I've converted some ome.tiff files to .zarr and have had issues with the reading time of the zarr files.
For this example I'm using an image of shape [ 4, 16484, 11620 ], and I have stored it with chunk size (1,1024, 1024) as well as unchunked.
All files are stored in a RAID0 nvme ssd and have the same compression.
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 reproduce
This is the code I'm using:
start = time.time()
z = zarr.open(path_zarr)[:]
print(f"Zarr read time (chunked): {time.time() - start}")
start = time.time()
z_nochunk = zarr.open(path_zarr_nochunk)[:]
print(f"Zarr read time (no chunks): {time.time() - start}")
start = time.time()
d = dask.array.from_zarr(path_zarr).compute()
print(f"Dask read time (chunked): {time.time() - start}")
start = time.time()
d_nochunk = dask.array.from_zarr(path_zarr_nochunk).compute()
print(f"Dask read time (no chunks): {time.time() - start}")
start = time.time()
t = tifffile.imread(path_tiff)
print(f"Tiff read time: {time.time() - start}")
Results:
# 1 thread:
Zarr read time (chunked): 2.8317720890045166
Zarr read time (no chunks): 0.7048866748809814
Dask read time (chunked): 3.4919939041137695
Dask read time (no chunks): 0.7005000114440918
Tiff read time: 1.094351053237915
# 10 threads:
Zarr read time (chunked): 2.8606531620025635
Zarr read time (no chunks): 0.32688140869140625
Dask read time (chunked): 2.7447142601013184
Dask read time (no chunks): 0.712876558303833
Tiff read time: 0.4734377861022949
# 50 threads:
Zarr read time (chunked): 2.8490779399871826
Zarr read time (no chunks): 0.2691495418548584
Dask read time (chunked): 2.9153594970703125
Dask read time (no chunks): 0.716036319732666
Tiff read time: 0.4784407615661621
Zarr version
2.18.2
Numcodecs version
0.13.0
Python Version
3.12.4
Operating System
Linux
Installation
pip install zarr
Description
I've converted some ome.tiff files to .zarr and have had issues with the reading time of the zarr files. For this example I'm using an image of shape [ 4, 16484, 11620 ], and I have stored it with chunk size (1,1024, 1024) as well as unchunked. All files are stored in a RAID0 nvme ssd and have the same compression.
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 reproduce
This is the code I'm using:
Results:
Additional output
No response