That's if you're CPU-bound. I don't use Python, but I made an image acquisition program in C++ which could be a relevant example. We wanted to save the images to disk in real-time (30-60FPS). Doing this in the acquisition loop would make the software unusable (the goal is video-rate confocal microscope imaging); it's far too long, and much of it is just due to disk writes being slow, not to the compression time. Using a thread pool was the solution, not because of an actual increase in speed, but because from the loop's POV the write went from blocking to non-blocking so the CPU stopped wasting time waiting for the disk.
We also wanted shared memory since there can be a lot of image data which is shared between the image compression & saving, display, and possibly statistics or filtering modules.
Dunno about Python, but Perl has a library to do all disk writes in a separate (p)thread, so your main control thread never blocks on IO. (This is in addition to the usual event-loop tricks; I know Python can do nonblocking IO that way.)
That's if you're CPU-bound. I don't use Python, but I made an image acquisition program in C++ which could be a relevant example. We wanted to save the images to disk in real-time (30-60FPS). Doing this in the acquisition loop would make the software unusable (the goal is video-rate confocal microscope imaging); it's far too long, and much of it is just due to disk writes being slow, not to the compression time. Using a thread pool was the solution, not because of an actual increase in speed, but because from the loop's POV the write went from blocking to non-blocking so the CPU stopped wasting time waiting for the disk.
We also wanted shared memory since there can be a lot of image data which is shared between the image compression & saving, display, and possibly statistics or filtering modules.