Uh oh!
There was an error while loading.Please reload this page.
- Notifications
You must be signed in to change notification settings - Fork366
Inconsistent reading performance with multiple cpu threads#2184
Uh oh!
There was an error while loading.Please reload this page.
Uh oh!
There was an error while loading.Please reload this page.
-
Zarr version2.18.2 Numcodecs version0.13.0 Python Version3.12.4 Operating SystemLinux Installationpip install zarr DescriptionI've converted some ome.tiff files to .zarr and have had issues with the reading time of the zarr files. I've compared the reading times using 1, 10, and 50 logical threads (with taskset) and noticed the performance can vary greatly depending on the settings. If unchunked, additional threads significantly improves reading time, just like when reading ome.tiffs. In fact, reading unchunked files is significantly faster than ome.tiffs. However, chunked files do not seem to benefit from additional threads, even resulting in slower times. Reading with the dask library also seems to have inconsistent performance, although in a different way. Additionally, considering the hardware (RAID0 nvme ssd, dual 56 core CPU Intel Xeon Platinum 8280) , I'd assume that reading chunked files with multiple workers would be much faster, as the processing is done in parallel. But here we see that not only does it not seem to benefit from more workers, but it's an order of magnitude slower than reading an unchunked file. Is there something I could be missing here? Steps to reproduceThis is the code I'm using: Results: Additional outputNo response |
BetaWas this translation helpful?Give feedback.
All reactions
Replies: 1 comment 1 reply
-
Is there any update to this issue from the developers? I'm encountering the same issue in my project. |
BetaWas this translation helpful?Give feedback.
All reactions
-
As it, this post doesn't have enough information in it to be actionable.
|
BetaWas this translation helpful?Give feedback.
All reactions
This discussion was converted from issue #2084 on September 13, 2024 23:37.