Enabling GPU-native analytics with Xarray and kvikIO

TLDR#

We demonstrate registering an Xarray backend that reads data from a Zarr store directly to GPU memory as CuPy arrays using the new kvikIO library and GPU Direct Storage technology.

Background#

What is GPU Direct Storage#

Quoting this nVIDIA blogpost

What is kvikIO#

For Xarray, the key bit is that kvikIO exposes a zarr store kvikio.zarr.GDSStore that does all the hard work for us. Since Xarray knows how to read Zarr stores, we can adapt it to create a new storage backend that uses kvikio. And thanks to recent work funded by the Chan Zuckerberg Initiative, creating and registering a new backend is quite easy!

Integrating with Xarray#

Getting all this to work nicely requires using three in-progress pull requests that

1. Teach Zarr to handle alternative array classes
2. Rewrite a small bit of Xarray to not cast all data to a numpy array after read from disk
3. Make a backend that connects Xarray to kvikIO

Writing the backend for Xarray was relatively easy with most of the code copied over or inherited from the existing Zarr backend. We did have to ensure that dimension coordinates could be read in directly to host memory without raising an error (by default kvikIO loads all data to device memory). This is required because Xarrays creates pandas.Index objects for such variables. In the future, we could consider using cudf.Index instead to allow a fully GPU-backed Xarray object.

Usage#

Assuming you have all the pieces together (see Appendix I and Appendix II for step-by-step instructions), then using all this cool technology only requires adding engine="kvikio" to your open_dataset line (!)

1import xarray as xr
2
3ds = xr.open_dataset("file.zarr", engine="kvikio", consolidated=False)
4
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Notice that importing cupy_xarray was not needed. cupy_xarray uses entrypoints to register the kvikIO backend with Xarray.

With this ds.load() will load directly to GPU memory and ds will now contain CuPy arrays. At present there are a few limitations:

1. Zarr stores cannot be read with consolidated metadata, and
2. compression is unsupported by the kvikIO backend.

Quick demo#

First create an example uncompressed dataset to read from

import xarray as xr

store = "./air-temperature.zarr"

airt = xr.tutorial.open_dataset("air_temperature", engine="netcdf4")

for var in airt.variables:
    airt[var].encoding["compressor"] = None
airt.to_zarr(store, mode="w", consolidated=True)
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Now read

# consolidated must be False
ds = xr.open_dataset(store, engine="kvikio", consolidated=False)
ds.air
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<xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53)>
[3869000 values with dtype=float32]
Coordinates:
  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0
  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0
  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00
Attributes:
    GRIB_id:       11
    GRIB_name:     TMP
    actual_range:  [185.16000366210938, 322.1000061035156]
    dataset:       NMC Reanalysis
    level_desc:    Surface
    long_name:     4xDaily Air temperature at sigma level 995
    parent_stat:   Other
    precision:     2
    statistic:     Individual Obs
    units:         degK
    var_desc:      Air temperature
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Note that we get Xarray's lazy backend arrays by default, and that dimension coordinate variables lat, lon, time were read. At this point this looks identical to what we get with a standard xr.open_dataset(store, engine="zarr") command.

Now load a small subset

1type(ds["air"].isel(time=0, lat=10).load().data)
2
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cupy._core.core.ndarray
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Success!

Xarray integrates decently well with CuPy arrays so you should be able to test out analysis pipelines pretty easily.

Cool demo#

We don't have a cool demo yet but are looking to develop one very soon!

Reach out if you have ideas. We would love to hear from you.

Summary#

We demonstrate integrating the kvikIO library using Xarray's new backend entrypoints. With everything set up, simply adding engine="kvikio" enables direct-to-GPU reads from disk or over the network.

Acknowledgments#

My time on this project was funded by NASA-OSTFL 80NSSC22K0345 "Enhancing analysis of NASA data with the open-source Python Xarray Library"

Appendix I : Step-by-step install instructions#

Wei Ji Leong helpfully provided steps to get started on your machine:

# May need to install nvidia-gds first
# https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#ubuntu-installation-common
sudo apt install nvidia-gds

git clone https://github.com/dcherian/cupy-xarray.git
cd cupy-xarray

mamba create --name cupy-xarray python=3.9 cupy=11.0 rapidsai-nightly::kvikio=22.10 jupyterlab=3.4.5 pooch=1.6.0 netcdf4=1.6.0 watermark=2.3.1
mamba activate cupy-xarray
python -m ipykernel install --user --name cupy-xarray

# https://github.com/pydata/xarray/pull/6874
pip install git+https://github.com/dcherian/xarray.git@kvikio
# https://github.com/zarr-developers/zarr-python/pull/934
pip install git+https://github.com/madsbk/zarr-python.git@cupy_support
# https://github.com/xarray-contrib/cupy-xarray/pull/10
git switch kvikio-entrypoint
pip install --editable=.

# Start jupyter lab
jupyter lab --no-browser
# Then open the docs/kvikio.ipynb notebook
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Appendix II : making sure GDS is working#

Scott Henderson pointed out that running python kvikio/python/benchmarks/single-node-io.py prints nice diagnostic information that lets you check whether GDS is set up. Note that on our system, we have "compatibility mode" enabled. So we don't see the benefits now but this was enough to wire everything up.

----------------------------------
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
   WARNING - KvikIO compat mode
      libcufile.so not used
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
GPU               | Quadro GP100 (dev #0)
GPU Memory Total  | 16.00 GiB
BAR1 Memory Total | 256.00 MiB
GDS driver        | N/A (Compatibility Mode)
GDS config.json   | /etc/cufile.json
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