juanjgalvez

5 ms·

1. 1.▲
   by juanjgalvez 8y ago

   We'll update the documentation with the API in the next couple of days. Eventually there will also be a more comprehensive manual explaining every feature.
2. 2.▲
   by juanjgalvez 8y ago

   For CharmPy this is not currently supported, but Charm++ can interoperate with MPI libraries, so with luck it wouldn't require much effort to get it working. I will open an issue on github to track this task.
3. 3.▲
   by juanjgalvez 8y ago

   You don't actually have to specify hosts or addresses in your application code. When the application starts, the runtime will know how many processes there are and on which hosts. The key is to use a job launcher. For the par-map.py ex
4. 4.▲
   by juanjgalvez 8y ago

   For a single workstation and the task you describe, the pool.map() functionality of the multiprocessing module should be perfectly adequate. Not sure how scheduling overhead would compare between charmpy and multiprocessing, but for this ta
5. 5.▲
   by juanjgalvez 8y ago

   You must be referring to the example in the README. That is the only example in the source code or docs that uses `import *` as far as I'm aware. But yeah, I agree. It's fixed now.
6. 6.▲
   by juanjgalvez 8y ago

   Hi. Yes, applications can span multiple nodes (e.g. in clusters and supercomputers), and is one of the main use cases of charm++/charmpy. The fact that you don't see anything in the API or examples is that application code is basi
7. 7.▲
   by juanjgalvez 8y ago

   There are quite a few differences between them. Disclaimer: I work on CharmPy, and I'm not an expert on Dask, so my comments might be biased and not entirely accurate with respect to Dask. Obvious difference between the two is programm
8. 8.▲
   by juanjgalvez 8y ago

   You can install charmpy on Windows with pip if that works for you. Launching multiple processes is straightforward (you can check the documentation at charmpy.readthedocs.io). We don't offer an API yet in charmpy to explicitly do thing