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Dmitry Tiagulskyi, Yaroslav Yermilov "It Scales Until It Doesn’t"


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We are used to thinking that “high-load” means distributed systems, computing power, application, and kernel profiling. But sometimes you can’t simply scale your cluster. Maybe your hashmaps don’t fit in the server memory. Maybe you need single-digit millisecond latency. Maybe the cost is too high. Or your server is a … mobile phone.

In this talk, we will show how popular and lesser-known algorithms, data structures, and systems tuning helped us to overcome these blockers. Who said you don’t need to know algorithms nowadays?

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Dmitry Tiagulskyi, Yaroslav Yermilov "It Scales Until It Doesn’t"

  1. 1. It Scales Until It Doesn’t - Software Engineer @ Core Services - Software Engineer @ Core Services
  2. 2. ● Algorithms & Data Structures for Language Models (Ngrams) ○ Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein “Introduction to Algorithms” (Chapter 11 “Hash Tables” (253) specifically) ○ F.C. Botelho, R. Pagh, and N. Ziviani. Simple and space-efficient minimal perfect hash functions. In Proc. of the 10th Workshop on Algorithms and Data Structures (WADs’07), pages 139–150. Springer LNCS vol. 4619, 2007. ○ Djamal Belazzougui, Fabiano Botelho, and Martin Dietzfelbinger. 2009. Hash, displace, and compress. Algorithms - ESA 2009, pages 682–693. ○ Kenneth Heafield. 2011. KenLM: Faster and smaller language model queries. In Proceedings of the Sixth Workshop on Statistical Machine Translation, Edinburgh, UK, July. Association for Computational Linguistics. ○ Adam Pauls and Dan Klein. 2011. Faster and smaller ngram language models. In Proceedings of ACL, Portland, Oregon. ○ David Talbot and Miles Osborne. 2007. Randomised language modelling for statistical machine translation. In Proceedings of ACL, pages 512–519, Prague, Czech Republic. ○ D. Guthrie, M. Hepple, and W. Liu, “Efficient minimal perfect hash language models,” in Proceedings of LREC’10. Valletta, Malta: European Language Resources Association (ELRA), May 2010. References
  3. 3. ● AWS Virtualization, ENA ○ ○ ○ ○ AWS re:Invent 2017: Optimizing Network Performance for Amazon EC2 Instances: ○ ○ AWS re:Invent 2017: C5 Instances and the Evolution of Amazon EC2 Virtualization: ○ ○ References
  4. 4. ● NUMA ○ ○ ● Network Servers, IO Multiplexing & Epoll ○ ○ Asynchronous IO with Boost.Asio: ○ ○ ● Netty ○ One Framework to rule them all by Norman Maurer: ○ References
  5. 5. ● Benchmarking ○ ○ ○ ○ ● Java Profiling ○ ○ ○ References