値解析シンポジウム (NAS2016), 霧島ホテル, 鹿児島県霧島市, 2016/06/08. (In Japanese)

  • Yusuke Morikura, Daichi Mukunoki, Takeshi Fukaya, Naoya Yamanaka and Shin’ichi Oishi: Performance Evaluation of Verified Computation for Linear Systems on Parallel Computers, 2nd Annual Meeting on Advanced Computing System and Infrastructure (ACSI2016), Jan. 2016.