Do the JVM-based pythons solve any threading issues? Plain parallel java
seems indispensable.
Bill
--
Phobrain.com
On 2025-07-03 05:05, Nathan via NumPy-Discussion wrote:
> If a NumPy array is shared between two threads, NumPy doesn't do anything to
> synchronize array access. This is true
If a NumPy array is shared between two threads, NumPy doesn’t do anything
to synchronize array access. This is true in all Python versions and build
configurations - since NumPy releases the GIL during most array operations
whether or not you’re using free-threaded Python doesn’t change much except
On a related note, does numpy's gufunc mechanism provide any thread safety,
or is the responsibility on the extension writer to do that? For simple
numpy array inputs, I would think that I don't have to worry about
free-threaded python messing things up (unless I have a global state), I'm
wondering
Warren,
The examples in ufunclab helped clear up a few things and I was able to
experiment and get a working gufunc! Thank you for your help!
Ben Root
On Fri, Jun 27, 2025 at 8:54 PM Benjamin Root wrote:
> Warren,
>
> I'm fine with implementing it in C. I just didn't think gufuncs were for
> m
Warren,
I'm fine with implementing it in C. I just didn't think gufuncs were for
me. I couldn't tell from the description if it would be for my usecase
since I wasn't looping over subarrays, and I didn't see any good examples.
Maybe the documentation could be clearer. I'll have a look at your exam
On Fri, Jun 27, 2025 at 5:29 PM Benjamin Root via NumPy-Discussion
wrote:
>
> I'm looking at a situation where I like to wrap a C++ function that takes two
> doubles as inputs, and returns an error code, a position vector, and a
> velocity vector so that I essentially would have a function signa
