> you can't use languages that don't share memory between threads ... Python, Go, Ruby
> trust me, I'm not confused
Sorry but you really are confused about this. You said that Python, Go and Ruby don't have shared memory between threads, but they simply do. All of these languages's concurrency and parallelism models are fundamentally shared-memory, and all even allow completely unsynchronised access to shared memory if you wanted to do that.
If you won't believe me on that, here are five peer-reviewed papers from reputable venues on Ruby alone that talk about the shared memory model it has. Either all of these experts and all the reviewers are mistaken, or you are.
B. Daloze, S. Marr, D. Bonetta, H. Mössenböck. Efficient and Thread-Safe Objects for Dynamically-Typed Languages. In Proceedings of the ACM International Conference on Object Oriented Programming Systems Languages and Applications (OOPSLA), 2016.
C. Ding, B. Gernhardt, P. Li, and M. Hertz. Safe Parallel Programming in an Interpreted Language. In Proceedings of the First Workshop on the High Performance Scripting Languages, 2015.
L. Lu, W. Ji, and M. L. Scott. Dynamic Enforcement of Determinism in a Parallel Scripting Language. In Proceedings of the 35th Conference on Programming Language Design and Implementation (PLDI), 2014.
R. Odaira, J. G. Castanos, and H. Tomari. Eliminating Global Interpreter Locks in Ruby through Hardware Transactional Memory. In Proceedings of the 19th Symposium on Principles and Practice of Parallel Programming (PPoPP), 2014.
W. Ji, L. Lu, and M. L. Scott. TARDIS: Task-level Access Race Detection by Intersecting Sets. In Proceedings of the 4th Workshop on Determinism and Correctness in Parallel Programming (WoDet), 2013.
> R. Odaira, J. G. Castanos, and H. Tomari. Eliminating Global Interpreter Locks in Ruby through Hardware Transactional Memory. In Proceedings of the 19th Symposium on Principles and Practice of Parallel Programming (PPoPP), 2014.
Well thanks for this, can't wait to sit down & read it.
Python, Go and Ruby do not have the ability to run multiple real OS threads that can run on separate cores and share process memory at the same time.
They might try to work around that, but they will never be able to run one common task to/from many sockets on all cores of one machine at the same time.
Yes, they do. Python does. I'm sorry but your wires are getting crossed somewhere. You also completely ignored the comment you where replying to and the sources he cites for you and repeated essentially the same thing you said before. This is quite rude.
Python has a GIL, so only one thread can interact with the Python interpreter at one time. You can have any number of threads running at one time if they release the GIL and don't touch the interpreter (i.e if they are doing IO or calling a C function).
They, however, are still real OS threads, running (waiting for a lock) on separate cores and sharing process memory. That's the definition of a thread. They are not fake threads. They share memory. They run on separate cores (perhaps).
This is not technically true, but often is practically so. No matter, it has no bearing at all on the concept of "async" as modeled or in contexts you've discussed.
I'm not sure why you're trying to salvage this or what your endgame is here but please reconsider.
> trust me, I'm not confused
Sorry but you really are confused about this. You said that Python, Go and Ruby don't have shared memory between threads, but they simply do. All of these languages's concurrency and parallelism models are fundamentally shared-memory, and all even allow completely unsynchronised access to shared memory if you wanted to do that.
If you won't believe me on that, here are five peer-reviewed papers from reputable venues on Ruby alone that talk about the shared memory model it has. Either all of these experts and all the reviewers are mistaken, or you are.
B. Daloze, S. Marr, D. Bonetta, H. Mössenböck. Efficient and Thread-Safe Objects for Dynamically-Typed Languages. In Proceedings of the ACM International Conference on Object Oriented Programming Systems Languages and Applications (OOPSLA), 2016.
C. Ding, B. Gernhardt, P. Li, and M. Hertz. Safe Parallel Programming in an Interpreted Language. In Proceedings of the First Workshop on the High Performance Scripting Languages, 2015.
L. Lu, W. Ji, and M. L. Scott. Dynamic Enforcement of Determinism in a Parallel Scripting Language. In Proceedings of the 35th Conference on Programming Language Design and Implementation (PLDI), 2014.
R. Odaira, J. G. Castanos, and H. Tomari. Eliminating Global Interpreter Locks in Ruby through Hardware Transactional Memory. In Proceedings of the 19th Symposium on Principles and Practice of Parallel Programming (PPoPP), 2014.
W. Ji, L. Lu, and M. L. Scott. TARDIS: Task-level Access Race Detection by Intersecting Sets. In Proceedings of the 4th Workshop on Determinism and Correctness in Parallel Programming (WoDet), 2013.