<html><head><meta http-equiv="Content-Type" content="text/html; charset=utf-8"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class="">Title:<div class=""><br class=""></div><div class="">Performance tuning Twitter services with Graal and Machine Learning</div><div class=""><br class=""></div><div class="">Abstract:</div><div class=""><br class=""></div><div class="">Running Twitter services on Graal has been very successful and saved Twitter a lot of money on datacenter cost. But we would like to run more efficient to reduce cost even more. I mean, who doesn’t? In order to do this we are using our Machine Learning framework called Autotune to tune Graal inlining parameters. This talk will show how much performance improvement we got by autotuning Graal.</div><div class=""><br class=""></div><div class="">Recording:</div><div class=""><br class=""></div><div class="">OK</div><div class=""><br class=""></div><div class="">Bio:</div><div class=""><br class=""></div><div class="">Chris Thalinger is a software engineer working on Java Virtual Machines for over 14 years. His main expertise is in compiler technology with Just-In-Time compilation in particular. Initially being involved with the CACAO and GNU Classpath projects, the focus shifted to OpenJDK as soon as Sun made the JDK open-source. Ever since Chris has worked on the HotSpot JVM at Sun, Oracle and now at Twitter.</div><div class=""><br class=""></div><div class=""><div class="">Microblog URL:</div></div><div class=""><br class=""></div><div class=""><a href="https://twitter.com/christhalinger" class="">https://twitter.com/christhalinger</a></div><div class=""><br class=""></div></body></html>