Running Twitter services on Graal has been very successful and saved Twitter a lot of money on data center costs. But Twitter would like to run more efficiently to reduce cost even more—who doesn’t?
Chris Thalinger walks you through how Twitter uses its machine learning framework Autotune to tune Graal inlining parameters and details the performance improvement Twitter showed after autotuning Graal.
Chris Thalinger is a staff software engineer at Twitter who has been working on Java virtual machines for over 14 years. His main expertise is in compiler technology with just-in-time compilation, in particular. He was involved with the CACAO and GNU Classpath projects, but his focus shifted to OpenJDK as soon as Sun made the Java development kit (JDK) open source. Previously, Chris worked on the HotSpot JVM at Sun and Oracle.
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