@techreport{oai:ipsj.ixsq.nii.ac.jp:00176383,
 author = {中島, 研吾 and 大島, 聡史 and 塙, 敏博 and 星野, 哲也 and 伊田, 明弘 and Kengo, Nakajima and Satoshi, Ohshima and Toshihiro, Hanawa and Tetsuya, Hoshino and Akihiro, Ida},
 issue = {16},
 month = {Dec},
 note = {SELL-C-σ 法は疎行列演算の性能を高める行列格納手法として注目されているが，これまでは専ら疎行列ベクトル積に適用されてきた．科学技術計算において広く使用されている ICCG 法は前進後退代入，不完全コレスキー分解等のデータ依存性を有するプロセスを含むため，多色順序付け等によって並列性を抽出する必要がある．本研究は世界でも初めて，ICCG 法に SELL-C-σ 法を適用した事例である．Intel Xeon Phi （Knights Corner，Knights Landing） 上での性能評価を実施し，特に Knights Landing 上では従来手法と比較して高い性能改善を達成することができた．, SELL-C-σ storage format is widely known method for efficient computation of sparse matrices. It has been mainly applied to SpMV operations. ICCG is used for solving linear equations with sparse matrices in a wide range of applications of science and engineering. Because ICCG includes operations with data-dependency, such as forward / backward substitutions, and incomplete Cholesky factorization, extraction of parallelism by reordering is needed. The present work is the first example, where SELL-C-σ storage format is applied to ICCG. Performance of the developed solver has been evaluated on Intel Xeon Phi (Knights Corner, Knights Landing), and performance of the ICCG with SELL-C-σ on Knights Landing was better than existing methods.},
 title = {ICCG法ソルバーのIntel Xeon Phi向け最適化},
 year = {2016}
}