Hi,

I had similarly bad experience with many groups and many attributes as well. Some things can be done (best do all of them):

1. Make sure you set |H5Pset_libver_bounds(,||H5F_LIBVER_LATEST,||H5F_LIBVER_LATEST|) when creating the file such that the newest library features are used

2. compress the group metadata, which may reduce disk reading times (some high-speed compressor such as LZ4 is recommended)

3. Use the split file driver to place meta-data into different physical files than the raw data, so meta-data are always compact on disk (might be less of an issue for SSDs)

4. Reorganize your attributes into datasets instead; when appending data it may be less inefficient to update the datasets, so during an incremental data update you can still write attributes, then do some postprocessing that builds a "cache" of the attributes into a dataset instead, and during reading you read that dataset instead of the attributes. This postprocessing to build the "attribute cache" would take some time of course, but if it needs to be done once only while reading happens frequently, it is worth the effort. Depends on your use case scenario of course.

Cheers,

           Werner


On 13.07.2017 18:33, Deepak 8 Kumar wrote:
Hello!

I have a HDF5 based application to read the hdf5 file which has almost 3200 groups and 56000 attributes. The application is using the standard hdf5 api and it took almost 11 minutes to read only the groups and attributes. I used StopWatch and observed that H5Aopen_name() is taking almost 97 percent of the total time. I am using HDF 1.10.1 Windows10 x64.

My question is that is this the expected behavior here with H5Aopen_name() or I am not reading it properly? What approach we should take in this kind of file with large number of attributes?
Any insight is greatly appreciated.

Thanks,
Deepak Kumar


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Dr. Werner Benger                Visualization Research
Center for Computation & Technology at Louisiana State University (CCT/LSU)
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