You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Hi, thanks for this very powerful package. My analysis group and I are trying to incorporate it into one of our LHCb analyses, the results are very promising.
However, we are missing a multithreading functionality. We have to deal with many millions of events and this can take many hours or days in a single core. I managed to add myself a numba parallel range inside kernels.py/_evaluate_numba function, but would be great to have an option in the constructor or an additional method (or whatever) to do this automatically.
The text was updated successfully, but these errors were encountered:
Hi @apereiroc I'm glad to hear the package is working well for you! This is an excellent idea, and something I really hadn't thought much about so far. So far, the package overall has had very minimal speed optimization, which I think is very important to work on. Any implementations/recommendations you have are very welcome either as general ideas or specific code PRs!
Hi, thanks for this very powerful package. My analysis group and I are trying to incorporate it into one of our LHCb analyses, the results are very promising.
However, we are missing a multithreading functionality. We have to deal with many millions of events and this can take many hours or days in a single core. I managed to add myself a numba parallel range inside kernels.py/_evaluate_numba function, but would be great to have an option in the constructor or an additional method (or whatever) to do this automatically.
The text was updated successfully, but these errors were encountered: