[giltene]

While optimization for dynamic languages is a cool research area for a JIT compiler, and may one day help expand the use of dynamic languages on the JVM, the VAST majority of code that runs on JVMs in written in Java, Scala, and Clojure, none of which is dynamic. What you do see in those languages is plenty of idiomatic loops, stream, and buffer processing. All of which benefit greatly from the new vectorization capabilities in AVX2 (and the soon to be widely available AVX512).

perhaps someone should advertise AVX2 and AVX512 under the tag "It's not you father's numeric-processing-focused vectorization"

As for the charts in the blog post: yes, it's a depiction. The Y axis gains will vary by application, obviously. And we've seen that gain range form a few % to high 10s of %s with real workloads, measured by real customers who use them. Use cases span a wide range, including databases, in-memory query engines, search and indexing, protocol gateways, stream processing, messaging, analytics, and matching engines. None are things that classically make you think "numeric processing", BTW.

[mike_hearn]

Yes, I'd very much appreciate some sort of blog post or link on how AVX2/512 can accelerate ordinary business type apps. I know it can be used to accelerate things like hashing, checksumming and so on, but if it's widely applicable to the sort of code normally found on the JVM, that could make a very interesting set of articles.

Thanks for the followup!