After upgrading to CP3.1.8 and Win10 (from CP2.2.0 and Win7 on a different machine) I’ve noticed that analysis time doesn’t scale linearly with the number of images sent to the pipeline.
On a very simple pipeline (ID primary objects in 2 separate channels, output count to CSV), a single 384 plate (2 channels, 4 fields per well) takes about 40 minutes on my desktop and 2 plates take 80 minutes. But 6 plates take over 7 hours and adding more plates can bump the rate to 2hrs/plate according to the timer in the CP window lower right corner.
These times are by loading the images using the drag/drop interface and the images/metadata/Names&Types modules.
If I use the legacy LoadData module instead, the read times improve slightly: 32 min per plate for up to 2 plates, but then the per plate time increases to over an hour per plate when listing 6 plates worth of image sets in the CSV input file.
When I was using CP2 on my older machine, I regularly analyzed 36 plates at a time with no drop in per plate throughput.
My workstation is a 6core i7 CPU (12 threads)/16GB RAM, and CellProfiler is set to use 8 workers with 1024MB RAM for Java, which I think are the defaults from when I installed CP on this machine.
Is this degradation expected? Or are my CP3 settings wrong? Or is there a new way I’m supposed to be running multiple plates?