SQL Server 使いのための Azure Synapse Analytics - Spark 入門Daiyu Hatakeyama
Japan SQL Server Users Group - 第35回 SQL Server 2019勉強会 - Azure Synapese Analytics - SQL Pool 入門 のセッション資料です。
Spark の位置づけ。Synapse の中での入門編の使い方。そして、Synapse ならではの価値について触れてます。
SQL Server 使いのための Azure Synapse Analytics - Spark 入門Daiyu Hatakeyama
Japan SQL Server Users Group - 第35回 SQL Server 2019勉強会 - Azure Synapese Analytics - SQL Pool 入門 のセッション資料です。
Spark の位置づけ。Synapse の中での入門編の使い方。そして、Synapse ならではの価値について触れてます。
CTF for ビギナーズのネットワーク講習で使用した資料です。
講習に使用したファイルは、以下のリンク先にあります。
https://onedrive.live.com/redir?resid=5EC2715BAF0C5F2B!10056&authkey=!ANE0wqC_trouhy0&ithint=folder%2czip
次の3件の論文を紹介しました:
swDNN: A Library for Accelerating Deep Learning Applications on Sunway TaihuLight
Accelerating Graph and Machine Learning Workloads Using a Shared Memory Multicore Architecture with Auxiliary Support for in-Hardware Explicit Messaging
MOCHA: Morphable locality and compression aware architecture for convolutional neural networks
Rabbit Order: Just-in-time Reordering for Fast Graph AnalysisJunya Arai
The document describes Rabbit Order, a fast reordering algorithm for graph analysis proposed by researchers at NTT Corp. and other institutions. Rabbit Order uses two main techniques: 1) hierarchical community-based ordering to improve locality by co-locating neighboring vertices within communities, and 2) parallel incremental aggregation to quickly obtain the hierarchical community structure and enable fast reordering. Experimental results showed Rabbit Order provided up to 3.5x speedup for PageRank analysis over graphs compared to no reordering, by reducing reordering overheads.
42. 参考文献
• [Tokuhisa ‘12] A. Tokuhisa et al., “Classifying and assembling two-dimensional X-ray laser diffraction
patterns of a single particle to reconstruct the three-dimensional diffraction intensity function: resolution
limit due to the quantum noise.” Acta crystallographica. Section A, Foundations of crystallography, May
2012.
• [Dahlin ‘94] M. D. Dahlin, R. Y. Wang, T. E. Anderson, and D. A. Patterson, “Cooperative caching: using
remote client memory to improve file system performance,” in Proceedings of the 1st USENIX
conference on Operating Systems Design and Implementation, 1994.
• [Sarkar ‘96] P. Sarkar and J. Hartman, “Efficient cooperative caching using hints,” in Proceedings of the
second USENIX symposium on Operating systems design and implementation, 1996, pp. 35–46.
• [安井 ‘12] 安井隆, 清水正明, 堀敦史, and 石川裕, “ローカルディスクを活用するユーザレベル並列ファイルキャッ
シュシステムの設計,” in 先進的計算基盤システムシンポジウム論文集, 2012, vol. 2012, pp. 100–107.
• [Nisar ‘12] A. Nisar, W. Liao, and A. Choudhary, “Delegation-Based I/O Mechanism for High Performance
Computing Systems,” IEEE Trans. Parallel Distrib. Syst., vol. 23, no. 2, pp. 271–279, 2012.
• [Liang ‘05] S. Liang, R. Noronha, and D. K. Panda, “Swapping to Remote Memory over InfiniBand: An
Approach using a High Performance Network Block Device,” in Cluster Computing, 2005. IEEE
International, 2005, pp. 1–10.
• [Nieplocha ‘06] J. Nieplocha, B. Palmer, V. Tipparaju, M. Krishnan, H. Trease, and E. Aprà, “Advances,
Applications and Performance of the Global Arrays Shared Memory Programming Toolkit,” Int. J. High
Perform. Comput. Appl., vol. 20, no. 2, pp. 203–231, 2006.
• [Lever ‘01] Chuck Lever, “Close-To-Open Cache Consistency in the Linux NFS Client,” 2001,
http://www.citi.umich.edu/projects/nfs-perf/results/cel/dnlc.html
42