Using HDF5/zlib compression in NetCDF4, part 2: testing the compression settings

There has been a previous post, courtesy of Greg Garner, on why HDF5/zlib compression matters for NetCDF4. That post featured a plot that showed how much you could compress your data when increasing the compression level. But the fine print also acknowledged that this data was for a pretty idealized dataset. So how much should you compress your data in a real-world application? How can you test what your trade-off really is between compression and computing time?

Follow this 4-step process to find out!

I’ll be illustrating this post using my own experience with the Water Balance Model (WBM), a model developed at the University of New Hampshire and that has served for several high-profile papers over the years (including Nature and Science). This is the first time that this model, written in Perl, is being ported to another research group, with the goal of exploring its behavior when running large ensembles of inputs (which I am starting to do! Exciting, but a story for another post).

Step 1. Read the manual

There is a lot of different software for creating NetCDF data. Depending on the situation, you may have a say on which to use, or be already using the tool that comes with the software suite you are working with. Of course, in the latter case, you can always change the tools. But reasonable a first step before that is to test them. Ergo, look up the documentation for the software you are using, to see how you can control compression on them.

Here, WBM uses the PDL::NetCDF Perl library, which has useful functions for adding data to a NetCDF file after every time step the model runs. Contrary to Greg’s post that uses C and where there are two flags (“shuffle” and “deflate”) and a compression level parameter (“deflate_level”), for PDL::NetCDF there are only two parameters. The SHUFFLE flag is the equivalent in Perl of the “shuffle” flag in C. The DEFLATE Perl parameter ihas integer values from 0 to 9, with a value 0 being equivalent to the C-flag “deflate” being turned off, and any value from 1 to 9 being equivalent to the “deflate”C-flag being on, the value of DEFLATE being then equivalent to the value of the “deflate_level” parameter in Greg’s post. Therefore, the DEFLATE variable from the PDL::NetCDF library in Perl lumps together the parameters “deflate” and “deflate_level” used in C.

I then located the DEFLATE and SHUFFLE variables within the auxiliary functions of the WBM. In the function write_nc, the following two lines of codes are key:

 my $deflate = set_default($$options{DEFLATE},1); # NetCDF4 deflate (compression) parameter</pre>
my $shuffle = set_default($$options{SHUFFLE},0); # NetCDF4 shuffle parameter 

Step 2. Set up a test protocol

This builds on Greg’s idea of recording time and resulting file size for all compression level. Here we are interested in these quantities for full-scale model runs, and not just for the generation of a single NetCDF dataset.

In this case therefore, we want to contrast the default setting above with stronger compression settings, for ensemble runs of WBM on the Cube (the local HPC cluster). For a better comparison, let us place ourselves in the conditions in which ensemble runs will be made. Runs will use all 16 cores of a Cube node, therefore for each compression setting, this experiment runs 16 instances of the WBM on a single node. Each of the 16 instances runs on a single core. All WBM runs are identical so the only differences between run times and result file size come from compression settings.

Compression settings for (SHUFFLE,DEFLATE) are (0,1) by default, and we compare that with all settings from (1,1) to (1,9).

Step 3. Run experiment, get results

Here are the results from this experiment. Results consider 47 output fields for WBM runs with a daily time-step for 8 years (2009-2016), plus 5 years of warmup (this is pretty common for hydrological models). All this in a spatial mesh of 148,500 grid cells. A folder containing binaries for a single input variable, for this time span and spatial coverage, has a size of 3.1GB. Therefore, the expected size for 47 variables in binary format is 146Go. Let us compare with our results:

netcdf_expe_results

As one can see the presence of the shuffle flag or the value of the deflate parameter have little influence on the size of the results files. Compressed results are 3 to 4 time smaller than binaries, which highlights the interest of compressing, but also means we do not have the order(s) of magnitude differences reported by Greg’s blog post. This is mainly because the binary format used for WBM inputs is much more efficient than the uncompressed ASCII that Greg used in his experiment. For a deflate parameter of 9, there is an apparent problem within the PDL library, and no output (note that a single-core run with shuffle=0 and deflate=9 did not lead to a similar problem).

Step 4. Conclude on compression parameters

Here the epxerimental setup has shown that carefully selecting the output fields will save more space than fine-tuning NetCDF compression parameters. For instance, some of the 47 output fields above are fully redundant with others. Others are residual fields, and the only interest of looking them up is to verify that a major development within the WBM code did not mess up with the overall water balance.

More generally, the effects of compression are situation-specific and are not as great when there is no obvious regularity in the data (as is often the case with outputs from large models), or when the binary format used is already much better than ASCII. This said, NetCDF still occupies much less space than binaries, and is much easier to handle: WBM outputs are contained in one file per year (8 files total) with very useful metadata info…


 

 

One thought on “Using HDF5/zlib compression in NetCDF4, part 2: testing the compression settings

  1. Pingback: Establishing an Effective Data Backup Strategy For Your Workstation – Water Programming: A Collaborative Research Blog

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