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?

1. It Scales
Until It Doesn’t
yaroslav.yermilov@grammarly.com - Software Engineer @ Core Services
dima.tiagulskyi@grammarly.com - Software Engineer @ Core Services

109. ● 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

110. ● AWS Virtualization, ENA
○ https://en.wikipedia.org/wiki/X86_virtualization
○ https://aws.amazon.com/ru/blogs/aws/elastic-network-adapter-high-performance-network-interface-for-amazon-ec2/
○ https://medium.com/@paccattam/aws-enhanced-networking-an-overview-aee8a852cf5c
○ AWS re:Invent 2017: Optimizing Network Performance for Amazon EC2 Instances: https://youtu.be/-dWgqtGKPfc
○ https://www.kernel.org/doc/Documentation/networking/scaling.txt
○ AWS re:Invent 2017: C5 Instances and the Evolution of Amazon EC2 Virtualization: https://youtu.be/LabltEXk0VQ
○ http://www.brendangregg.com/blog/2017-11-29/aws-ec2-virtualization-2017.html
○ https://blog.ubuntu.com/2017/04/05/ubuntu-on-aws-gets-serious-performance-boost-with-aws-tuned-kernel
References

111. ● NUMA
○ https://www.redhat.com/files/summit/2014/summit2014_riel_chegu_w_0340_automatic_numa_balancing.pdf
○ https://www.cmg.org/wp-content/uploads/2015/10/numa.pdf
● Network Servers, IO Multiplexing & Epoll
○ https://eli.thegreenplace.net/2018/measuring-context-switching-and-memory-overheads-for-linux-threads/
○ Asynchronous IO with Boost.Asio: https://youtu.be/rwOv_tw2eA4
○ https://eklitzke.org/blocking-io-nonblocking-io-and-epoll
○ https://habr.com/post/416669/
● Netty
○ One Framework to rule them all by Norman Maurer: https://youtu.be/DKJ0w30M0vg
○ https://dou.ua/lenta/columns/netty-optimization/
References

112. ● Benchmarking
○ http://www.brendangregg.com/blog/2018-06-30/benchmarking-checklist.html
○ http://www.brendangregg.com/activebenchmarking.html
○ http://www.brendangregg.com/usemethod.html
○ http://www.brendangregg.com/linuxperf.html
● Java Profiling
○ http://psy-lob-saw.blogspot.com/2016/02/why-most-sampling-java-profilers-are.html
○ https://github.com/jvm-profiling-tools/async-profiler
○ https://medium.com/netflix-techblog/java-in-flames-e763b3d32166
References