[theresistor]

> Do they just target the lowest common denominator of operations? Or do they somehow adapt to the operations supported by the user's CPU?

Mostly the former. Some highly optimized bits of software do the latter—they are built with multiple code paths optimized for different hardware capabilities, and select which one to use at runtime.

> Do dynamic languages (Javascript, Python, PHP...) get a speed boost because they can compile just in time and use all the features of the user's CPU?

Hypothetically yes, but in practice no for the languages you mentioned because they don't map well to things like SIMD. Some JIT-based numerical computing systems as well as JIT-based ML compilers do reap those benefits.

[jsheard]

.NET/C# does pretty well with SIMD for a high level language, it has portable SIMD primitives which get JITed to whatever the system supports at runtime, and they're used quite extensively throughout the stdlib so you benefit even if you're not writing SIMD routines yourself.

They tried to do something similar in Javascript but it added way too much complexity to the runtimes and ended up getting dropped in favor of WASM SIMD.

[twic]

It's possibly worth mentioning that Java is getting a vector API which explicitly abstracts over some of the details of SIMD, including width. You have a type Vector<T> which represents enough of some type T to fill a vector register (eg eight 32-bit numbers in a 256-bit register), operations on Vector<T> which produce another Vector<T>, and some way to break arrays up into Vectors of the right size for the platform. The API is a bit clunky, but you write code with it, the compiler performs a miracle, and efficient platform-specific vector code comes out.

https://docs.oracle.com/en/java/javase/23/docs/api/jdk.incub...

https://docs.oracle.com/en/java/javase/23/docs/api/jdk.incub...

[thfuran]

Though it's pretty much incubating forever, or until Valhalla, whichever comes first.