id,node_id,number,title,user,state,locked,assignee,milestone,comments,created_at,updated_at,closed_at,author_association,active_lock_reason,draft,pull_request,body,reactions,performed_via_github_app,state_reason,repo,type
435311415,MDU6SXNzdWU0MzUzMTE0MTU=,2908,More efficient rolling with large dask arrays,9040990,closed,0,,,3,2019-04-19T21:35:59Z,2019-10-04T17:04:37Z,2019-10-04T17:04:37Z,NONE,,,,"#### Code Sample

```python
import xarray as xr
import dask.array as da

dsize=[62,12,100,192,288]
array1=da.random.random(dsize,chunks=(dsize[0],dsize[1],1,dsize[3],int(dsize[4]/2)))
array2=xr.DataArray(array1)
rollingmean=array2.rolling(dim_1=3,center=True).mean()  # <-- this kills all workers

```
#### Problem description

I'm working on NCAR's cheyenne with a 36GB netcdf using dask_jobqueue.PBSCluster, and trying to calculate the running-mean along one dimension. Despite having plenty of memory reserved (400GB), I can watch DataArray.rolling blow up the bytes stored in the dashboard until the job hangs and all the workers are killed.  

The above snippet reproduces the issue with the same array size and chunksize as what I'm working with. This worker-killing behavior does not occur for arrays that are 100x smaller.  I've found a speedy way to calculate what I need without using rolling, but I thought I should bring this to your attention regardless. 

In case it's relevant, here's how I'm setting up the dask cluster on cheyenne:
```python
from dask.distributed import Client
from dask_jobqueue import PBSCluster  #version 0.4.1

cluster=PBSCluster(cores=36, processes=9, memory='109GB', project=myproj, resource_spec='select=1:ncpus=36:mem=109G', queue='regular', walltime='02:00:00')
numnodes=4
client = Client(cluster)
cluster.scale(numnodes*9)

```

#### Output of ``xr.show_versions()``

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1 (default, Dec 14 2018, 19:28:38) 
[GCC 7.3.0]
python-bits: 64
OS: Linux
OS-release: 3.12.62-60.64.8-default
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
libhdf5: 1.10.4
libnetcdf: 4.6.2

xarray: 0.12.1
pandas: 0.24.1
numpy: 1.15.4
scipy: 1.2.1
netCDF4: 1.4.2
pydap: None
h5netcdf: None
h5py: None
Nio: None
zarr: 2.3.1
cftime: 1.0.3.4
nc_time_axis: None
PseudonetCDF: None
rasterio: None
cfgrib: None
iris: None
bottleneck: None
dask: 1.1.5
distributed: 1.26.1
matplotlib: 3.0.2
cartopy: 0.17.0
seaborn: 0.9.0
setuptools: 40.6.3
pip: 18.1
conda: 4.6.13
pytest: None
IPython: 7.3.0
sphinx: None

</details>
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