@inproceedings{oai:ipsj.ixsq.nii.ac.jp:00080214,
 author = {南, 一生 and 井上, 俊介 and 堤, 重信 and 前田, 拓人 and 長谷川, 幸弘 and 黒田, 明義 and 寺井, 優晃 and 横川, 三津夫 and Kazuo, Minami and Shunsuke, Inoue and Shigenobu, Tsutsumi and Takuto, Maeda and Yukihiro, Hasegawa and Akiyoshi, Kuroda and Masaaki, Terai and Mitsuo, Yokokawa},
 book = {ハイパフォーマンスコンピューティングと計算科学シンポジウム論文集},
 month = {Jan},
 note = {疎行列とベクトルの積は、流体や構造計算等の工学や地球科学の分野で多く使用されている計算カーネルであり、プログラムの要求する B/F 値が高く、スカラマシンでは高い CPU 単体性能を得る事が難しい。本稿では、京速コンピュータ 「京」 の汎用マシンとしての性能を実証するために準備しているアプリケーションである Seism3D と FrontFlow/Blue の計算カーネルを題材に、性能の予測方法の提示、実測による性能評価、評価結果に基づくチューニング手法の提示、チューニング結果の性能評価について述べる。, In products of sparse matrix and vectors, which are often seen in application programs such as structural analysis code using finite element methods, the higher ratio of memory bandwidth and floating-point rate (B/Flop) is required by the program. A scalar machine is difficult to obtain high performance in a CPU due to its low ratio of B/Flop. We are developing the K computer as a 10 Peta scale super computer and have to demonstrate its performance by using real applications. Application programs Seism3D and FrontFlow/blue, which have products of sparse matrix and vectors as kernels in the programs, are suitable to show how to obtain higher performance by tuning them to the K computer. In this paper, techniques on how to estimate performance of the codes and to tune them are presented. Results on performance evaluation of tuning versions are also described.},
 pages = {23--31},
 publisher = {情報処理学会},
 title = {「京」コンピュータにおける疎行列とベクトル積の性能チューニングと性能評価},
 volume = {2012},
 year = {2012}
}