[REVIEW] Optimize groupby-agg in dask_cudf

[rjzamora]

Experimental groupby-aggregation optimizations. New algorithm leverages groupby(...).agg(..) in cudf, rather than looping through each column. New backend applies to operations like the following:

# List of aggregations
ddf.groupby("id").agg(["min", "max", "mean", "count"], split_out=1, split_every=8)

# Dictionary of aggregations
ddf.groupby("id").agg({"x": ["min", "max", "mean", "count"]}, split_out=1, split_every=8)

Note that the new backend can also be used for a pandas-backed Dask-DataFrame (e.g. dask_cudf.groupby_agg(ddf, ...)). However, the new algorithm does not seem to benefit performance in pandas.

cc @pentschev @kkraus14

TODO:

• Add tests
• Clean up, validate, harden
• Add "std" and "var" support
• Handle aggregation dict

[GPUtester]

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[codecov]

Codecov Report

Merging #6248 into branch-0.16 will increase coverage by 0.45%.
The diff coverage is 95.16%.

@@               Coverage Diff               @@
##           branch-0.16    #6248      +/-   ##
===============================================
+ Coverage        84.42%   84.87%   +0.45%     
===============================================
  Files               82       83       +1     
  Lines            13857    14383     +526     
===============================================
+ Hits             11699    12208     +509     
- Misses            2158     2175      +17     

Impacted Files	Coverage Δ
python/dask_cudf/dask_cudf/groupby.py	95.05% <95.05%> (ø)
python/dask_cudf/dask_cudf/__init__.py	82.35% <100.00%> (+1.10%)	⬆️
python/dask_cudf/dask_cudf/core.py	76.14% <100.00%> (+0.23%)	⬆️
python/cudf/cudf/io/feather.py	100.00% <0.00%> (ø)
python/cudf/cudf/comm/serialize.py	0.00% <0.00%> (ø)
python/custreamz/custreamz/_version.py	0.00% <0.00%> (ø)
python/dask_cudf/dask_cudf/_version.py	0.00% <0.00%> (ø)
python/dask_cudf/dask_cudf/io/tests/test_csv.py	100.00% <0.00%> (ø)
python/dask_cudf/dask_cudf/io/tests/test_orc.py	100.00% <0.00%> (ø)
python/dask_cudf/dask_cudf/io/tests/test_json.py	100.00% <0.00%> (ø)
... and 35 more

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[kkraus14]

rerun tests

[kkraus14]

rerun tests