Simply copying a chunk of data between two libraries through Python is already painful. There are so-called "buffer API" in Python, but it's very rare that Python users can actually take advantage of this feature. If anything in Python as much as looks at the data, that's not going to work etc.
Similarly, concurrency. A lot of native libraries for Python are written with the expectation that nothing in Python really runs concurrently. And then you are presented with two bad options: try running in different threads (so that you don't have to copy data), but things will probably break because of races, or run in different processes, and spend most of the time copying data between them. Your interface to stuff like MPI is, again, only at the native level, or you will copy so much that the benefits of distributed computation might not outweigh the downsides of copying.
I think we will get there in the end but it will be slow.
When I was doing performances stuff: Intel was our main platform and memory consistency was stronger there. We would try to write platform-agnostic multi threading code (in our case, typically spin-locks) but without testing properly on the other platforms, we would make mistakes and end up with race conditions, accessing unsynchronized data, etc.
I think Python will be the same deal. With Python being GIL'd through most of its life cycle bits and pieces won't work properly, multi-threaded until we fix them.
there’s already a PEP to address the GIL. python is going through its JVM optimization phase. it’s too popular and ubiquitous that improving the GIL and things like it are inevitable
Simply copying a chunk of data between two libraries through Python is already painful. There are so-called "buffer API" in Python, but it's very rare that Python users can actually take advantage of this feature. If anything in Python as much as looks at the data, that's not going to work etc.
Similarly, concurrency. A lot of native libraries for Python are written with the expectation that nothing in Python really runs concurrently. And then you are presented with two bad options: try running in different threads (so that you don't have to copy data), but things will probably break because of races, or run in different processes, and spend most of the time copying data between them. Your interface to stuff like MPI is, again, only at the native level, or you will copy so much that the benefits of distributed computation might not outweigh the downsides of copying.