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Performance tuning Twitter services with Graal and Machine Learning
Running Twitter services on Graal has been very successful and saved Twitter a lot of money in data center costs. But in order to run more efficiently and reduce costs, even more, Twitter is using a machine learning framework called Autotune to tune Graal inlining parameters. This session shows how much performance improvement was gained by autotuning Graal.
Chris Thalinger is a software engineer who has been working on Java Virtual Machines for more than 14 years. His primary expertise is in compiler technology, with a focus on just-in-time compilation. Initially involved with the CACAO and GNU classpath projects, his 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.
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