![]() On my side, event with all CPU working to resize image and save results in cache: It will depend of your computer (mine is 24CPU / 60GB RAM but on my laptop 4CPU/16GB it’s slower :)) and how you’ve implemented things also Python is not the best language for this (a compiled language will provide better results in memory usage and speed) So giving support to folders of 60k seems impossible unless you filter search that and cache it after and still it would lag Once cache is generated, it took around ~1.2s to load 10000files thumbnails in a treeview ![]() Also load thumbnails asynchronously in treeview to keep ui responsiveīut the thing for performance is not only the use of multithreading, you have to generate a cache and use it.Use of threads allow to keep ui responsive while computer is doing intensive computation.Generating thumbnails (512, 256, 128 and 64px size thumbnail for each file) is a little bit longer (between ~50s and ~300s according to computer activity to generate 40000 thumbnails from 80GiB images – tested from a SSD, running with 24thread).Doing analysis of ~10000 files (~80GiB) took a couple of seconds (analysis read all files size, image dimension, hash calculation, …).QThread are not so difficult to use for parallelization once you’ve understood how to use it The biggest problem is that just the process of searching for the images takes too long.
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